Those who want to run their own models, for cost, privacy or control, need to serve inference efficiently. vLLM is the reference open-source engine for that: high throughput and efficient memory use to serve LLMs, with an OpenAI-compatible API.
What is vLLM?
The core technique is PagedAttention, which manages cache memory efficiently and enables high throughput. It does continuous batching, tensor and pipeline parallelism and supports quantization (FP8, AWQ, GPTQ). Since it exposes an OpenAI-compatible API, it works as a drop-in for many clients.
Key features
- PagedAttention for efficient memory use and high throughput
- Continuous batching and tensor/pipeline parallelism
- FP8, AWQ, GPTQ quantization
- OpenAI-compatible API, drop-in for many clients
How Reche uses it
Self-hosting models makes sense in scenarios of high API cost or privacy requirements. Reche evaluates when it is worth running your own models with vLLM and when a managed API is more efficient, always by the math that works for the client.