Inference

15 June 2026

DeepSeek-V4-Pro: specs, benchmarks, and how to run it on Lyceum

DeepSeek-V4-Pro delivers frontier-level reasoning and a massive 1M-token context window. Learn how to deploy it through Lyceum's OpenAI-compatible API with simple per-token pricing.

7 October 2026

Cutting Inference Spend by Routing Requests by Difficulty

Some requests may work well on a smaller model. Measure that share, include verification and escalation costs, and test quality before routing production traffic.

31 July 2026

Kimi K3 API: Where to Run It, and What a 1M-Token Context Costs

Kimi K3 is Moonshot AI's open-weights model with a 1M-token context window. On Lyceum it runs as moonshotai/kimi-k3 at $3.00 input, $0.75 cached input and $15.00 output per million tokens, EU-hosted, with prompts and outputs not stored or used for training. Kimi K2.6 was retired on 5 October 2026, and its model id now routes to Kimi K3.

25 April 2026

EU AI Act Infrastructure Requirements: Deadlines and Duties After the AI Omnibus

European AI teams face a critical regulatory shift. While the initial bans on prohibited practices took effect on 2 February 2025, 2 August 2026 is the date the Regulation applies in general and the date the Commission gains its power to fine general-purpose AI model providers under Article 101. The AI Omnibus, in force since 27 July 2026, then moved the Chapter III obligations for Annex III high-risk systems to 2 December 2027, and high-risk systems captured by Article 6(1), AI systems that are, or are safety components of, products covered by the EU product legislation listed in Annex I, to 2 August 2028. For teams building in sectors like healthcare, critical infrastructure, or employment, the Act requires evidence about the AI system and its operation. The necessary controls depend on the system and the provider's or deployer's role, rather than on a particular cloud architecture. Initial compliance work for a single high-risk system is a material cost line, and ongoing monitoring adds operational overhead on top of it. Moving beyond the 'move fast and break things' era, engineering teams must now treat compliance as a core component of their technical stack.

8 September 2026

GLM-5.2 Instant: Latency-Optimised GLM, EU-Hosted

GLM-5.2 Instant offers the same 1M-token context window and identical per-token pricing as the base GLM-5.2 model, but is positioned for lower latency. It runs on EU-sovereign serverless inference in eu-north1, so teams can evaluate throughput on their own traffic.

11 September 2026

EU Hosted LLM API: Lyceum Model Roster, Prices and Changes

Which model strings answer on this OpenAI-compatible endpoint, which offering records publish a hosting location and complete price data, and which older strings are absent from the live roster. Facts checked on 10 September 2026, with a roster request you can rerun yourself.

1 September 2026

GLM-5.3: specs, benchmarks, and how to run it on Lyceum

GLM-5.3 is ZAI's latest MoE model, offering a 1M-token context window and emergent cyber capabilities for agentic workflows. It is available on Serverless Inference via a drop-in OpenAI-compatible API, billed purely per token.

5 October 2026

Multi-Step Agent Workloads: Latency and Cost Budgets

An agent's latency is the sum of its sequential steps and its cost the sum of its calls, and neither is visible from a single request. This guide sets a task-level latency and cost budget, allocates it across step types, and adds a termination rule before the agent is built.

5 October 2026

Testing Open Coding Models on Multi-File Agentic Edits

Test coding models on representative changes from your own repository. Use task-specific acceptance tests, regression checks and recorded costs for every attempt, including failures.

6 October 2026

Piloting an Open-Model Coding Assistant Without Infrastructure

A credible pilot of an open-model coding assistant needs an endpoint, not a GPU: point Aider, Cline, Continue or Roo Code at a hosted OpenAI-compatible endpoint, agree criteria and a control first, and let per-token metering produce the evidence.

6 October 2026

EU-Hosted LLM API Providers Compared

Selected inference providers compared on models, processing region, price, compatibility and retention. Prices were checked on 1 October 2026.

6 October 2026

Is Your AWS EU Region Actually GDPR-Safe? The CLOUD Act Problem

An EU region helps establish processing location. Legal disclosure risk also depends on the entities and access involved. Assess both without assuming that a US parent automatically creates a GDPR transfer.

23 February 2026

KV Cache Memory Calculation for LLMs: A Technical Guide

Large Language Model (LLM) weights are only half the story. As sequence lengths grow and batch sizes increase, the Key-Value (KV) cache often becomes the primary consumer of GPU VRAM, leading to the dreaded Out-of-Memory (OOM) errors that plague production environments. For ML engineers, understanding the precise memory requirements of the KV cache is not just a theoretical exercise; it is a prerequisite for efficient scaling. This article provides a deep dive into the mechanics of KV caching, the mathematical foundations for memory estimation, and how modern architectures like Llama 3 or Mistral utilize advanced attention mechanisms to mitigate memory bottlenecks.

10 June 2026

LLM Tokens Per Second Benchmark: Measured TTFT on Lyceum

This page publishes Lyceum's own measured throughput and first-token latency per model, with the test conditions beside every number, so a workload can be sized against a real measurement rather than a borrowed one.

30 September 2026

Roo Code OpenAI Compatible Provider: Lyceum Model Setup

This guide runs each Roo Code mode on an open-weight model from Lyceum Serverless Inference, chosen for that mode's job: one base URL and one key, a profile per mode, limits set by hand, and a tool-calling check before real work.

2 October 2026

What Coding Agents Actually Cost Per Token in Production

Budget per completed task, then cut it with prompt caching, scoped context and iteration caps, all without changing the model

2 October 2026

Setting Spend Limits on LLM Token Usage Across a Dev Team

If LLM spend across your team is only visible on the invoice, this guide puts attribution and an enforceable limit at the layer where they actually work.

30 September 2026

Self-Hosting vs a Managed API: The Break-Even Volume

The usual comparison divides a GPU hourly rate by theoretical throughput and calls self-hosting cheaper. Priced with utilisation and the full serving stack included, the crossover moves, and a managed dedicated endpoint sits between the two extremes.

30 September 2026

Where to Run DeepSeek in Europe: V4 Flash vs Pro

If DeepSeek is blocked at your compliance review, this page answers per model where V4 Flash and V4 Pro run in Europe and what each costs.

1 October 2026

Together AI Alternatives for EU Data Residency

A roster of per-token providers with European processing, with the region question answered per provider and, where it matters, per model.

7 September 2026

Recommending an EU Inference Provider: A Deal-Risk Checklist

If you are about to recommend an inference provider to a client, this checklist covers the four risks that land on you rather than on them - and applies itself to us.

1 October 2026

How to Use Lyceum Models in Zed

This guide shows how to add an open-weight Lyceum model to Zed's agent panel with a single settings.json block. Learn how to correctly declare provider capabilities, specify context limits, and secure your API key.

30 September 2026

Inference Provider Reliability: Verify Uptime Without an SLA

An SLA is a financial apology, not an engineering guarantee. Evaluate an inference provider's reliability by verifying their open-stack architecture, scrutinizing their public status page, and measuring latency metrics like TTFT and ITL yourself.

28 September 2026

RAG Pipeline GPU Sizing: Cost Per Query, End to End

Estimating the cost of a RAG pipeline requires decoupling the compute economics of embedding, vector search, and LLM generation. Transitioning from retail API markups to dedicated GPU infrastructure fundamentally lowers the cost per query when scaled for batch concurrency.

14 August 2026

How to Use Lyceum API within Claude Code

This guide shows how to run Claude Code on an open-weight model through Serverless Inference, with three commands and no proxy, and how to check which model is answering.

28 August 2026

Best Open-Model APIs for Agentic Coding (2026)

Agentic coding fundamentally changes model economics, shifting the focus from single-shot completions to multi-step tool calls where output prices compound. This guide breaks down the 18-fold output price spread across open models for autonomous agents.

8 September 2026

Using Lyceum Models in opencode: Custom Provider Setup

opencode supports custom OpenAI-compatible endpoints via its provider configuration. Pointing it at a European serverless inference endpoint lets developers use frontier open-weight models directly in the terminal, cutting token costs while keeping codebase data inside the EU.

21 September 2026

How to Use Lyceum Models in Cline

This guide shows how to run Cline's autonomous coding loop on an open-weight model from Lyceum Serverless Inference, with the limits set right.

25 September 2026

Tamper-Evident AI Audit Trails: Hash Chains and Retention

A tamper-evident audit trail ensures any alteration to inference records is mathematically detectable. By building hash-chained logs over metadata and HMAC-SHA-256 digests in the application layer, you can prove system integrity without violating data retention limits.

25 September 2026

Article 50 AI Act: Marking Outputs with C2PA & SynthID

Article 50 of the EU AI Act imposes strict transparency obligations on generative media, splitting machine-readable marking from visible disclosure. Here is how C2PA, SynthID, and embedded metadata satisfy the rule, and why compliance is a pipeline decision you must own.

25 September 2026

Provider or Deployer? AI Act Roles for Inference Engines

For teams building on a third-party inference engine, EU AI Act compliance starts with a counterintuitive fact: you are likely both a deployer of the upstream models and the provider of the AI system you ship.

24 September 2026

Is Your AI System High-Risk? A Decision Tree Through Annex III

Classifying your AI system under the EU AI Act is a rigid decision tree, not a judgement call. This guide maps out the Annex I and Annex III routes, breaking down the 4 conditions for derogation to give engineering teams a definitive exit state and compliance timeline.

22 September 2026

Does Fine-Tuning Make You a GPAI Provider?

Engineering teams worry fine-tuning an open-source model might classify them as a GPAI provider under the EU AI Act. By calculating compute against the Commission’s one-third threshold, you can prove your workload remains safely outside the scope.

23 September 2026

EU AI Act for Developers: A Practical Compliance Checklist (2026)

The EU AI Act assigns strict technical duties based on your role, but reading the legislation isn't practical. This routing hub indexes exactly which compliance obligations apply to your engineering team and links to the specific technical guides for implementation.

21 September 2026

How to Use Lyceum Models in GitHub Copilot

GitHub Copilot now supports custom endpoints, allowing development teams to use open-weight models via bring-your-own-key. This guide explains how to configure VS Code to connect to Lyceum Serverless Inference, bypass model name restrictions, and avoid empty-response errors.

11 September 2026

DeepSeek V4 Pro vs Claude Opus 4.6: Agent Costs Compared

If you are weighing DeepSeek-V4-Pro against Claude Opus 4.6, this page gives the verified per-token comparison on your own token ratio. We show how agent workflows accumulate context and why you must evaluate cost at the finished task level before switching.

10 September 2026

MiniMax M3 vs Claude Sonnet 5: The Mid-Tier Cost Gap

If you are weighing MiniMax-M3 against Claude Sonnet 5, this page gives the verified per-token comparison on your own token ratio and what you would need to test before switching.

9 September 2026

GLM-5.2 vs Claude Opus 5: The Real Cost Gap

For enterprise teams comparing GLM-5.2 against Claude Opus 5, the true cost difference depends entirely on your token ratio. This guide breaks down the per-token math, where each model processes your data, and what to test before migrating your workload.

28 August 2026

GLM-5.2 vs Kimi-K2.6 vs Qwen3: Coding APIs Compared

Comparing GLM-5.2, Kimi-K2.6, and Qwen3-Coder-30B-A3B reveals a clear divide: two are general-purpose flagships for complex reasoning, and one is a highly distilled code specialist. We break down the architectures, use cases, and the twenty-fold price gap between them.

14 August 2026

Kimi K3 vs Claude Fable 5: The Top-Tier Comparison

Kimi K3 matches Claude Fable 5's top-tier reasoning with 2.8 trillion parameters and a 1-million-token context window, all at a lower list price. European AI teams can run Kimi K3 on Lyceum's eu-north1 infrastructure for full data sovereignty

10 September 2026

Kimi-K3 vs DeepSeek-V4-Pro for Reasoning Work

Comparing Kimi-K3 and DeepSeek-V4-Pro solely on per-token price is misleading for reasoning work. Because models emit massive volumes of intermediate thought tokens, the true cost metric is the total billed volume per finished, correct answer.

21 September 2026

Annex IV Technical Documentation: The ML Team Checklist

Annex IV of the EU AI Act turns technical documentation into a strict legal requirement for high-risk AI systems. This guide translates the 9 mandatory legal points into a concrete checklist for ML engineering teams.

21 September 2026

Do You Need a DPIA for LLM Inference? A Deployer's Guide

Before shipping an LLM feature, you need to know if sending prompts to an API triggers a DPIA. This guide clarifies that the DPIA is a GDPR instrument, not an AI Act one, and maps exactly how to extract the 4 mandatory compliance inputs from your inference provider.

28 August 2026

Best Open Vision-Language Model APIs (2026)

For enterprise AI teams, evaluating open vision-language models comes down to balancing reasoning depth, inference cost, and data residency. Here is a direct comparison of the top EU-hosted multimodal APIs, Qwen2.5-VL and MiniCPM-V 4.5, and how to test them on your payloads.

11 September 2026

Self-Host vs API: The Token Break-Even for Open Models

If you are applying one self-hosting rule of thumb to every model you run, this guide shows how the crossover moves with model size and where it reverses entirely.

14 September 2026

AI Video Agents: Real-Time Generative GPU Infrastructure

This guide helps teams test latency, concurrency and cost before building an interactive video product. Measure your exact model and hardware, including the scheduling and sharing options your latency budget permits.

17 September 2026

Open Inference Stack: vLLM, Dynamo vs Proprietary Engines

If you are weighing an open serving stack against a proprietary engine, this guide separates the speed question from the portability question. We examine what vLLM and NVIDIA Dynamo buy you, keeping open source software, self-hosted versus managed deployments, and exposed controls as separate axes to evaluate.

9 September 2026

Running Wan 2.2 on Cloud GPU: VRAM Needs & Cost per Clip

Lyceum does not provide a serverless API for Wan 2.2. Instead, deploying open-weight video models requires On-demand GPU VMs, where cost per clip is driven by VRAM scaling, hardware matching, and utilization.

12 August 2026

Batch vs Real-Time Inference Pricing: When the Discount Wins

Major AI providers cut inference costs by 50 percent when teams route requests through asynchronous batch queues instead of real-time endpoints. Slashing spend requires isolating workloads that tolerate 24-hour turnaround times from those requiring interactive responses.

17 September 2026

Is Your Inference Provider Quantizing the Model: How to Tell

If a model behaves differently across providers, compare task quality under controlled settings. These tests cannot prove quantization; request documented serving details to understand the configuration.

31 August 2026

Autoscaling GPU Inference: KServe vs Ray Serve vs llm-d

KServe, Ray Serve, and llm-d offer different approaches to scaling GPU inference on Kubernetes. While KServe standardizes general model serving and Ray Serve enables Python-native pipelines, llm-d adds LLM-specific optimizations like disaggregated prefill and decode.

18 September 2026

Empty Response on a Reasoning Model: Why and How to Fix It

A blank reply can result from an output limit, a client integration problem or a response that needs further handling. Inspect the complete response before changing settings. Reasoning text alone is not a final answer.

15 September 2026

How to Use Lyceum Models in Cursor

Connect Cursor to Lyceum through its OpenAI-compatible endpoint, then test the model in Ask/chat. Agent support depends on the provider, model and client version. This guide separates the documented setup from features that still need validation.

18 September 2026

DeepSeek-V4.1-Flash: specs, benchmarks, and how to run it

DeepSeek-V4.1-Flash is live on Lyceum Serverless Inference: this page covers what changed, what it costs per token ($0.50 input, $0.13 cached, $1.50 output per 1M) and the exact request to send. Architecture and benchmark figures are DeepSeek's own, vendor-reported.

16 September 2026

Sovereign-Washing: How to Vet a "Sovereign" EU Cloud Claim

Cloud providers routinely claim EU sovereignty without removing non-EU legal or operational dependencies. Here is an eight-question framework to cut through sovereignty washing, followed by an honest self-assessment of where we pass and where we fall short.

16 September 2026

Self-Hosted TTS on GPU vs API: The Voice-Synthesis Cost Cliff

Moving text-to-speech off an API and onto a GPU replaces a linear per-character bill with a flat hourly rate, creating a clear cost crossover. This guide provides the exact break-even arithmetic to determine when self-hosting voice models becomes cheaper than paying a vendor.

18 September 2026

Time to First Token: What Actually Determines LLM API Latency

Time to first token dictates how fast your AI product feels to users. This technical breakdown explores the infrastructure layers that drive LLM latency, from queueing and continuous batching to prompt prefilling and Server-Sent Events.

18 September 2026

How AI Consultancies Choose LLM APIs for Client Projects

For AI consultancies, selecting an LLM API is about managing deal risk and reselling margins. This guide breaks down how to protect client data, avoid vendor lock-in with OpenAI SDK compatibility, and deploy EU-sovereign models to pass strict enterprise InfoSec audits.

18 September 2026

Switching the OpenAI SDK to an Open-Model Endpoint

Changing the base URL in the OpenAI SDK takes a minute, but a true migration requires checking five critical behavioural differences underneath the compatible interface. This guide covers how to repoint the SDK and verify structured output, tool calls, and ignored parameters.

8 September 2026

Qwen3.5-9B: Compact Qwen With the Narrowest Price Spread

Qwen3.5-9B is a compact open-weight model with a 256K context window and the narrowest input-to-output price spread in the catalogue. Costing $0.15 per million input tokens and $0.20 per million output tokens, it significantly reduces the total bill for output-heavy workloads.

25 August 2026

Running GLM 5.1, 5.2 and 5.2 Instant in Europe: Self-Hosting and Serverless Options

Z.ai's GLM-5 series introduces 1M-token contexts and powerful agentic capabilities via a 744B MoE architecture. For European teams, running these models locally requires massive GPU clusters, making a managed serverless endpoint a highly practical alternative.

12 August 2026

Where to Run Kimi Models in Europe: K2.6, K2.7 Code and K3

Moonshot AI's Kimi models deliver frontier capabilities for agentic coding. K2.6 and K2.7 Code offer 1T-parameter scale with 256K context, while K3 pushes to 2.8T parameters and a 1M-token window. European teams can run them via EU-hosted APIs to maintain data residency.

27 June 2026

GLM-5.2: specs, benchmarks, and how to run it on Lyceum

7 June 2026

Cost Per Million Tokens: The 2026 Provider Comparison Guide

Inference now consumes up to 80% of enterprise AI compute budgets. Discover the true cost per million tokens in 2026 and why renting from US-based API providers is destroying your unit economics.

15 September 2026

What It Costs to Train a Text-to-Video Model: Real GPU Budgets

Calculate the real cost of a text-to-video training run using active parameters, latent tokens, and dense MFU. Build a realistic campaign budget in GPU-hours to multiply by your own quoted hardware rates.

14 September 2026

AI Act Article 26: Deployer Logging & 6-Month Retention Explained

For high-risk AI deployers, Article 26(6) requires keeping system logs for at least six months. When using a zero-retention API, the provider stores nothing, meaning this logging capability must be built entirely within your own application.

11 September 2026

What a Works Council and DPO Ask Before an AI Coding Tool Ships

Before an AI coding tool ships, IT compliance, Data Protection Officers, and Works Councils must approve the deployment. Here is exactly what they ask about pattern learning, employee monitoring, and data residency, and how to build an approval package that satisfies them.

11 September 2026

Verify Coding Agent Function Calling Before Switching

When migrating an AI coding agent to a sovereign inference engine, ensuring OpenAI-compatible function calling is critical. Learn how to test tool calling support on open-weight models to avoid broken workflows.

11 September 2026

Self-Hosting Code Completion for VS Code and JetBrains

Transitioning from closed ecosystems to a VS Code open source code completion model guarantees data sovereignty for European engineering teams. By pairing a local IDE extension with an EU-hosted serverless inference endpoint, developers achieve low-latency coding assistance.

11 September 2026

Qwen3-235B Cost per Token Across Providers

If the same Qwen3-235B is priced very differently across providers, this page names the five dimensions behind the gap and shows how to compare like for like.

11 September 2026

Per-Token vs Per-GPU-Hour: Which Inference Pricing Fits

If you are choosing between paying per token and paying per GPU-hour, this guide reframes it as a question about who carries the utilisation risk and matches each option to a traffic shape.

8 September 2026

AI Dubbing Costs: GPU Pipelines vs Commercial APIs

If you are costing an AI dubbing feature, this guide breaks the chain into its four stages and prices each one built against bought. Compare a self-built GPU pipeline against commercial dubbing APIs on a normalised cost per minute of finished audio.

7 September 2026

MiniMax-M3: EU-Hosted Where M2.5 Is Global

If you are evaluating MiniMax under a data-residency requirement, this page states which of the two models has a confirmed European region and what choosing it costs you per token.

7 September 2026

GPU Cost for Batch Vision Inference: DINOv3, SAM & CLIP

If a batch vision job is running slower than the GPU suggests it should, this guide finds the real bottleneck first and then sizes batch, resolution and precision around it.

7 September 2026

Kimi-K2.7-Code: Code-Specialised Kimi, EU-Hosted

Kimi-K2.7-Code brings a 256K context and a confirmed EU region to open-weight coding models. With input priced at $1.25 and output at $4.50 per million tokens, understanding this ratio is critical for managing the cost of agentic software engineering.

10 September 2026

vLLM vs SGLang vs TensorRT-LLM (2026): Picking a Serving Engine

Comparing vLLM, SGLang, and TensorRT-LLM on peak throughput is the wrong approach. The real variables that dictate inference performance are model churn and prefix sharing - and for most platform teams, the most practical solution is to decline the engine choice entirely.

10 September 2026

Cheapest Way to Run DeepSeek V4 via API

Finding the cheapest DeepSeek V4 API starts by recognizing that V4 is actually two models: Pro and Flash. Before comparing provider rates, you must choose your variant and understand how input and output splits drive your true per-token cost.

9 September 2026

Static IP and DNS for GPU Inference Endpoints

Enterprise buyers often request a static IP and custom DNS for GPU inference endpoints to satisfy default-deny egress firewalls. This guide breaks down why managed endpoints rarely offer static IPs, how to structure egress policies securely, and the connectivity questions to ask providers. For Dedicated Inference and large workloads, contact a Lyceum engineer to review custom dedicated deployment options.

7 September 2026

Whisper Transcription: GPU Cost & Batch Throughput Sizing

Running batch speech-to-text on massive audio archives through managed APIs scales costs linearly with every audio hour you send. Moving Whisper pipelines to self-hosted European GPUs and optimizing with CTranslate2 converts that per-minute bill into a GPU-hour bill you can size, measure and control.

3 September 2026

Text Embedding API: Throughput, Batching and Cost

Embedding inference is prefill-only, fundamentally changing how workloads scale. Size your corpus backfill and live query path separately, eliminate padding waste, and decide between serverless and dedicated endpoints based on duty cycle rather than instinct.

3 September 2026

Open Models with Reliable Function Calling & JSON Output

Discover why reliable JSON output is more than just picking a model off a leaderboard. Learn how to combine open models, inference-engine constraints like guided decoding, and tiered retry logic to build cost-effective function calling pipelines.

1 September 2026

Qwen3.8 Flash Next: specs, benchmarks and Lyceum API

Qwen3.8 Flash Next is a multimodal MoE model previewing the Qwen4 architecture, activating just 6B parameters per token for high-efficiency agent workflows. It is available on Lyceum Serverless Inference via an OpenAI-compatible API with no infrastructure overhead.

1 September 2026

Qwen3.8 27B: specs, benchmarks, and how to run it on Lyceum

Qwen3.8 27B is a 27-billion-parameter dense multimodal model offering a 256K context window. Now available on Lyceum Serverless Inference, it supports prompt caching and built-in reasoning traces for complex agentic workloads at $0.40 per 1M input tokens.

1 September 2026

Qwen3.8 2.4T A95B: specs, benchmarks, and how to run it on Lyceum

Qwen3.8 2.4T A95B is the new open-weight flagship, featuring a 256K context window and a hybrid-attention MoE architecture. It is available now on Lyceum Serverless Inference via an OpenAI-compatible API, billed per token with zero provisioning overhead.

1 September 2026

GLM-5.3 Flash: specs, benchmarks, and how to run it on Lyceum

GLM-5.3 Flash is ZAI's highly efficient MoE model, featuring 18B active parameters and a 1M-token context window. Available now on Lyceum Serverless Inference, it delivers frontier coding and agentic capabilities starting at $0.05 per 1M cached input tokens.

2 September 2026

Prefill/Decode Disaggregation: Faster Long-Context LLM Serving

Prefill-decode disaggregation splits compute-heavy prompt processing from memory-bound token generation onto separate GPU pools. It eliminates the latency spikes caused when long contexts stall active decodes, optimizing SLO adherence without sacrificing hardware utilization.

2 September 2026

OpenAI & Anthropic Replacements: Open-Model Map

A task-by-task migration map for teams replacing OpenAI or Anthropic APIs with open-weight models. We cover the exact models, per-token prices, and hosting regions to match your specific workloads.

1 September 2026

Speculative Decoding: Acceptance Rate vs. Throughput

Speculative decoding trades spare memory bandwidth for faster token generation, but at high concurrency, it competes with real requests and slows down throughput. Here is how to calculate your acceptance rate and find the exact concurrency where your GPU stops being memory-bound.

31 August 2026

Fixing vLLM CUDA Out of Memory: KV Cache Tuning Guide

vLLM CUDA out of memory errors usually stem from startup reservations, not runtime loads. By tuning gpu_memory_utilization and max-model-len, you can right-size the KV cache and stabilize inference without renting larger GPUs.

27 August 2026

Best Open Model API for OCR and Document Extraction

Vision-language models have made traditional OCR obsolete by extracting structured JSON directly from document images. For European teams, running these models on an EU-hosted, zero-retention API solves the GDPR compliance challenge of processing invoices and contracts.

27 August 2026

Best Open Model for RAG Generation: Which Size Wins

When building a RAG pipeline, the generation model acts as a reading comprehension engine rather than a factual knowledge base. Discover why choosing an efficient 30B model over a massive 235B architecture slashes your compute bill while delivering the exact same answers.

26 August 2026

30B vs 70B vs 235B: How to Pick Open Model Size Per Task

Parameter count is no longer a reliable proxy for inference cost. With Mixture-of-Experts architectures breaking the linear pricing curve, you can stop guessing and use a simple per-token price ladder to size open models precisely against your workload.

26 August 2026

Best Multilingual Embedding APIs for RAG (2026)

Choosing the right multilingual embedding API requires testing on your own corpus rather than trusting aggregate leaderboard scores. Here is how to evaluate retrieval quality across languages, avoid silent vector mismatches, and leverage Lyceum's EU-hosted Qwen3-Embedding-8B.

24 August 2026

Which Open-Weight Models Are Actually Hosted in Europe, and Where

Navigating EU data residency requires mapping exactly where your compute runs. This guide details which open-weight models are EU-hosted and how zero data retention is engineered in VRAM to ensure strict European compliance.

24 August 2026

Zero Data Retention in LLM Inference: How to Verify It

Enterprise AI teams risk exposing proprietary data to LLM APIs with hidden retention policies. True zero data retention means prompts exist only in temporary GPU memory. Here is how to verify provider claims and build a stateless, GDPR-compliant inference architecture.

21 August 2026

How to Test an Open-Weight Model for Free Before You Commit

Evaluating open-weight models on free API tiers allows teams to benchmark latency, cost, and quality without hardware capex. By pairing free trial credits with an automated evaluation harness, engineers can validate an LLM's performance on domain-specific tasks before committing.

20 August 2026

DeepSeek-V4-Flash: specs, benchmarks, and how to run it

DeepSeek-V4-Flash is a 284-billion parameter MoE model offering agentic reasoning across a 1-million token context window. Lyceum serves it via an OpenAI-compatible API from eu-north1 in the European Union, optimized for enterprise inference at $0.15 per million input tokens.

20 August 2026

Per-Seat Licences vs Per-Token Inference: Where the Line Sits

For enterprise AI, the math is shifting from per-seat licences that start at $30 per user per month to consumption-based inference. Transitioning to per-token open models scales AI usage without artificially inflating headcount costs, provided you control the output-token tax.

1 August 2026

DeepSeek V4 Flash: 1M-Token Context for AI Products

DeepSeek V4 Flash introduces a 284B parameter MoE architecture with 13B active parameters, delivering low time-to-first-token latency and a 1,048,576-token context window. For AI-native products, this means high-throughput agent loops and long-context retrieval hosted natively in Europe

14 August 2026

How Lyceum's Serverless Inference Billing Works

Lyceum's billing model is built to eliminate idle waste and hidden networking fees. By combining pay-per-token Serverless Inference with per-second workload execution and zero egress charges, it ensures you only pay for the exact compute and tokens your models use.

14 August 2026

DeepSeek V4 Pro API: EU Hosting, Pricing and Context Limits

DeepSeek V4 Pro API runs in European data centres with 1M token context, $2.00/$4.00 pricing per 1M tokens, zero data retention, and full OpenAI SDK compatibility.

16 May 2026

Deploying Microsoft Phi-4 Inference on GPU Cloud: A Production Guide

30 July 2026

Schrems II and LLM Hosting: Navigating Data Residency Risks

The legal landscape for AI infrastructure in Europe has shifted from theoretical concern to operational risk. The intersection of the GDPR, the US Cloud Act, and the phased implementation of the EU AI Act has created a complex environment for CTOs and ML engineers. While many US-headquartered providers offer 'EU Regions,' the underlying ownership of the infrastructure remains a critical point of failure for compliance. For startups handling sensitive medical, financial, or manufacturing data, the physical location of a GPU is only half the battle. The real challenge lies in jurisdictional sovereignty and the technical reality of how prompt data, model weights, and logs are managed across borders.

15 April 2026

Serverless GPU Inference: Architecture, Economics, and Compliance

13 August 2026

Modal vs RunPod for Serverless GPU Inference

Modal and RunPod offer leading serverless GPU platforms, but actual cost is driven by billing mechanics like idle timeouts and cold starts, not just the per-hour rate. This comparison breaks down deployment lock-in, serverless premiums, and strict EU compliance options.

13 August 2026

Hugging Face Inference Endpoints Cost vs Serverless GPU

Hugging Face Inference Endpoints bill by the instance hour, meaning you pay for uptime instead of actual usage. For low-traffic APIs, an always-on endpoint is an expensive overspend. We analyze the duty-cycle crossover where serverless GPUs become the cheaper choice.

13 August 2026

How to Estimate Serverless Inference Costs Before You Commit

Provider quotes for serverless inference are difficult to compare. By understanding the core identity that converts throughput into cost per token, you can evaluate quotes against your own workload's batching, quantization, and utilisation metrics.

13 August 2026

Finding the Cheapest Open Model That Clears Your Quality Bar

Most teams default to the largest models available, driving up inference bills unnecessarily. By defining a strict quality bar and testing from the cheapest open model upward, you can drastically reduce compute costs without sacrificing output quality.

13 August 2026

Image Generation API Pricing: Cost Per Image Compared

Per-image pricing hides the real cost drivers of generative AI: diffusion steps and resolution. This guide breaks down how to calculate true cost per image, compares leading API providers, and proves exactly when a dedicated GPU mathematically beats pay-as-you-go billing.

12 August 2026

AWS Bedrock Pricing Explained: What You Actually Pay Per Token

AWS Bedrock token prices are only the baseline. To forecast your real inference costs, you must account for separate input and output rates, provisioned throughput commitments, and hidden data transfer fees, and compare those against EU-sovereign open-model endpoints.

12 August 2026

Azure OpenAI Token Pricing vs EU Open-Model APIs

Azure OpenAI's complex token pricing and PTU commitments can quickly inflate inference costs, and varying deployment types obscure true data residency. Moving to an EU-sovereign, open-model API drastically cuts total compute spend while guaranteeing GDPR compliance by design.

12 August 2026

EU-Hosted Inference Cost: The Sovereignty Premium Measured

The assumption that EU data sovereignty carries a pricing premium ignores the hidden costs of public cloud infrastructure. When accounting for hyperscaler egress fees, idle GPU waste, and the legal overhead of Schrems II compliance, EU-hosted inference is frequently cheaper.

12 August 2026

Groq Alternatives in Europe: Fast Inference Inside the EU

While Groq's custom LPUs deliver massive token generation speed, European teams face severe transatlantic network latency that undermines these gains. By hosting models locally on sovereign infrastructure, enterprises recover the Time to First Token gap and ensure GDPR compliance.

27 June 2026

Qwen3-Embedding-8B: specs, benchmarks, and how to run it on Lyceum

Qwen3-Embedding-8B delivers state-of-the-art retrieval performance across 100+ languages. Built on the Qwen3 foundation, it supports customizable output dimensions and instruction-aware queries for complex RAG pipelines.

27 June 2026

Wan Image: specs, benchmarks, and how to run it on Lyceum

Wan Image delivers photorealistic generation with advanced prompt adherence. Here is how to deploy it on Lyceum Technology.

25 June 2026

Qwen3-235B-A22B: specs, benchmarks, and how to run it on Lyceum

Qwen3-235B-A22B-Instruct-2507 is Alibaba's flagship Mixture-of-Experts model, activating only 22B parameters per token for efficient performance. With a 256K context window and strong coding capabilities, it rivals top-tier proprietary models.

25 June 2026

Qwen3-30B-A3B: specs, benchmarks, and how to run it on Lyceum

Qwen3-30B-A3B activates only 3 billion parameters per token, delivering the reasoning capabilities of a 30B model at high speeds. Learn how to deploy this cost-efficient MoE model on Lyceum's EU-sovereign infrastructure.

22 June 2026

Nemotron-3-Nano-30B: specs, benchmarks, and how to run it on Lyceum

NVIDIA's Nemotron-3-Nano-30B-A3B combines a Mamba-Transformer architecture with a Mixture-of-Experts design to deliver top-tier reasoning at a fraction of the compute cost. Here is how to deploy it on Lyceum's EU-sovereign infrastructure.

21 June 2026

MiniCPM-V 4.5: specs, benchmarks, and how to run it on Lyceum

MiniCPM-V 4.5 scores 77.0 on OpenCompass in an efficient 8B package. With its novel 3D-Resampler, it compresses video tokens by 96x, making long-video understanding highly cost-effective.

21 June 2026

MiniMax-M2.5: specs, benchmarks, and how to run it on Lyceum

MiniMax-M2.5 delivers frontier-level coding performance at a fraction of the cost of proprietary models. Learn how to deploy this 230B parameter MoE model on Lyceum's serverless platform.

19 June 2026

Image Ultra: specs, benchmarks, and how to run it on Lyceum

Image Ultra delivers high-quality image generation in under one second. Designed for latency-sensitive applications, it offers a drop-in OpenAI-compatible API on EU-sovereign infrastructure.

18 June 2026

gpt-oss-120b: specs, benchmarks, and how to run it on Lyceum

gpt-oss-120b brings OpenAI's reasoning capabilities to the open-source ecosystem. With 117B parameters and a sparse MoE architecture, it delivers o4-mini-level performance while fitting on a single 80GB GPU.

18 June 2026

Hermes-4-405B: specs, benchmarks, and how to run it on Lyceum

Hermes-4-405B introduces a hybrid reasoning mode that balances fast responses with deep, think-tag deliberation. Now available on Lyceum's European infrastructure, it delivers strong math and coding performance without the censorship of proprietary models.

17 June 2026

GLM-5.1: specs, benchmarks, and how to run it on Lyceum

GLM-5.1 is a Mixture-of-Experts model with 754B parameters and 40B active per token, built for sustained, multi-step software engineering tasks. With a leading SWE-Bench Pro score among the models on its own card, it offers an open-weight alternative to frontier proprietary models.

16 June 2026

FLUX.1 Dev: specs, benchmarks, and how to run it on Lyceum

FLUX.1 Dev brings strong prompt adherence and photorealism to open-weights image generation. Learn how to deploy this 12B parameter rectified flow transformer on Lyceum's EU-hosted infrastructure.

16 June 2026

FLUX.2 Klein: specs, benchmarks, and how to run it on Lyceum

FLUX.2 Klein optimizes the speed-to-quality ratio for AI image generation. With a unified architecture for text-to-image and editing, it delivers photorealistic 1024x1024 outputs in under a second.

14 June 2026

GDPR and EU AI Act Overlap: Technical Guide for AI Infrastructure

Securing personal data is no longer enough. Engineering teams must now architect their machine learning pipelines to meet stringent product safety and risk management standards.

13 June 2026

EU AI Act High Risk System Classification Guide

The EU AI Act introduces strict obligations for high risk AI systems, with penalties reaching 15 million euros. Engineering teams must understand classification rules and infrastructure requirements to avoid regulatory roadblocks.

13 June 2026

EU AI Act Prohibited AI Systems Checklist for Engineering Teams

The grace period for unacceptable risk AI systems ended on February 2, 2025. Engineering teams running models that breach the Article 5 prohibitions now face fines up to €35 million or 7% of global turnover, whichever is higher.

12 June 2026

EU AI Act Compliance Timeline: Navigating the August 2026 Deadlines

August 2026 remains a hard deadline for transparency, GPAI enforcement, and data governance. Engineering teams must secure their infrastructure now to avoid severe penalties.

12 June 2026

EU AI Act Foundation Model Obligations 2026: A Technical Guide

The grace period is ending. By August 2026, the European Commission will actively enforce compliance for foundation models, turning data residency and infrastructure choices into critical engineering constraints.

11 June 2026

EU AI Act Conformity Assessment: The GPU Infrastructure Guide

The high-risk deadlines now fall on 2 December 2027 and 2 August 2028. Your conformity assessment will fail if your underlying GPU infrastructure cannot prove data sovereignty, logging traceability, and strict access controls.

11 June 2026

vLLM vs TensorRT-LLM: Production Benchmark & Guide

Choosing the right inference engine dictates your infrastructure costs and user experience. We break down the latest performance data to help you optimize your production deployments.

10 June 2026

Serverless GPU Cold Start Latency: Architecture Comparison

Scale-to-zero GPU infrastructure promises massive cost savings, but a 40-second cold start will kill any real-time AI application. Here is a technical breakdown of where the time actually goes and how modern inference stacks are solving the VRAM bottleneck.

9 June 2026

The 2026 Guide to AI Inference SLAs: Uptime, Economics, and EU Compliance

Deloitte expects inference to take roughly two-thirds of all compute in 2026. When your application relies on sub-second LLM responses, every minute of provider downtime lands on a live user session.

9 June 2026

2026 LLM Inference Latency in Europe: GPU Cost Guide

Inference now accounts for the majority of AI GPU spend. Here is how European engineering teams are optimizing latency, throughput, and cost per token on H100 infrastructure in 2026.

8 June 2026

EU vs US Inference API Latency: The Cost of Transatlantic AI

Sending inference requests across the Atlantic adds roughly 75 to 160 milliseconds of unavoidable fiber latency. For modern compound AI systems, that delay multiplies exponentially, degrading user experience while exposing sensitive data to US jurisdictions.

8 June 2026

Llama 3 vs Mistral vs Qwen: 2026 Model Selection Guide

Choosing the right open-weight model is only half the battle. See how Llama 3, Mistral, and Qwen compare on VRAM, quantization, and serving cost, and how to size the infrastructure behind them.

7 June 2026

GPU Vector Database Cloud Integration: Architecture Guide

Vector databases are hitting the billion-vector scale, and CPU-bound indexing is choking under the load. Moving vector search to GPUs cuts index build times by up to 17x, but deploying this infrastructure requires strict attention to data sovereignty and cost control.

6 June 2026

Tool Calling Latency in LLM Inference: Production Optimization

Tool calling transforms language models into capable agents, but it introduces massive latency bottlenecks. Learn how to optimize inference engines, reduce token overhead, and deploy high-performance infrastructure.

5 June 2026

Scaling Multi-Agent Orchestration: GPU Memory, Inference, and Costs

Multi-agent systems work flawlessly on a local machine but break under production load. Learn how to decouple orchestration from inference and scale your GPU infrastructure efficiently.

5 June 2026

RAG Pipeline GPU Infrastructure: The Engineering Guide

You built a RAG pipeline. It retrieves 20 chunks, sends 32,000 tokens to the LLM, and your GPU throws an Out of Memory (OOM) error. Memory management in RAG is not a software problem. It is a hardware budget.

4 June 2026

The 2026 Guide to GPU Infrastructure for AI Agents

Autonomous AI agents demand distributed infrastructure optimized for latency and bursty traffic. Building for agentic workflows requires rethinking VRAM allocation, cold starts, and compliance.

4 June 2026

Long Context Inference: GPU Requirements & VRAM Guide

Context kills VRAM. Learn the exact math behind KV cache bottlenecks and how to architect your GPU infrastructure for 128K+ token workloads.

3 June 2026

EU Compliant AI Agent Infrastructure: The 2026 Engineering Guide

Agentic AI multiplies token consumption compared to standard generative AI, because every reasoning step resends the accumulated context. Running these workloads on non-sovereign infrastructure exposes engineering teams to compliance risks and unsustainable hyperscaler costs.

2 June 2026

Agent Inference Cost Optimization: Engineering the 2026 Stack

Agentic workflows multiply token consumption several times over compared to standard chat interfaces. We break down the engineering techniques and infrastructure decisions required to keep LLM inference costs viable at scale in 2026.

2 June 2026

Run Vision Language Models on GPU Cloud: VRAM & Setup Guide

Vision language models consume massive VRAM for image tokens. Learn the exact hardware requirements and deployment strategies for production VLMs.

1 June 2026

2026 Open-Source LLM Comparison: Benchmarks & Enterprise Deployment

Open-source models now match proprietary alternatives in reasoning and coding. For European engineering teams, the challenge has shifted from model selection to sovereign, GDPR-compliant deployment.

1 June 2026

Open Source vs Closed API LLM Cost Comparison

API token prices have plummeted, but at scale, pay-as-you-go models still drain budgets. We work the arithmetic on where self-hosting open-source LLMs becomes cheaper than closed APIs, with every assumption shown.

31 May 2026

LLM Context Length vs. GPU Memory: Calculating VRAM Requirements

Parameter count only tells half the story. Learn how to calculate the exact GPU memory required for long-context LLM inference and avoid catastrophic Out-of-Memory errors in production.

31 May 2026

Multimodal AI Inference on European GPUs: Compliance and Cost Optimization

Running multimodal AI inference at scale exposes the structural flaws of hyperscaler pricing and compliance models. Engineering teams require infrastructure that provides high throughput for complex data types while maintaining strict data residency.

30 May 2026

Deploy Whisper Large v3 GPU API: VRAM, Performance & EU Hosting

Running Whisper Large v3 in production requires strict VRAM management and optimized inference engines. For European teams, it also demands provable data sovereignty.

30 May 2026

The Guide to Serving Fine-Tuned LLMs in Production

Training a model is no longer the hard part. Serving fine-tuned models at scale requires avoiding memory bottlenecks and excessive costs for idle GPUs.

29 May 2026

Deploy Qwen 2.5 72B on GPU Cloud: VRAM Sizing and vLLM Setup

Running Qwen 2.5 72B in production requires strict memory management and the right infrastructure. Learn how to calculate VRAM requirements, configure vLLM, and deploy on EU-sovereign GPUs without hyperscaler price premiums.

28 May 2026

Deploy Gemma 3 on European GPU Cloud: VRAM, Setup, and GDPR Compliance

Google's Gemma 3 models bring multimodal capabilities and 128K context windows to open weights AI. Running them in production requires careful VRAM planning and infrastructure that guarantees data residency.

28 May 2026

Deploy a Hugging Face Model Inference API: 2026 Production Guide

Moving a Hugging Face model from a local notebook to a production API requires solving three hard problems: GPU memory fragmentation, unpredictable cold starts, and strict data residency requirements.

27 May 2026

Deploy DeepSeek R1 on European GPU Cloud: VRAM, Costs, and Compliance

Deploying DeepSeek R1 requires massive VRAM and strict data governance. Learn how to size your hardware and run production inference on EU-sovereign infrastructure without hyperscaler markups.

24 May 2026

Deploy Hugging Face Model to GPU Cloud

Moving a Hugging Face model from a local notebook to production requires strict VRAM math and the right inference engine. Learn how to deploy open-source LLMs at scale without hyperscaler cost overruns.

20 May 2026

Inference Cost Per Token vs. Dedicated GPU: 2026 Economics

Token-based billing is a retail markup on compute. As your AI product scales, paying a US-based provider for every word generated becomes your largest line item. We break down the engineering math behind the switch to dedicated GPUs.

18 May 2026

GGUF vs GPTQ vs AWQ: The Definitive LLM Quantization Framework

We break down the exact performance, memory, and throughput differences between GGUF, GPTQ, and AWQ for production inference.

15 May 2026

LLM Inference Cost Per Token: Serverless vs. Dedicated Comparison

Inference cost per unit of model quality keeps falling, yet AI infrastructure bills continue to climb. We break down where dedicated GPUs become cheaper than serverless APIs, and how to work out your own threshold.

9 May 2026

US-Based Inference APIs vs. EU Sovereign Providers: A Strategic Guide

When hyperscaler credits expire, infrastructure decisions shift from prototyping speed to production sustainability. Here is why relying on US-based APIs introduces severe compliance risks, and how the open-source stack has closed the performance gap.

3 May 2026

Fireworks and Baseten Alternatives in Europe: A Strategic Guide

US-based managed inference platforms offer excellent developer experiences but fail on EU data sovereignty and cost at scale. Learn how European ML teams are migrating to sovereign infrastructure to maintain compliance and reduce GPU spend.

28 April 2026

Host LLM in Europe Without US Data Transfer: A Technical Guide

European AI teams face a critical choice: scale on US-based infrastructure and risk regulatory non-compliance, or build on sovereign EU foundations. This guide explores how to deploy high-performance LLMs in European data centres, and where the exceptions to that footprint actually sit.

27 April 2026

GDPR Compliant LLM Inference: A Guide for European AI Teams

European AI startups face a critical choice between high-performance inference and the data residency terms customers and regulators expect. As hyperscaler credits expire and scrutiny intensifies, teams must move to infrastructure whose processing locations and transfer mechanisms they can document, without giving up low latency.

26 April 2026

European Alternatives to US Inference APIs: A Sovereignty Guide

For European AI teams, the choice of inference infrastructure is no longer just about latency or price. Regulatory pressure and the high cost of US hyperscalers are driving a migration toward sovereign European alternatives that offer provable data residency.

25 April 2026

EU Sovereign Inference Platform Comparison: 2026 Technical Guide

European AI teams face a critical choice between high-performance US inference platforms and strict GDPR compliance. This guide compares technical architectures and legal frameworks to help you select a sovereign infrastructure that scales without regulatory risk.

24 April 2026

Data Residency for LLM APIs: A Guide for European AI Teams

European AI startups face a critical choice: optimize for speed using US-based APIs or prioritize compliance to win enterprise contracts. This guide explores why data residency is no longer optional for teams scaling LLM applications in regulated markets.

23 April 2026

Serverless Inference Cold Start Latency: A Technical Optimization Guide

Cold starts remain the primary barrier to responsive serverless AI. This guide breaks down the technical stages of GPU initialization and provides a framework for minimizing latency in production environments.

23 April 2026

vLLM Production Deployment Guide: Scaling Sovereign Inference

Moving LLMs from experimental notebooks to production-grade infrastructure requires more than just raw compute. This guide explores how to navigate memory fragmentation, optimize KV caches, and maintain GDPR compliance while scaling vLLM in 2026.

22 April 2026

Self-Host LLM APIs on EU Infrastructure: The Modern Guide

As hyperscaler credits expire and the EU AI Act's high-risk obligations phase in, deferred to 2 December 2027 for Annex III systems and 2 August 2028 for Annex I systems, AI teams are moving toward sovereign infrastructure. This guide explores how to self-host LLM APIs in Europe to ensure data residency without sacrificing performance.

21 April 2026

Reduce LLM Inference Latency on GPUs: A Technical Guide

High latency in LLM inference drives up compute costs and degrades user experience. This guide explores the hardware and software strategies required to minimize Time to First Token (TTFT) and maximize throughput on modern NVIDIA GPUs.

21 April 2026

The Economics of Scale to Zero: Slashing GPU Inference Costs in 2026

Running dedicated GPU instances for bursty inference workloads is the fastest way to burn through venture capital. Scale-to-zero orchestration allows teams to eliminate idle compute costs without sacrificing the performance required for production-grade AI.

20 April 2026

OpenAI Compatible API Self Hosted: A Guide for EU AI Teams

Relying on proprietary US-based APIs creates significant risks for European AI teams, from GDPR non-compliance to unsustainable scaling costs. By adopting a self-hosted, OpenAI-compatible architecture, you can maintain full control over your data residency while moving to per-second and per-token pricing you can model directly against your own traffic.

20 April 2026

Pay Per Token vs Dedicated GPU Inference: The Break-Even Guide

As hyperscaler credits expire, AI startups face a critical infrastructure fork: continue paying per token or move to dedicated GPUs. This guide breaks down the utilization math, latency trade-offs, and sovereignty requirements for European engineering teams.

19 April 2026

Multi-Model Serving on Single GPUs with vLLM and PagedAttention

Dedicating a high-end GPU to a single model often leaves most of the card idle and the unit economics unsustainable. Modern inference stacks now allow for concurrent model execution on a single H100 or B200 node without the latency penalties of traditional context switching.

19 April 2026

NVIDIA Dynamo: A Technical Guide to Inference Orchestration

The recent release of NVIDIA Dynamo has fundamentally shifted the landscape for AI infrastructure leads. By bridging the performance gap between open-source frameworks and proprietary engines, this orchestration layer allows teams to maintain full portability without sacrificing throughput.

18 April 2026

Host Fine-Tuned Model Production APIs: A Technical Guide

Moving a fine-tuned model from a local notebook to a production API requires solving for memory management, cold starts, and unsustainable hyperscaler costs. This guide explores the technical architecture needed to serve LLMs with high throughput while keeping processing inside European data centers.

18 April 2026

Self-Hosted LLM API Gateway Guide: Architecture and Infrastructure

Fragmented model access often leads to security vulnerabilities and unpredictable cost overruns. A self-hosted LLM API gateway centralizes control, ensuring GDPR compliance while providing a unified interface for your inference workloads.

17 April 2026

Deploying Mistral Large on European GPU Cloud Infrastructure

European AI teams face a dilemma: high-performance LLMs like Mistral Large 2 require massive GPU clusters, but US-based clouds often fail strict GDPR and data residency requirements. This guide explores how to deploy Mistral Large 2 on EU-sovereign infrastructure without the hyperscaler price tag.

17 April 2026

Deploying Private LLM Endpoints on GPU Cloud: A 2026 Strategy

As AI startups outgrow their initial cloud credits, the shift toward private LLM endpoints becomes a necessity for cost control and GDPR compliance. This guide examines the technical architecture and economic frameworks required to deploy high-performance inference on European GPU infrastructure.

16 April 2026

Deploying Custom Docker Model Inference APIs for Production

Moving beyond black-box APIs requires a robust containerization strategy and optimized GPU orchestration. This guide explores how to build and deploy custom Docker inference endpoints that maintain data residency while maximizing throughput.

16 April 2026

Deploying Llama 3 Inference APIs on Sovereign GPU Clouds

Scaling Llama 3 inference requires balancing VRAM bottlenecks against unsustainable hyperscaler costs. This guide explores how to deploy production-grade APIs using European infrastructure and modern orchestration stacks.

15 April 2026

Optimizing LLM Inference Throughput with Batching Strategies

Maximizing GPU utilization requires moving beyond simple request-level processing. This guide explores how continuous batching and PagedAttention solve the memory bandwidth bottleneck for production LLM serving.