Private vLLM inference for your product

Implementation experience

I implemented an inference API with model access scoped by organisation and key, worker availability checks and streamed responses. Usage and latency records are kept separate from model input.

The transport waits when the client is slow to receive data. This limits response buffering and accounts for client behaviour in service operations.

The challenge

An API responding in an initial test does not prove it can handle your product's workload. Context length, concurrent requests and attention cache change memory use.

Approach

• Assess the model, available infrastructure and product requirements.
• Size context, concurrency and memory using a representative workload.
• Integrate the API with controlled access, identified versions and monitoring.

Consulting scope

The proposal can include deployment, integration, capacity criteria, updates and recovery, with documentation for the team.

Measurements cover queueing, time to first token and behaviour under concurrency. Hardware, models and traffic determine sizing.

Application in your company

Suitable for companies that need to operate models on owned or rented infrastructure, connect inference to a product and assign responsibility for access, updates, observability and recovery.