Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/liortesta/clawdagent/tensorrt-llmnpx skills add liortesta/ClawdAgent --skill tensorrt-llmgit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/liortesta/clawdagent/tensorrt-llm)<a href="https://agentmods.dev/skills/liortesta/clawdagent/tensorrt-llm"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/tensorrt-llm.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00075 | $0.01453 |
| Opus 5 | $0.00037 | $0.00727 |
| Sonnet 5 | $0.00015 | $0.00291 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
tensorrt-llm scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:8000/v1/chat/completions \ This is a copy
84% identical to tensorrt-llm — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
# Docker (recommended)
docker pull nvidia/tensorrt_llm:latest
# pip install
pip install tensorrt_llm==1.2.0rc3
# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12
Basic inference
from tensorrt_llm import LLM, SamplingParams
# Initialize model
llm = LLM(model="meta-llama/Meta-Llama-3-8B")
# Configure sampling
sampling_params = SamplingParams(
max_tokens=100,
temperature=0.7,
top_p=0.9
)
# Generate
prompts = ["Explain quantum computing"]
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
print(output.text)
Serving with trtllm-serve
# Start server (automatic model download and compilation)
trtllm-serve meta-llama/Meta-Llama-3-8B \
--tp_size 4 \ # Tensor parallelism (4 GPUs)
--max_batch_size 256 \
--max_num_tokens 4096
# Client request
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Meta-Llama-3-8B",
"messages": [{"role": "user", "content": "Hello!"}],
"temperature": 0.7,
"max_tokens": 100
}'
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
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 188 lines · 75 tokens per session scan A 13f8e09b3e05
tensorrt-llm is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 8d ago), licensed Apache-2.0. It adds 75 tokens to every session and 1,453 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to tensorrt-llm, differing in 24 lines, and is treated as a copy.
Other skills, from other repositories
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.
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.
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.
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.
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.
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.