vLLM for Production Inference — Max Open-Source LLM Throughput

Self-hosting LLMs is economically attractive at high request volumes, but inference must be efficient — GPUs are expensive and every unused gigabyte of VRAM is wasted money. vLLM with PagedAttention offers 2-4x higher throughput compared to naive implementations and is becoming the de facto standard for production LLM serving on open-source models.

PagedAttention¶

PagedAttention is vLLM’s key innovation. It manages the KV cache (key-value cache for the attention mechanism) like virtual memory with dynamic page allocation. Traditional inference allocates a fixed memory block for maximum sequence length — most of it goes unused. PagedAttention allocates pages on-demand, resulting in more efficient GPU memory utilization and the ability to serve significantly more concurrent requests on the same hardware.

Benchmarks¶

• Mistral 7B on A100: 2.5x throughput vs HuggingFace Transformers — dozens of requests per second
• Mixtral 8x7B on 2xA100: 80+ tokens/sec with tensor parallelism
• Llama 70B on 4xA100: 25+ tokens/sec with 100+ concurrent requests

Continuous batching (dynamically adding requests to a running batch) eliminates waiting for the entire batch to complete. Prefix caching accelerates repeated prompts (system prompt shared across requests). Speculative decoding with a smaller draft model further reduces latency.

Alternatives¶

• TensorRT-LLM: Fastest inference on NVIDIA hardware thanks to kernel optimizations, but vendor lock-in and more complex setup
• TGI (Text Generation Inference): HuggingFace integration, simple setup, good performance
• Ollama: Development and experimentation, not high-throughput production serving

For production on NVIDIA hardware: vLLM for flexibility and open-source, TensorRT-LLM for maximum performance. TGI as a compromise with the simplest setup.

Production Deployment¶

vLLM exposes an OpenAI-compatible API, making migration from OpenAI API trivial — just change the base URL. Kubernetes deployment with horizontal pod autoscaling on GPU metrics (utilization, queue depth) ensures elastic scaling based on load. For multi-model serving, consider vLLM with LoRA adapters — one base model, multiple fine-tuned variants without duplicate memory.

vLLM Is the Default for LLM Serving¶

PagedAttention, continuous batching, OpenAI-compatible API, and an active community make vLLM the best choice for production open-source LLM inference.