vLLM
High-throughput LLM serving with PagedAttention
About
Production-grade LLM inference server. PagedAttention enables high throughput and efficient KV cache memory management.
Choose vLLM when…
- •You're serving LLMs at high throughput in production
- •Continuous batching and PagedAttention are needed
- •You're running your own GPU inference cluster
Builder Slot
LLM providers and inference servers — where the actual model computation happens
Other tools in this slot:
Stack Genome Detection
AIchitect's Genome scanner detects vLLM in your project via these signals:
vllmIntegrates with (10)
LiteLLM connects to a self-hosted vLLM endpoint via its OpenAI-compatible API, treating it as any other provider.
→ Self-hosted GPU inference via vLLM accessible through the same LiteLLM interface as cloud providers — one config for everything.
LlamaIndex connects to a vLLM-hosted endpoint via its OpenAI-compatible API, treating self-hosted vLLM as a generation provider.
→ LlamaIndex RAG pipelines backed by self-hosted GPU inference — enterprise-grade retrieval and generation with full data residency.
Often paired with (1)
Alternatives to consider (2)
Pricing
In 2 stacks
Ruled out by 2 stacks
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