vLLM
High-throughput LLM inference engine with PagedAttention — the default choice when you need to squeeze a GPU.
vLLM serves open models with continuous batching and PagedAttention, delivering several times the throughput of naive HuggingFace pipelines on the same card. It exposes an OpenAI-compatible API, so swapping it in front of an existing app is usually a one-line base-url change.
Best for: Self-hosted production inference on NVIDIA/AMD GPUs
Deploy: Self-hostable
What to do about it
- High-throughput LLM inference
- Open-source and self-hosted
- Compatible with OpenAI API
What it is
vLLM is a high-throughput LLM inference engine that serves open models with continuous batching and PagedAttention.
It delivers several times the throughput of naive HuggingFace pipelines on the same card.
vLLM exposes an OpenAI-compatible API, making it easy to swap in front of an existing app with a one-line base-url change.
Where it fits in a deployment stack
LLM inference engines like vLLM solve the problem of efficiently serving large language models in production environments.
They normally sit next to model training pipelines and application servers, taking over the work of processing input and generating responses.
In a production system, these engines are typically responsible for handling incoming requests, processing them, and returning results to the application server.
How to evaluate it
To evaluate vLLM, you need to consider its performance, scalability, and compatibility with your existing infrastructure.
- Measure p95 latency of the retrieval step under 50 concurrent sessions.
- Inspect the output of the engine with a sample dataset to verify accuracy.
- Feed it a large batch of input data to test its throughput.
- Inject failures into the system to test its error handling and recovery.
- Verify the engine's compatibility with your existing model formats and APIs.
- Monitor the engine's resource utilization to ensure it can scale with your workload.
What to verify before you commit
Before committing to vLLM, you need to verify its suitability for your specific use case and ensure it meets your requirements.
- Check the licence terms to ensure they align with your organization's policies.
- Verify how the engine handles sensitive data and ensure it meets your security standards.
- Calculate the operational cost of running the engine and ensure it fits within your budget.
- Test the engine's failure behavior to ensure it meets your availability requirements.
- Evaluate the potential for vendor lock-in and ensure you have a clear exit strategy.
- Review the engine's documentation and support resources to ensure they meet your needs.
Where this fits in the FDE path
- FDE career path — the levels, the titles that map to them, and what changes at each step
- Interview guide — what the loop tests beyond coding
- Pay data — 223 postings — midpoint median $231,000, employer-published ranges only
- 922 live FDE roles — pulled daily from employers’ own job boards