vLLM: The High-Throughput Engine Behind Production Inference
PagedAttention and continuous batching make vLLM the default choice for serving open models at scale. Here is the why.
If Ollama is for your laptop, vLLM is for your fleet. It is built to squeeze maximum throughput out of expensive GPUs.
The core ideas
- PagedAttention — memory-efficient KV cache management.
- Continuous batching — keeps the GPU busy across requests.
- Compatible APIs — OpenAI-style endpoints ease migration.
When to reach for it
Once you are serving real traffic, vLLM’s throughput gains translate directly into lower cost per token. It is the workhorse of self-hosted inference.
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