vLLM

The core innovation of vLLM. PagedAttention manages the key-value (KV) cache in non-contiguous blocks, analogous to virtual memory paging in operating systems.

• Zero fragmentation: Eliminates internal and external memory waste common in traditional contiguous KV cache allocation.
• Dynamic block mapping: KV cache blocks can be shared across multiple sequences, enabling techniques like beam search and parallel sampling with near-zero memory overhead.
• Near-optimal memory utilization: Achieves up to 4x higher batch sizes compared to frameworks like Hugging Face Transformers (HF) by packing more sequences into available GPU memory.

This approach solves the fundamental memory fragmentation problem that previously limited LLM serving throughput.

Unlike static batching which waits for all sequences in a batch to complete before accepting new requests, vLLM implements iteration-level scheduling.

• Dynamic insertion: New requests join the running batch at every forward pass iteration, not just at batch boundaries.
• Early eviction: Completed sequences immediately release their KV cache blocks back to the pool, freeing memory for incoming requests.
• Throughput gains: This eliminates the 'padding waste' of static batching where short sequences wait for long ones, resulting in 10x+ throughput improvements under high load.

The scheduler operates at the granularity of individual transformer iterations, maximizing GPU utilization moment-to-moment.

vLLM is engineered for maximum tokens-per-second across concurrent users, making it the de facto standard for production LLM serving.

• Up to 24x higher throughput than Hugging Face Transformers and up to 3.5x higher than NVIDIA FasterTransformer, as measured in the original research paper.
• Preemption support: Implements both swapping (offloading KV cache blocks to CPU RAM) and recomputation strategies to handle memory pressure gracefully without crashing.
• Tensor parallelism: Distributes model weights across multiple GPUs for serving models that exceed single-GPU memory capacity.

These capabilities make vLLM suitable for serving models from 7B to 405B parameters in production environments.

vLLM ships with a drop-in compatible HTTP server that implements the OpenAI Completions and Chat Completions API specifications.

• Seamless migration: Any application written for the OpenAI API can switch to a self-hosted vLLM endpoint by changing only the base_url.
• Streaming support: Full support for server-sent events (SSE) token streaming, enabling real-time text generation in chat interfaces.
• Multi-model serving: A single vLLM instance can serve multiple LoRA adapters or different models, selected per-request via the model parameter.

This compatibility layer eliminates the need for custom client libraries and accelerates adoption in existing LLM application stacks.

vLLM integrates multiple weight quantization methods to reduce memory footprint and accelerate inference on diverse hardware backends.

• AWQ and GPTQ: Supports Activation-aware Weight Quantization and GPTQ for 4-bit integer weight compression with minimal accuracy loss.
• FP8 inference: Native support for 8-bit floating point on NVIDIA H100 GPUs, leveraging hardware acceleration via the Transformer Engine.
• AMD ROCm and Intel Gaudi: Extends beyond NVIDIA CUDA to support AMD Instinct GPUs and Intel Gaudi accelerators, enabling sovereign infrastructure deployments on non-NVIDIA hardware.
• CPU offloading: Optional KV cache offloading to system RAM for serving large models on resource-constrained GPU nodes.

Advanced optimization techniques that further reduce latency and computational cost for specific serving patterns.

• Automatic prefix caching: Identical prompt prefixes across requests (common in RAG and chatbot applications) share KV cache blocks, eliminating redundant computation. A system prompt processed once is reused for all subsequent turns.
• Speculative decoding: Uses a small 'draft' model to predict multiple future tokens, which the main model then verifies in parallel. This can achieve 2-3x faster generation without quality degradation.
• Chunked prefill: Splits long prompt processing into smaller chunks interleaved with decode steps, preventing long prompts from starving other requests of compute time.

These features target the specific bottlenecks encountered in production LLM serving at scale.

The core memory management algorithm that enables vLLM's high throughput. It manages the Key-Value (KV) cache in non-contiguous blocks, analogous to virtual memory paging in operating systems.

• Eliminates memory fragmentation by allocating KV cache in fixed-size blocks
• Allows blocks to be shared across sequences, enabling techniques like prefix caching
• Reduces memory waste from 60-80% in traditional systems to under 4%
• Enables continuous batching by dynamically mapping logical to physical KV cache blocks

An iteration-level scheduling technique that dynamically adds new requests to a running batch as soon as previous sequences complete, rather than waiting for the entire batch to finish.

• Contrasts with static batching, which blocks until all sequences in a batch are complete
• Achieves 10x or higher throughput compared to static batching under variable request loads
• Works by splicing new token generation into the GPU's forward pass at each iteration
• Requires fine-grained KV cache management, which PagedAttention provides

A compression technique that reduces the memory footprint of stored attention keys and values by representing them in lower-precision formats.

• vLLM supports FP8 KV cache via just-in-time quantization on NVIDIA H100/H200 GPUs
• Reduces KV cache memory by 2x with negligible accuracy loss
• Enables larger batch sizes and longer context lengths on the same hardware
• Works in tandem with PagedAttention to maximize effective memory capacity

An optimization that automatically detects and reuses KV cache blocks from previously processed sequences when a new request shares a common prefix.

• Critical for multi-turn conversations where the system prompt is identical across requests
• Eliminates redundant computation of the shared prefix, reducing Time to First Token (TTFT)
• vLLM implements this transparently through its block-sharing mechanism in PagedAttention
• Particularly effective for RAG pipelines with long, static system prompts

A latency-reduction technique where a small draft model proposes multiple future tokens, and the target model verifies them in parallel rather than generating one token at a time.

• Reduces per-token generation latency by 2-3x without changing output distribution
• vLLM supports speculative decoding through its modular architecture
• The draft model runs on spare compute or a smaller GPU, while the target model verifies
• Most effective for latency-sensitive applications like chatbots and code completion

A model parallelism strategy that shards individual weight matrices across multiple GPUs, enabling inference on models too large for a single accelerator.

• vLLM supports Megatron-LM style tensor parallelism natively
• Splits each transformer layer's attention and MLP blocks across GPUs
• Requires high-bandwidth interconnects like NVLink or NVSwitch for efficiency
• Enables serving models like Llama 3 405B on a single node with 8 GPUs