Serving runtime · Fast-moving · Advanced
vLLM (2)
A high-throughput inference server for open-weight language models, built around efficient KV cache management.
What vLLM (2) is
vLLM raises serving throughput substantially by paging the attention cache and batching requests continuously rather than in fixed groups.
How it works
It exposes an OpenAI-compatible API, supports tensor parallelism across GPUs, quantised weights and structured output constraints.
Why it matters
It is the common default for self-hosting open models at production volume.
Common uses
- →Self-hosted model APIs
- →High-concurrency inference
- →Cost-optimised deployments
Strengths
- ✓Large throughput gains
- ✓Drop-in API compatibility
Watch for
- ✓GPU memory tuning required
- ✓Operational responsibility stays with you
Continue exploring
More in this collection
Browse all AI Technology