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LLM Inference Optimization: vLLM, TensorRT-LLM, and Serving at Scale

PagedAttention, continuous batching, quantization, and production serving strategies

By AI Skill Navigation Editorial TeamPublished June 9, 2026

LLM Inference Optimization: vLLM, TensorRT-LLM, and Serving at Scale (2026)

Inference (not training) is where LLMs cost the most in production—so squeezing more tokens per second per GPU dollar is the core optimization problem. This guide covers key techniques (KV cache management, batching, quantization) and the two dominant serving engines: vLLM and TensorRT-LLM.

The Bottleneck: KV Cache

Transformer inference caches the key/value tensors for every token processed to avoid recomputation—this is the KV cache. It grows with sequence length and dominates memory usage during serving. Managing it well solves most problems.

  • PagedAttention (vLLM): Manages KV cache like an OS manages virtual memory—in pages—eliminating fragmentation and allowing more concurrent sequences in the same VRAM. This is vLLM's core innovation.
  • Continuous Batching: Instead of waiting for a batch to finish, new requests join a running batch immediately, keeping the GPU saturated. This is the single biggest throughput gain for concurrent scenarios.

    • vLLM vs TensorRT-LLM

    vLLMTensorRT-LLM

    StrengthsEasy + high throughput, PagedAttentionMaximum NVIDIA GPU performance Setuppip install vllm, simpleHeavier (compile engine) Use caseMost production servingSqueeze last bit of latency/throughput on NVIDIA

    vLLM is the pragmatic default—easy to run, OpenAI-compatible, great throughput. TensorRT-LLM can be faster on NVIDIA hardware via a compiled optimized engine, but setup is more involved. Start with vLLM; move to TensorRT-LLM when you've confirmed you need the last bit of performance. For vLLM vs Ollama, see Ollama vs vLLM.

    bash

    vLLM: one command to start a high-throughput OpenAI-compatible server
    
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

    Other Techniques

  • Quantization (4-bit AWQ/GPTQ) reduces memory and bandwidth—see Model Quantization Guide.
  • Speculative Decoding uses a small draft model to propose tokens, verified by the large model, speeding up generation.
  • Prefix Caching reuses KV cache for shared prompt prefixes (system prompt, few-shot examples).
  • Pick the right model size. The simplest optimization is using a smaller model for simple requests—see GPT-4o mini vs Claude Haiku.

    • FAQ

    Why is throughput low under load? Likely no continuous batching. vLLM solves this out of the box. vLLM or TensorRT-LLM? vLLM is easy and has great throughput; TensorRT-LLM gives extreme NVIDIA performance when needed. Biggest single gain? Continuous batching, then quantization, then speculative/prefix caching. Does quantization slow things down? Usually the opposite—less memory bandwidth per token often speeds up inference.

    Summary

    Optimize inference by managing the KV cache (PagedAttention), keeping the GPU busy (continuous batching), and shrinking the model (quantization). vLLM does most of this with one command; TensorRT-LLM squeezes the last drop on NVIDIA. Measure tokens/sec on your hardware and stack gains.

    *Last updated: June 2026. Verify against vLLM and TensorRT-LLM documentation.*

    Also available in 中文.