serving-llms-vllm

Serve LLMs with high throughput by deploying an inference endpoint using vLLM, which utilizes PagedAttention and continuous batching to increase tokens/sec for multi-user workloads. This configuration handles high concurrency reliably in real applications.

Yes, you can fit larger LLMs in limited VRAM by applying memory-efficient model scaling techniques. vLLM supports tensor parallelism alongside quantization methods like AWQ, GPTQ, and FP8 to reduce memory footprint during deployment.

Deploy an OpenAI-compatible API by running a vLLM server configured on port 8000 with GPU memory utilization set to 0.9. This allows clients to immediately start sending requests for chat and completions style integrations.

Validate vLLM server performance by measuring Time To First Token (TTFT) and requests per second (req/sec) metrics. These metrics help tune throughput and latency settings for your specific production inference workload.

Yes, vLLM supports offline batch inference in addition to real-time API serving. The inference endpoint applies to both API serving and offline batch processing, allowing flexible deployment across different traffic patterns.

Yes, you can optionally enable Prometheus monitoring for your vLLM inference endpoint. This allows you to track production traffic metrics and validate ongoing performance alongside the OpenAI-compatible server configuration.