Agent skill · software engineering · davila7
tensorrt-llm
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
Why this skill is useful
Adds executable scripts and advanced optimization techniques for LLM inference on NVIDIA GPUs that are not commonly known or documented.
What it needs
Requires docker, torch installed locally. About 2k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
TensorRT-LLM NVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs. When to use TensorRT-LLM Use TensorRT-LLM when: Deploying on NVIDIA GPUs (A100, H100, GB200) Need maximum throughput (24,000+ tokens/sec on Llama 3) Require low latency for real-time applications Working with quantized models (FP8, INT4, FP4) Scaling across multiple GPUs or nodes Use vLLM instead when: Need simpler setup and Python-first API Want PagedAttention without TensorRT compilation Working with AMD GPUs or non-NVIDIA hardware Use llama.cpp instead when: Deploying on CPU or Apple Silicon Need edge deployment without NVIDIA GPUs Want simpler GGUF quantization format Quick start Installation Basic inference Serving with trtllm-serve Key features Performance optimizations In-flight batching: Dynamic batching during generation Paged KV cache: Efficient memory management Flash Attention: Optimized attention kernels Quantization: FP8, INT4, FP4 for 2-4× faster inference CUDA graphs: Reduced kernel launch overhead Parallelism Tensor parallelism (TP): Split model across GPUs Pipeline parallelism (PP): Layer-wise distribution Expert parallelism: For Mixture-of-Experts models Multi-node: Scale beyond single machine Advanced features Speculative decoding: Faster generation with draft models LoRA serving: Efficient multi-adapter deployment Disaggregated serving: Separate prefill and generation Common patterns Quantized model (FP8) Multi-GPU deployment Batch inference Performance benchmarks Meta Llama 3-8B (H100 GPU): Throughput: 24,000 tokens/sec Latency: ~10ms per token vs PyTorch: 100× faster Llama 3-70B (8× A100 80GB): FP8 quantization: 2× faster than FP16 Memory: 50% reduction with FP8 Supported models LLaMA family: Llama 2, Llama 3, CodeLlama GPT family: GPT-2, GPT-J, GPT-NeoX Qwen: Qwen, Qwen2, QwQ DeepSeek: DeepSeek-V2, DeepSeek-V3 Mixtral: Mixtral-8x7B, Mixtral-8x22B Vision: LLaVA, Phi-3-vision 100+ models on HuggingFace References Optimization …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/inference-serving-tensorrt-llm