Tensorrt Llm
CheckedProvides LLMs guidance and assistance for building AI and machine learning applications.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
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terminal$ npx skills add davila7/claude-code-templates --skill tensorrt-llm -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davila7
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Install target
Live repository
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Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
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Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
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Pinned to a commit: passed
Scanned davila7/claude-code-templates at 79182c51fb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 79182c51fb
- Scanned
- 4 files, 1,097 lines
About this skill
What it does
This skill enables AI agents to deploy and manage Large Language Models using NVIDIA TensorRT-LLM, a high-performance inference engine. It optimizes model execution through quantization, kernel fusion, and static batching to drastically reduce latency on GPU hardware.
When to use it
- Deploying open-source LLMs like Llama or Mistral in production environments requiring low-latency responses.
- Optimizing inference throughput for high-concurrency chatbot applications or API services.
- Reducing memory footprint and computational cost by applying dynamic quantization techniques.
- Serving models on NVIDIA GPUs to leverage hardware-specific accelerations not available in standard Python runtimes.
Key capabilities
- Model Optimization: Automatically applies quantization (INT8, FP16) to reduce model size and speed up inference.
- Kernel Fusion: Combines multiple operations into single GPU kernels to minimize overhead.
- Static Batching: Groups incoming requests to maximize GPU utilization during inference.
- Engine Building: Compiles PyTorch models into optimized TensorRT engines for deployment.
Example prompts
- "Build a TensorRT-LLM engine from the Llama-2-7b model using FP16 precision and configure it for 8-GPU parallel serving."
- "Optimize this Hugging Face model by applying INT4 quantization and generate an inference engine compatible with A100 GPUs."
- "Set up a static batching configuration for the Mistral-7B model to handle 50 concurrent requests with minimal latency."
Tips & gotchas
Ensure your environment has compatible NVIDIA drivers and CUDA versions installed before attempting to build engines. Quantization can significantly improve speed but may slightly reduce output quality, so test different precision levels (INT8 vs FP16) based on your accuracy requirements.