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Commits, branches and sync with the but CLI
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Works with
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High-throughput LLM inference on NVIDIA GPUs v1.0.0
Use this resource to serve a large language model at high throughput on NVIDIA GPUs
New
- 461 downloads
- 242 likes
- 247 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install gpu-llm-inference-tensorrtWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent installs TensorRT-LLM, preferably in a Docker container, loads a model, configures sampling and runs inference, then can move to serving or multi-GPU. You get an inference engine optimised for NVIDIA GPUs.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: installation, basic inference procedure and configuration
- references/: multi-GPU parallelism, optimisations and serving
Good fit for
- You serve a model to many requests on NVIDIA GPUs
- You work with models quantised to FP8 or INT4
Not for
Tags
- inference
- gpu
- nvidia
- llm
- throughput
About
You say
“Set up TensorRT-LLM to serve this FP8-quantised model on two H100s.”
Skill published by Orchestra Research under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to serve a large language model at high throughput on NVIDIA GPUs
- Review and adjust the result
Content preview
Files
- LICENSE1 KB
- NOTICE-PALATEKIT.md0.5 KB
- references/multi-gpu.md6.5 KB
- references/optimization.md5.5 KB
- references/serving.md9.6 KB
- SKILL.md4.9 KB
Origin and licence
Listed unmodified
- Author
- Orchestra Research
- Source
- github.com/nousresearch/hermes-agent/tree/0c74cc682dc81bf78b78a1737b3ee77a820ccb79/optional-skills/mlops/tensorrt-llm
- Licence
- MIT
Notice
« tensorrt-llm » (Orchestra Research, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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