vLLM

Implements virtual memory-style paging for KV cache tensors, allocating fixed-size blocks (pages) that can be reused across requests without contiguous memory constraints. Uses a block manager that tracks physical-to-logical page mappings, enabling efficient memory fragmentation reduction and dynamic batching of requests with varying sequence lengths. Reduces memory overhead by 20-40% compared to contiguous allocation while maintaining full sequence context.

Implements a scheduler (Scheduler class) that dynamically groups incoming requests into batches at token-generation granularity rather than request granularity, allowing new requests to join mid-batch and completed requests to exit without stalling the pipeline. Uses a priority queue and state machine to track request lifecycle (waiting → running → finished), with configurable scheduling policies (FCFS, priority-based) and preemption strategies for SLA enforcement.

Tracks request state through a finite state machine (waiting → running → finished) with detailed metrics at each stage. Maintains request metadata (prompt, sampling params, priority) in InputBatch objects, handles request preemption and resumption for SLA enforcement, and provides hooks for custom request processing. Integrates with scheduler to coordinate request transitions and resource allocation.

Maintains a registry of supported model architectures (LLaMA, Qwen, Mistral, etc.) with automatic detection based on model config.json. Loads model-specific optimizations (e.g., fused attention kernels, custom sampling) without user configuration. Supports dynamic registration of new architectures via plugin system, enabling community contributions without core changes.

Collects detailed inference metrics (throughput, latency, cache hit rate, GPU utilization) via instrumentation points throughout the inference pipeline. Exposes metrics via Prometheus-compatible endpoint (/metrics) for integration with monitoring stacks (Prometheus, Grafana). Tracks per-request metrics (TTFT, inter-token latency) and aggregate metrics (batch size, queue depth) for performance analysis.

Processes multiple prompts in a single batch without streaming, optimizing for throughput over latency. Loads entire batch into GPU memory, generates completions for all prompts in parallel, and returns results as batch. Supports offline mode for non-interactive workloads (e.g., batch scoring, dataset annotation) with higher batch sizes than streaming mode.

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 sharding strategies (row/column parallelism for linear layers, spatial parallelism for attention). Coordinates execution via AllReduce and AllGather collective operations through NCCL backend, with automatic communication scheduling to overlap computation and communication. Supports both intra-node (NVLink) and inter-node (Ethernet) topologies with topology-aware optimization.

Caches KV cache blocks for repeated prompt prefixes across requests, using hash-based prefix matching to identify reusable blocks without recomputation. Maintains a prefix tree (trie) of cached prefixes with reference counting for garbage collection, enabling zero-copy sharing of KV cache pages between requests with common prompt prefixes (e.g., system prompts, few-shot examples).

Accelerates token generation by running a small draft model (e.g., 7B) to speculatively generate k tokens, then verifying them in parallel with the target model using batch verification. Accepts speculative tokens if they match the target model's output, otherwise rejects and resamples from the target. Reduces effective latency per token by 1.5-2.5x for compatible model pairs without sacrificing output quality.

Processes multi-modal inputs (images, videos, audio) by routing them through specialized encoders (CLIP, Qwen-VL, LLaVA) before concatenating embeddings with text tokens. Handles variable-resolution images via dynamic patching, supports batch processing of mixed image/text sequences, and manages encoder caching to avoid redundant vision encoding. Integrates with the main token generation pipeline via embedding concatenation.

Reduces model precision from FP32/FP16 to FP8 or INT8 using post-training quantization (PTQ) or quantization-aware training (QAT), with per-channel or per-token scaling to minimize accuracy loss. Implements fused quantization kernels that perform dequantization and computation in a single GPU kernel, reducing memory bandwidth by 4-8x. Supports mixed-precision (quantize weights, keep activations at higher precision) for critical layers.

Exposes vLLM inference engine via OpenAI-compatible HTTP API endpoints (/v1/completions, /v1/chat/completions) with streaming response support via Server-Sent Events (SSE). Handles request parsing, validation, and response formatting to match OpenAI API contracts, enabling drop-in replacement for OpenAI clients. Includes built-in request queuing, timeout handling, and error recovery with configurable concurrency limits.

Manages Low-Rank Adaptation (LoRA) adapters as pluggable modules that can be loaded/unloaded at runtime without reloading base model weights. Maintains a registry of available adapters, handles adapter weight merging into base model weights during inference, and supports multi-adapter inference by routing requests to appropriate adapter. Enables efficient fine-tuning and personalization without full model retraining.

Enables models to call external tools by constraining token generation to valid function signatures defined via JSON schema. Uses guided decoding (constrained beam search) to enforce schema compliance at generation time, preventing invalid JSON or missing required fields. Integrates with OpenAI-compatible API via tool_choice parameter, automatically parsing and validating tool calls before returning to client.