vLLM is the gold standard for high-throughput LLM inference in production. Developed at UC Berkeley and backed by a16z, it introduced PagedAttention - a memory management technique inspired by operating systems that reduces memory waste from 60-80% to near zero. This means you can serve 2-4x more concurrent requests on the same hardware. vLLM powers inference at companies like Anyscale, Replicate, and many LLM API providers. If you're self-hosting LLMs at scale, vLLM is likely your best choice.
Key Statistics
2-4x
More concurrent requests vs naive serving
Source: vLLM Paper
60-80%
Memory waste eliminated by PagedAttention
Source: vLLM Paper
35K+
GitHub stars
Source: GitHub Jan 2026
24x
Higher throughput than HuggingFace TGI
Source: vLLM Benchmarks
Pros and Cons
✓ Pros
- • Best-in-class throughput for LLM serving
- • OpenAI-compatible API out of the box
- • Active development with frequent releases
- • Supports most popular model architectures
- • Great documentation and community
✗ Cons
- • Requires capable GPU hardware
- • More complex setup than Ollama
- • Some models not yet supported
- • Resource-intensive compared to inference-only solutions
PagedAttention Explained
Traditional LLM inference allocates memory contiguously for each request, leading to massive fragmentation. A 128K context request might allocate memory for the full context even when using only 1K tokens. PagedAttention divides key-value cache into fixed-size blocks that can be allocated non-contiguously, just like virtual memory in operating systems. This eliminates waste and enables features like prefix caching and beam search that would be prohibitively expensive otherwise.
- → Reduces KV cache memory waste from 60-80% to near zero
- → Enables 2-4x more concurrent requests on same hardware
- → Allows dynamic memory allocation as context grows
- → Powers advanced features like prefix caching and speculative decoding
Continuous Batching
Instead of waiting for a batch to complete before starting new requests (static batching), vLLM processes requests as they arrive and completes (continuous batching). When one request finishes, its slot immediately becomes available for a new request. This dramatically improves throughput and latency, especially with variable-length requests.
- → Requests enter and exit batch dynamically
- → No waiting for slowest request in batch
- → Maximizes GPU utilization at all times
- → Critical for production API serving
Speculative Decoding
vLLM supports speculative decoding - using a small 'draft' model to generate candidate tokens that the large model then verifies in parallel. If the draft model predicts correctly (often 70-80% of tokens), you get those tokens essentially free. This can 2x throughput for some workloads with no quality loss.
- → Draft model generates candidate tokens
- → Large model verifies candidates in parallel
- → 2x speedup possible with no quality degradation
- → Works best with similar model architectures
Key Features
vllm offers these core capabilities:
- → PagedAttention for near-zero memory waste
- → Continuous batching for maximum throughput
- → OpenAI-compatible API server built-in
- → Speculative decoding for faster generation
- → Tensor parallelism for multi-GPU serving
- → Prefix caching for repeated prompts
- → Support for NVIDIA, AMD, and TPU
- → Quantization support (AWQ, GPTQ, SqueezeLLM)
Use Cases
Here are the most common ways people use vllm:
- → Production LLM API serving at scale
- → Self-hosted OpenAI-compatible endpoints
- → Cost optimization vs cloud API pricing
- → Private deployments with sensitive data
- → Batch inference for large datasets
- → Research requiring custom model serving
Getting Started
Follow these steps to set up vllm:
- → Install: pip install vllm
- → Start server: python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3-8b
- → Server runs on port 8000 by default
- → Use OpenAI SDK with base_url='http://localhost:8000/v1'
- → For multi-GPU: add --tensor-parallel-size N
- → For production: use docker image vllm/vllm-openai
Official Resources
Key Takeaways
- → Best-in-class throughput for LLM serving
- → OpenAI-compatible API out of the box
- → Active development with frequent releases
- → Supports most popular model architectures
Related Searches
Frequently Asked Questions
vLLM vs Ollama - which should I use?
Ollama is easier for local development and experimentation. vLLM is better for production serving at scale with high throughput requirements. Use Ollama for personal use, vLLM for APIs serving many users.
vLLM vs TGI (Text Generation Inference)?
vLLM typically achieves higher throughput due to PagedAttention. TGI is well-integrated with HuggingFace ecosystem. Both are production-ready; vLLM usually wins on raw performance.
What hardware do I need for vLLM?
For a 7B model, 16GB+ VRAM (RTX 4090 or A10). For 70B models, you need multi-GPU setups with 80GB+ total VRAM (A100 or H100). Quantization can reduce requirements significantly.
Can I use vLLM with OpenClaw?
Yes. Configure OpenClaw to use your vLLM server as an OpenAI-compatible endpoint. Set the base URL to your vLLM server address and use the model name you served.
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