cluster
Inference Serving
Running models in production on serverless infrastructure: deployment, scaling, cold starts, batch, rate limits and custom model hosting. Serves teams moving from evaluation to production.
30 articles
Subclusters
Articles
August 14, 2026
How to use Lyceum API within Claude Code
European AI consultancies need a GDPR-compliant way to use Claude Code. By routing it through an API wrapper, you can point the tool to Lyceum's OpenAI-compatible Dedicated Inference endpoint and use sovereign, open-weight models for sensitive client coding tasks.
June 11, 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.
June 10, 2026
LLM Inference Tokens Per Second: 2026 Hardware and Software Benchmarks
Optimizing LLM inference requires balancing memory bandwidth, quantization, and engine choice. We analyze the latest 2026 benchmarks to help you maximize throughput and minimize cost per token.
June 10, 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.
June 6, 2026
Streaming Inference API: Architecting Real-Time AI Agents
Real-time AI agents require sub-second Time-to-First-Token (TTFT) to function naturally. But achieving this on hyperscaler infrastructure often leads to cost overruns, OOM errors, and compliance risks.
June 4, 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.
June 3, 2026
Async Batch Inference & AI Agents: Scaling GPU Cloud for Agentic Workloads
AI agents break traditional auto-scaling. Learn how to manage persistent processes, avoid OOM errors, and optimize GPU utilization for complex multi-step workflows.
May 31, 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.
May 30, 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.
May 28, 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.
May 24, 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.
May 23, 2026
Autoscale GPU Inference Production: Cost Optimization and EU Compliance
Moving Large Language Models from prototype to production exposes critical infrastructure bottlenecks. Learn how to engineer autoscaling triggers, eliminate idle compute waste, and maintain strict GDPR compliance.
May 18, 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.
April 23, 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.
April 23, 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.
April 22, 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.
April 22, 2026
Serverless GPU Inference: Architecture, Economics, and Compliance
Most AI infrastructure leads struggle with low GPU utilization, which erodes margin. Serverless GPU inference offers a path to eliminate idle capacity while maintaining the low-latency performance required for production LLMs.
April 21, 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.
April 21, 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.
April 19, 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.
April 19, 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.
April 18, 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.
April 18, 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.
April 17, 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.
April 17, 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.
April 16, 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.
April 16, 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.
April 15, 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.
April 15, 2026
Dedicated vs Shared GPU Inference: Scaling AI Infrastructure
Choosing between dedicated and shared GPU resources is no longer only a cost calculation. The decision hinges on latency consistency, memory bandwidth isolation, and the strict requirements of the EU AI Act.
February 23, 2026
KV Cache Memory Calculation for LLMs: A Technical Guide
Calculating KV cache memory is critical for preventing Out-of-Memory errors and optimizing throughput in LLM deployments. This guide breaks down the mathematical formulas and architectural variables that determine your GPU memory footprint.