Run models without infra work

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.

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.

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 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.

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.

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

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.

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.

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.

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.

15 April 2026

Serverless GPU Inference: Architecture, Economics, and Compliance

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.

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.

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.

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.

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.

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.

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.

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.

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.