vLLM
The serving engine when throughput is the whole problem.
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Every piece below comes from hands-on work with vLLM: pricing, real setup, where it wins, where it falls short, and the alternatives worth weighing.
vLLM — Tool Lab art
Articles featuring vLLM7 articles
Continuous Batching: How It Improves LLM Throughput
How continuous batching improves shared LLM throughput, changes latency, and works with KV cache in vLLM, SGLang, TensorRT-LLM and TGI.Oct 25, 2026Build
LLM Inference Explained: Prefill, Decode and Serving Choices
Understand LLM inference, from prefill and decode to caching and batching, then choose between a model API, an inference provider and your own GPUs.Oct 21, 2026Build
LLM Quantization: GGUF, AWQ, GPTQ and FP8 Explained
Understand the memory, speed and quality tradeoffs of LLM quantization, with a worked estimate and format choices for local runtimes and APIs.Oct 20, 2026Build
KV Cache Explained: Prefill, Decode and Prompt Caching
Understand KV cache in LLM inference, prefill vs decode, memory growth and prompt caching across Anthropic, OpenAI, Google and vLLM.Oct 15, 2026Build
vLLM vs SGLang
Compare vLLM and SGLang for agents and RAG: current releases, hardware, caching, JSON, LoRA, deployment and GPU cost calculations.Oct 14, 2026Build
Ollama vs vLLM
Choose Ollama or vLLM by workload, GPUs, and model size. Compare setup, concurrency, quantization, API compatibility, and migration costs.Oct 12, 2026Build
Ollama Alternatives in 2026: LM Studio, vLLM, llama.cpp, and Jan (Verified July 2026)
Ten Ollama alternatives compared for desktop, Apple Silicon, private APIs, mobile deployment, and production serving. Verified July 2026.Jul 29, 2026AINewsletter
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