vLLM

Implements virtual memory-inspired paging for KV cache blocks, allowing non-contiguous memory allocation and reuse across requests. Prefix caching enables sharing of computed attention keys/values across requests with common prompt prefixes, reducing redundant computation. The KV cache is managed through a block allocator that tracks free/allocated blocks and supports dynamic reallocation during generation, achieving 10-24x throughput improvement over dense allocation schemes.

Implements a scheduler that decouples request arrival from batch formation, allowing new requests to be added mid-generation and completed requests to be removed without waiting for batch boundaries. The scheduler maintains request state (InputBatch) tracking token counts, generation progress, and sampling parameters per request. Requests are dynamically scheduled based on available GPU memory and compute capacity, enabling variable batch sizes that adapt to request completion patterns rather than fixed-size batches.

Extends vLLM to support multi-modal models (vision-language models) that accept images or videos alongside text. The system includes image preprocessing (resizing, normalization), embedding computation via vision encoders, and integration with language model generation. Multi-modal data is processed through a specialized input processor that handles variable image sizes, multiple images per request, and video frame extraction. The vision encoder output is cached to avoid recomputation across requests with identical images.

Enables disaggregated serving where the prefill phase (processing input tokens) and decode phase (generating output tokens) run on separate GPU clusters. KV cache computed during prefill is transferred to decode workers for generation, allowing independent scaling of prefill and decode capacity. This architecture is useful for workloads with variable input/output ratios, where prefill and decode have different compute requirements. The system manages KV cache serialization, network transfer, and state synchronization between prefill and decode clusters.

Provides a platform abstraction layer that enables vLLM to run on multiple hardware backends (NVIDIA CUDA, AMD ROCm, Intel XPU, CPU-only). The abstraction includes device detection, memory management, kernel compilation, and communication primitives that are implemented differently for each platform. At runtime, the system detects available hardware and selects the appropriate backend, with fallback to CPU inference if specialized hardware is unavailable. This enables single codebase support for diverse hardware without platform-specific branching.

Implements specialized quantization and kernel optimization for Mixture of Experts models (e.g., Mixtral, Qwen-MoE) with automatic expert selection and load balancing. The FusedMoE kernel fuses the expert selection, routing, and computation into a single CUDA kernel to reduce memory bandwidth and synchronization overhead. Supports quantization of expert weights with per-expert scale factors, maintaining accuracy while reducing memory footprint.

Manages the complete lifecycle of inference requests from arrival through completion, tracking state transitions (waiting → running → finished) and handling errors gracefully. Implements a request state machine that validates state transitions and prevents invalid operations (e.g., canceling a finished request). Supports request cancellation, timeout handling, and automatic cleanup of resources (GPU memory, KV cache blocks) when requests complete or fail.

Partitions model weights and activations across multiple GPUs using tensor-level parallelism, where each GPU computes a portion of matrix multiplications and communicates partial results via all-reduce operations. The distributed execution layer (Worker and Executor architecture) manages multi-process GPU workers, each running a GPUModelRunner that executes the partitioned model. Communication infrastructure uses NCCL for efficient collective operations, and the system supports disaggregated serving where KV cache can be transferred between workers for load balancing.

Exposes vLLM inference engine through an OpenAI-compatible REST API (chat completions, completions, embeddings endpoints) using FastAPI. Supports streaming responses via Server-Sent Events (SSE), tool calling with structured function schemas, and JSON schema-based output constraints. The API server handles request parsing, response formatting, and error handling while delegating inference to the underlying AsyncLLMEngine, enabling drop-in replacement for OpenAI API clients.

Maintains a model registry that maps model names to architecture classes, automatically detecting model architecture from HuggingFace config.json files. The registry supports custom model registration via plugins, allowing users to add new architectures without modifying vLLM source code. Architecture detection uses configuration patterns (model_type, hidden_size, num_attention_heads) to instantiate the correct model class, and the system supports loading models from HuggingFace Hub, local paths, or custom sources.

Supports multiple quantization methods (FP8, INT8, INT4, GPTQ, AWQ) that reduce model size and memory bandwidth requirements. FP8 quantization uses per-token or per-channel scaling factors computed during model loading, and inference applies dequantization in the forward pass. The quantization backend is selected based on GPU architecture and model type, with specialized kernels for different quantization schemes. Quantized models achieve 2-4x memory reduction and 1.5-2x speedup compared to FP16 inference.

Implements speculative decoding where a smaller draft model generates candidate tokens, and the main model verifies them in parallel. If verification succeeds, multiple tokens are accepted in a single forward pass; if it fails, the draft token is rejected and the main model generates the correct token. This technique reduces the number of main model forward passes by 2-4x while maintaining identical output distribution. The draft model is typically a smaller version of the main model or a different architecture optimized for speed.

Manages Low-Rank Adaptation (LoRA) adapters that can be dynamically loaded and unloaded without reloading the base model. LoRA adapters are stored as low-rank weight matrices that are applied during inference via efficient matrix operations. The system tracks active adapters per request, allowing different requests to use different adapters simultaneously. Adapters are loaded on-demand and cached in GPU memory, with automatic eviction when memory is needed for other requests.

Automatically selects the optimal attention implementation (FlashAttention, FlashInfer, or standard attention) based on GPU architecture, model architecture, and sequence length. FlashAttention reduces memory bandwidth by computing attention in-place without materializing the full attention matrix, while FlashInfer provides further optimizations for inference workloads. The system detects GPU capabilities (compute capability, memory bandwidth) and model characteristics (attention head size, sequence length) to choose the best backend, with fallback to standard attention if specialized backends are unavailable.

Collects comprehensive metrics on inference performance including request latency, throughput, GPU utilization, memory usage, and cache hit rates. Metrics are aggregated in real-time and exposed via a metrics endpoint compatible with Prometheus monitoring systems. The system tracks per-request metrics (time to first token, generation latency) and system-level metrics (batch size, KV cache utilization, speculative decoding acceptance rate). Metrics are collected with minimal overhead (~1-2% performance impact) through efficient sampling and aggregation.