vllm

Learn to implement custom logit processors and sampling parameters in vLLM. Extend vLLM's capabilities by subclassing LogitsProcessor and registering your custom logic for advanced text generation control.

Master vLLM compilation modes eager CUDAGraph and Inductor. Learn to configure settings via CLI or Python API for optimized performance.

Debug vLLM memory issues and OOM errors with our guide. Isolate problems in workspace buffers, KV-cache, or activations using memory profiling tools.

Learn to use the vLLM gRPC API for high-performance serving. Stream tokens efficiently with unlimited message sizes and structured output support for powerful language model inference.

Master KV cache allocation and management in vLLM. Learn to set CacheConfig parameters like block_size and gpu_memory_utilization for optimized tensor allocation via CLI or Python.

Integrate LLaVA multi-modal models seamlessly with vLLM. Our guide simplifies image preprocessing and vision encoder inference for efficient LLM deployment.

Explore the vLLM v1 engine vs legacy engine architectural differences. Discover its unified scheduler, `EngineCore` loop, and removal of legacy features for enhanced performance.

Learn to configure attention backends like FlashAttention and FlashInfer in vLLM using CLI flags API or config files for faster inference. Optimize your LLM performance effortlessly.

Learn to use embedding models and pooling parameters in vLLM. This guide details how vLLM's pooling runner generates text embeddings efficiently by routing hidden states through the model's pooler.

Discover how expert parallelism in vLLM shards expert weights across GPUs for scalable distributed inference of Mixture-of-Experts models. Optimize your MoE inference.

Optimize GPU memory utilization in vLLM by setting the gpu_memory_utilization parameter. Balance throughput and OOM risk by adjusting this value. Learn how now.

Learn to implement beam search and parallel sampling in vLLM. Optimize your LLM inference with efficient batching and GPU acceleration for faster requests.