Scale without limits
Keep latency low and throughput high as your traffic grows, from cold starts and autoscaling to rate limits.
17 articles in Scale without limitsSearch all articles
Articles
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
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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