Tag: TensorRT-LLM

3 articles

• KV Cache Quantization in Production: Reducing Long-Context Inference Memory with FP8/INT8

  A systematic guide to KV cache quantization for long-context LLM inference: value, principles, vLLM/TensorRT-LLM implementation, evaluation metrics, and production risks to help teams safely reduce memory and latency costs.

  07/01/2026 LLM / Inference Optimization / KV Cache / Quantization / LLMOps / vLLM / TensorRT-LLM

• Speculative Decoding in Production: Why Low QPS Speeds Up but High Load Slows Down

  A production-oriented analysis of speculative decoding's benefit boundaries, covering draft models, verification, acceptance rate, request load, and batching, with a pre-deployment checklist.

  06/29/2026 LLM Inference / Speculative Decoding / vLLM / TensorRT-LLM / Performance Optimization

• LLM Quantization in Practice: FP8, INT4, AWQ, and Production Inference Trade-offs

  A systematic guide to LLM inference quantization: FP8, INT4, AWQ, GPTQ, KV Cache quantization, benefits, risks, selection methods, and a go-live checklist for production engineers.

  06/29/2026 LLM Inference / Quantization / vLLM / TensorRT-LLM / Model Serving