vllm

Implements a continuous batching scheduler that dynamically groups inference requests into GPU batches without waiting for all requests to complete, using the Scheduler and InputBatch state management system. Requests are added/removed mid-batch as they finish, maximizing GPU utilization by eliminating idle cycles between request completion and new request arrival. The scheduler tracks request state through the RequestLifecycle and allocates KV cache slots dynamically.

Manages GPU KV cache allocation across concurrent requests using a hierarchical slot-based allocator with support for prefix caching, which reuses KV cache blocks for repeated prompt prefixes across requests. The system tracks cache block ownership, eviction policies, and supports disaggregated serving where KV cache can be transferred between workers. Implements block-level granularity to minimize memory fragmentation and enable cache sharing across requests with common prefixes (e.g., system prompts, RAG context).

Provides a Model Registry that automatically detects model architectures from HuggingFace model IDs and loads appropriate model implementations. The system uses configuration parsing to identify model type (LLaMA, Qwen, Mixtral, etc.), then selects the corresponding modeling backend from the Transformers Modeling Backend. Supports custom model registration for non-standard architectures, enabling extensibility without modifying core code.

Implements an Attention Backend Selection system that automatically chooses the optimal attention implementation based on hardware capabilities and model requirements. Supports multiple attention backends including FlashAttention (fast approximate attention), FlashInfer (optimized for inference), and platform-specific implementations (ROCm, TPU). The system benchmarks available backends at startup and selects the fastest option, with fallback to standard attention if specialized backends are unavailable.

Provides comprehensive metrics collection through a Metrics and Observability system that tracks request latency, throughput, GPU utilization, cache hit rates, and other performance indicators. Metrics are collected at multiple levels: request-level (time-to-first-token, inter-token latency), batch-level (batch size, batch composition), and system-level (GPU memory, compute utilization). Integrates with monitoring systems through Prometheus-compatible metrics export.

Supports offline inference mode for batch processing where requests are read from files or data structures, processed in optimized batches, and results written to output files. The offline mode bypasses the HTTP server and request queue, enabling higher throughput for non-interactive workloads. Supports various input formats (JSONL, CSV, Parquet) and output serialization formats, with automatic batch composition for maximum GPU utilization.

Implements speculative decoding by running a smaller draft model to generate candidate tokens, then verifying them against the target model in parallel. The system uses a two-stage pipeline: draft model generates k tokens speculatively, then the target model validates all k tokens in a single forward pass. If verification succeeds, all k tokens are accepted; otherwise, the system falls back to the last verified token and continues. This reduces effective latency by amortizing target model inference across multiple tokens.

Supports distributed execution across multiple GPUs using tensor parallelism (splitting model layers across GPUs) and pipeline parallelism (splitting model stages across GPUs), coordinated through a multi-process engine architecture. The system uses NCCL for inter-GPU communication and implements a Communication Infrastructure layer that handles collective operations (all-reduce, all-gather) for gradient/activation synchronization. Workers are managed through the Worker and Executor Architecture, with each worker running on a separate GPU and coordinating through the EngineCore.

Supports multiple quantization methods including FP8 (8-bit floating point), INT8, and INT4 to reduce model size and memory footprint while maintaining inference quality. The system implements quantization through a modular backend that applies quantization to weights and activations, with support for per-channel and per-token quantization. FP8 quantization is particularly optimized for NVIDIA GPUs with native FP8 support (H100, L40S), using hardware-accelerated matrix operations to minimize performance overhead.

Optimizes inference for Mixture-of-Experts models through a FusedMoE layer architecture that combines expert selection, routing, and computation into fused CUDA kernels. The system implements efficient expert parallelism where experts are distributed across GPUs, with optimized all-to-all communication for token-to-expert routing. Supports both dense and sparse MoE patterns, with automatic kernel selection based on sparsity and hardware capabilities.

Provides an OpenAI-compatible HTTP API server that implements the OpenAI Chat Completions and Completions endpoints, enabling drop-in replacement for OpenAI's API. The server uses FastAPI for request handling, implements streaming responses via Server-Sent Events (SSE) for real-time token delivery, and includes request validation, error handling, and rate limiting. Supports both synchronous and asynchronous request processing through the async_llm interface.

Supports tool calling and structured output generation by constraining model outputs to match JSON schemas, using a constraint-based decoding approach that guides token generation to produce valid JSON. The system integrates with the sampling layer to enforce schema constraints at token generation time, preventing invalid JSON and ensuring outputs conform to specified tool signatures. Supports both OpenAI-style tool calling and arbitrary JSON schema constraints.

Supports Low-Rank Adaptation (LoRA) adapters that enable efficient fine-tuning and task-specific customization without modifying base model weights. The system manages multiple LoRA adapters in memory, allowing dynamic switching between adapters per-request through request metadata. Adapters are loaded on-demand and cached in GPU memory, with support for adapter composition (combining multiple adapters) and adapter-specific scaling.

Extends inference to multimodal models by implementing Multimodal Data Processing that handles images, audio, and text inputs. The system includes vision encoders (e.g., CLIP) that convert images to embeddings, audio processors that extract audio features, and integration with the input processing pipeline to merge multimodal embeddings with text tokens. Supports both image-to-text and audio-to-text tasks through a unified multimodal interface.