vLLM is the high-throughput, memory-efficient inference and serving engine for large language models that has become the industry standard for LLM deployment. With over 72,800 GitHub stars and 14,200 forks, vLLM powers production inference at companies ranging from startups to hyperscalers. Originally developed at UC Berkeley's Sky Computing Lab, the project has grown into a community-driven ecosystem trusted by NVIDIA, AMD, Intel, Google Cloud, Microsoft Azure, AWS, and hundreds of AI companies worldwide. ## Why vLLM Matters Serving large language models efficiently is one of the hardest infrastructure challenges in AI. A single LLM inference request requires loading billions of parameters into GPU memory, computing attention over potentially hundreds of thousands of tokens, and managing key-value caches that grow linearly with sequence length. Naive implementations waste enormous amounts of GPU memory on fragmented KV caches, leading to low throughput and high costs. vLLM solved this problem with PagedAttention, a memory management technique inspired by operating system virtual memory. By treating the KV cache as pages that can be allocated, freed, and shared on demand, vLLM eliminates memory fragmentation and enables near-optimal GPU utilization. The result is 2-4x higher throughput compared to naive serving implementations, with the same model quality. ## Core Architecture and How It Works ### PagedAttention: The Foundation PagedAttention is vLLM's signature innovation. In traditional LLM serving, each request pre-allocates a contiguous block of GPU memory for its KV cache based on the maximum possible sequence length. This leads to massive waste — a request that generates only 100 tokens still reserves memory for thousands. PagedAttention divides the KV cache into fixed-size pages (blocks) that are allocated on demand as the sequence grows. Pages from different requests can be stored non-contiguously in GPU memory, and completed pages can be immediately freed. This approach typically recovers 60-80% of the memory wasted by traditional systems. ### Continuous Batching vLLM uses continuous (or iteration-level) batching to maximize GPU utilization. Instead of waiting for all requests in a batch to finish before starting new ones, vLLM inserts new requests into the batch as soon as any request completes. This eliminates the "batch bubbles" that plague static batching systems, where the GPU sits idle waiting for the slowest request. Combined with PagedAttention, continuous batching enables vLLM to serve thousands of concurrent requests with minimal latency degradation. ### Speculative Decoding For latency-sensitive applications, vLLM supports speculative decoding — using a smaller, faster draft model to predict multiple tokens ahead, then verifying them with the full model in a single forward pass. When the draft model's predictions are correct (which happens frequently for common patterns), this technique delivers 2-3x lower latency without any change in output quality. ### Distributed Inference vLLM supports four parallelism strategies for models that exceed single-GPU memory: tensor parallelism (splitting model layers across GPUs), pipeline parallelism (assigning different layers to different GPUs), data parallelism (replicating the model across GPU groups), and expert parallelism for Mixture-of-Experts architectures like DeepSeek-V3 and Mixtral. The distributed backend uses NCCL for efficient GPU-to-GPU communication. ## Key Features ### OpenAI-Compatible API Server vLLM ships with a built-in API server that implements the OpenAI Chat Completions and Completions APIs. This means any application using the OpenAI SDK can switch to a self-hosted vLLM backend by changing a single URL — no code changes required. The server supports streaming, function calling, structured output, and vision inputs for multimodal models. ### Broad Model Support The engine supports virtually every popular model architecture: Llama, Mistral, Qwen, DeepSeek, GPT-NeoX, Falcon, Phi, Gemma, Command-R, and dozens more. It also supports multimodal models like LLaVA and embedding models for retrieval applications. New model architectures are typically supported within days of their release. ### Quantization and Efficiency vLLM supports GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 quantization methods, enabling deployment of large models on consumer-grade hardware. Quantized models maintain most of their quality while requiring 2-4x less GPU memory, making it possible to serve a 70B parameter model on a single GPU. ### Multi-Hardware Support Beyond NVIDIA GPUs, vLLM runs on AMD MI200/MI250/MI300 GPUs via ROCm, Google TPUs, Intel Gaudi accelerators, AWS Inferentia, IBM Spyre, Huawei Ascend NPUs, and even CPUs (Intel, ARM, PowerPC). This hardware diversity is unmatched by any competing inference engine. ## Practical Applications vLLM is the backbone of LLM inference at scale. Companies use it to serve chatbots, coding assistants, document analysis tools, and search engines. Cloud providers offer vLLM as a managed service for customers who want to self-host models. Research labs use it to run evaluations and benchmarks efficiently. The OpenAI-compatible API makes it the default choice for teams migrating from proprietary APIs to self-hosted models. ## Limitations - Initial setup requires CUDA toolkit installation and GPU driver configuration - Memory requirements for large models remain substantial even with PagedAttention - Some advanced features (speculative decoding, expert parallelism) require careful tuning - Documentation can lag behind the rapid development pace - AMD and non-NVIDIA hardware support, while functional, has fewer optimizations ## Who Should Use It vLLM is essential for any team deploying LLMs in production. Whether you are serving a 7B model on a single GPU or a 405B model across a cluster, vLLM provides the throughput, memory efficiency, and API compatibility needed for real-world deployment. It is particularly valuable for organizations that want to reduce inference costs, maintain data privacy with self-hosted models, or serve multiple models from a shared GPU pool.