vLLM

PagedAttention is the foundational algorithm in vLLM that manages the KV cache using virtual memory principles. Instead of allocating a large, contiguous block of memory per sequence, it partitions the cache into fixed-size blocks that can be mapped non-contiguously in physical GPU memory.

• Zero fragmentation: Eliminates internal and external memory fragmentation common in traditional KV cache allocators
• Memory sharing: Identical prompt prefixes across requests share the same physical KV cache blocks, dramatically reducing memory duplication in beam search and parallel sampling
• Dynamic allocation: Blocks are allocated on-demand as sequences generate new tokens, rather than pre-allocating maximum context length

This approach enables 2-4x higher batch sizes compared to traditional serving systems like Hugging Face Transformers.

Continuous batching, also called iteration-level scheduling, eliminates the straggler problem inherent in static batching. Traditional batching waits for every sequence in a batch to complete before accepting new requests. vLLM instead evicts finished sequences and inserts new ones at every forward pass iteration.

• No idle time: GPU compute units are never waiting for a single long sequence to finish generating
• Dynamic batch composition: Batch size fluctuates naturally based on request completion rates
• Preemption support: Long-running sequences can be temporarily swapped to CPU memory and resumed later, preventing head-of-line blocking

This technique is particularly impactful for workloads with highly variable output lengths, where static batching would leave significant GPU capacity unused.

vLLM supports tensor model parallelism to serve models that exceed the memory capacity of a single GPU. It shards the weight matrices of each transformer layer across multiple GPUs, performing collective communication operations during the forward pass.

• Megatron-LM style sharding: Splits attention heads and feed-forward dimensions across devices
• NCCL-based communication: Uses high-bandwidth GPU interconnects for all-reduce and all-gather operations
• Seamless scaling: The same serving code works for single-GPU and multi-GPU deployments without model modification

This enables serving models like Llama 2 70B or Mixtral 8x7B across multiple A100 or H100 GPUs with near-linear throughput scaling.

vLLM implements automatic prefix caching that detects when multiple requests share a common prompt prefix and reuses the computed KV cache blocks. This is critical for applications with system prompts, few-shot examples, or conversational context.

• Hash-based detection: Uses a rolling hash of token sequences to identify matching prefixes
• Copy-on-write semantics: When a sequence diverges from the shared prefix, only the divergent blocks are copied
• Cross-request reuse: Cached blocks persist across requests, benefiting long-running serving instances

For chatbot applications where every request includes the same lengthy system prompt, prefix caching can reduce time-to-first-token by 50-90%.

vLLM integrates multiple quantization backends and custom CUDA kernels to maximize throughput on NVIDIA hardware. It supports AWQ, GPTQ, and SqueezeLLM weight-only quantization methods that compress model weights to 4-bit integers.

• FlashAttention-2 integration: Uses the IO-aware exact attention kernel for optimal GPU memory bandwidth utilization
• Fused MoE kernels: Custom kernels for Mixture-of-Experts models that reduce kernel launch overhead
• FP8 inference: Support for 8-bit floating point on H100 GPUs with native transformer engine acceleration

These optimizations enable serving a 13B parameter model on a single consumer GPU or achieving maximum throughput on datacenter hardware.

An advanced iteration of dynamic batching where completed sequences are immediately evicted from the GPU batch and new requests are inserted without waiting for all sequences in the batch to finish. This eliminates idle compute time inherent in static batching. vLLM implements this natively, dynamically adjusting batch composition on every forward pass to maximize GPU utilization and throughput for autoregressive generation.

A memory mechanism in transformer-based generative models that stores previously computed Key and Value tensors for each token. During autoregressive decoding, the model reuses this cache instead of recomputing attention for all preceding tokens. Without efficient management, the KV cache causes memory fragmentation and over-allocation. vLLM's PagedAttention solves this by managing KV cache in fixed-size blocks, analogous to virtual memory paging in operating systems.

The core innovation within vLLM that manages KV cache memory using fixed-size blocks rather than contiguous allocation. Key properties:

• Zero fragmentation: Blocks can be allocated non-contiguously
• Memory sharing: Blocks can be shared across sequences for techniques like beam search and parallel sampling
• Near-optimal utilization: Reduces wasted memory to under 4% This approach enables vLLM to achieve up to 24x higher throughput than traditional serving systems like Hugging Face Transformers.

A latency-reduction technique where a small, fast draft model predicts multiple future tokens, which are then verified in parallel by the larger target model. If the draft tokens are correct, they are accepted in a single forward pass, dramatically accelerating generation. vLLM supports speculative decoding natively, leveraging its efficient memory management to handle the parallel verification step without KV cache conflicts between the draft and target models.

A model parallelism strategy where individual weight matrices are sharded across multiple GPUs, and collective communication operations synchronize partial results during each forward pass. vLLM integrates Megatron-LM style tensor parallelism to serve models too large for a single accelerator. This distributes both computation and the KV cache across devices, enabling inference on models like Llama 3 70B and Mixtral 8x22B on multi-GPU nodes.

A model optimization technique that reduces numerical precision of weights and activations from FP16/BF16 to lower-bit formats like INT4, INT8, or FP8. vLLM supports multiple quantization backends:

• AWQ: Activation-aware weight quantization
• GPTQ: Post-training quantization with calibration data
• FP8: Native 8-bit floating point on H100 GPUs Quantization reduces GPU memory footprint by 2-4x, allowing larger models or higher batch sizes on the same hardware with minimal accuracy degradation.