Nano-vLLM: How a vLLM-style inference engine works
2 days ago
- #Inference Engine
- #LLM
- #GPU Optimization
- LLM inference engines are critical for deploying large language models in production.
- Nano-vLLM is a minimal yet production-grade implementation of an inference engine, comparable to vLLM.
- The engine uses a producer-consumer pattern with a scheduler to manage sequences efficiently.
- Batching sequences improves throughput but introduces a trade-off between latency and throughput.
- LLM inference has two phases: prefill (processing input prompts) and decode (generating output tokens).
- The scheduler manages sequences in waiting and running queues, handling resource exhaustion.
- The Block Manager divides sequences into fixed-size blocks for efficient GPU memory management.
- Prefix caching via hashing allows reuse of common prefixes in sequences, improving efficiency.
- Tensor parallelism splits the model across multiple GPUs for large models.
- CUDA graphs reduce kernel launch overhead by pre-recording GPU operations.
- Sampling converts logits into tokens, controlled by temperature for variability in outputs.