[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71FPD4Y868YCSZ18STK56VH":3,"skill-same:01M71FPD4Y868YCSZ18STK56VH":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":13,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M71FPD4Y868YCSZ18STK56VH","tensorrt-llm",null,"Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.","gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6","gjbvsjjvgm-sketch","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F278511911?v=4","01M71F3JXKZQ70H2KCHHTXKZDA","01M71F3JY8A79Z3NZBW08TF4JD",0,"MIT","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ftensorrt-llm\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"tensorrt","tensorrt llm","latency","when","batching","for","orchestra","you","with","throughput","H100","pytorch","scaling","use","llm inference","Inference Serving","NVIDIA","FP8","Optimization","deployment","2026-11-10 10:12:30",false,"---\nname: tensorrt-llm\ndescription: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [tensorrt-llm, torch]\nmetadata:\n  hermes:\n    tags: [Inference Serving, TensorRT-LLM, NVIDIA, Inference Optimization, High Throughput, Low Latency, Production, FP8, INT4, In-Flight Batching, Multi-GPU]\n\n---\n\n# TensorRT-LLM\n\nNVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.\n\n## When to use TensorRT-LLM\n\n**Use TensorRT-LLM when:**\n- Deploying on NVIDIA GPUs (A100, H100, GB200)\n- Need maximum throughput (24,000+ tokens\u002Fsec on Llama 3)\n- Require low latency for real-time applications\n- Working with quantized models (FP8, INT4, FP4)\n- Scaling across multiple GPUs or nodes\n\n**Use vLLM instead when:**\n- Need simpler setup and Python-first API\n- Want PagedAttention without TensorRT compilation\n- Working with AMD GPUs or non-NVIDIA hardware\n\n**Use llama.cpp instead when:**\n- Deploying on CPU or Apple Silicon\n- Need edge deployment without NVIDIA GPUs\n- Want simpler GGUF quantization format\n\n## Quick start\n\n### Installation\n\n```bash\n# Docker (recommended)\ndocker pull nvidia\u002Ftensorrt_llm:latest\n\n# pip install\npip install tensorrt_llm==1.2.0rc3\n\n# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12\n```\n\n### Basic inference\n\n```python\nfrom tensorrt_llm import LLM, SamplingParams\n\n# Initialize model\nllm = LLM(model=\"meta-llama\u002FMeta-Llama-3-8B\")\n\n# Configure sampling\nsampling_params = SamplingParams(\n    max_tokens=100,\n    temperature=0.7,\n    top_p=0.9\n)\n\n# Generate\nprompts = [\"Explain quantum computing\"]\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    print(output.text)\n```\n\n### Serving with trtllm-serve\n\n```bash\n# Start server (automatic model download and compilation)\ntrtllm-serve meta-llama\u002FMeta-Llama-3-8B \\\n    --tp_size 4 \\              # Tensor parallelism (4 GPUs)\n    --max_batch_size 256 \\\n    --max_num_tokens 4096\n\n# Client request\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FMeta-Llama-3-8B\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Key features\n\n### Performance optimizations\n- **In-flight batching**: Dynamic batching during generation\n- **Paged KV cache**: Efficient memory management\n- **Flash Attention**: Optimized attention kernels\n- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference\n- **CUDA graphs**: Reduced kernel launch overhead\n\n### Parallelism\n- **Tensor parallelism (TP)**: Split model across GPUs\n- **Pipeline parallelism (PP)**: Layer-wise distribution\n- **Expert parallelism**: For Mixture-of-Experts models\n- **Multi-node**: Scale beyond single machine\n\n### Advanced features\n- **Speculative decoding**: Faster generation with draft models\n- **LoRA serving**: Efficient multi-adapter deployment\n- **Disaggregated serving**: Separate prefill and generation\n\n## Common patterns\n\n### Quantized model (FP8)\n\n```python\nfrom tensorrt_llm import LLM\n\n# Load FP8 quantized model (2× faster, 50% memory)\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-70B\",\n    dtype=\"fp8\",\n    max_num_tokens=8192\n)\n\n# Inference same as before\noutputs = llm.generate([\"Summarize this article...\"])\n```\n\n### Multi-GPU deployment\n\n```python\n# Tensor parallelism across 8 GPUs\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-405B\",\n    tensor_parallel_size=8,\n    dtype=\"fp8\"\n)\n```\n\n### Batch inference\n\n```python\n# Process 100 prompts efficiently\nprompts = [f\"Question {i}: ...\" for i in range(100)]\n\noutputs = llm.generate(\n    prompts,\n    sampling_params=SamplingParams(max_tokens=200)\n)\n\n# Automatic in-flight batching for maximum throughput\n```\n\n## Performance benchmarks\n\n**Meta Llama 3-8B** (H100 GPU):\n- Throughput: 24,000 tokens\u002Fsec\n- Latency: ~10ms per token\n- vs PyTorch: **100× faster**\n\n**Llama 3-70B** (8× A100 80GB):\n- FP8 quantization: 2× faster than FP16\n- Memory: 50% reduction with FP8\n\n## Supported models\n\n- **LLaMA family**: Llama 2, Llama 3, CodeLlama\n- **GPT family**: GPT-2, GPT-J, GPT-NeoX\n- **Qwen**: Qwen, Qwen2, QwQ\n- **DeepSeek**: DeepSeek-V2, DeepSeek-V3\n- **Mixtral**: Mixtral-8x7B, Mixtral-8x22B\n- **Vision**: LLaVA, Phi-3-vision\n- **100+ models** on HuggingFace\n\n## References\n\n- **[Optimization Guide](references\u002Foptimization.md)** - Quantization, batching, KV cache tuning\n- **[Multi-GPU Setup](references\u002Fmulti-gpu.md)** - Tensor\u002Fpipeline parallelism, multi-node\n- **[Serving Guide](references\u002Fserving.md)** - Production deployment, monitoring, autoscaling\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fnvidia.github.io\u002FTensorRT-LLM\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=tensorrt_llm\n\n\n","# TensorRT-LLM\n\nNVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.\n\n## When to use TensorRT-LLM\n\n**Use TensorRT-LLM when:**\n- Deploying on NVIDIA GPUs (A100, H100, GB200)\n- Need maximum throughput (24,000+ tokens\u002Fsec on Llama 3)\n- Require low latency for real-time applications\n- Working with quantized models (FP8, INT4, FP4)\n- Scaling across multiple GPUs or nodes\n\n**Use vLLM instead when:**\n- Need simpler setup and Python-first API\n- Want PagedAttention without TensorRT compilation\n- Working with AMD GPUs or non-NVIDIA hardware\n\n**Use llama.cpp instead when:**\n- Deploying on CPU or Apple Silicon\n- Need edge deployment without NVIDIA GPUs\n- Want simpler GGUF quantization format\n\n## Quick start\n\n### Installation\n\n```bash\n# Docker (recommended)\ndocker pull nvidia\u002Ftensorrt_llm:latest\n\n# pip install\npip install tensorrt_llm==1.2.0rc3\n\n# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12\n```\n\n### Basic inference\n\n```python\nfrom tensorrt_llm import LLM, SamplingParams\n\n# Initialize model\nllm = LLM(model=\"meta-llama\u002FMeta-Llama-3-8B\")\n\n# Configure sampling\nsampling_params = SamplingParams(\n    max_tokens=100,\n    temperature=0.7,\n    top_p=0.9\n)\n\n# Generate\nprompts = [\"Explain quantum computing\"]\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    print(output.text)\n```\n\n### Serving with trtllm-serve\n\n```bash\n# Start server (automatic model download and compilation)\ntrtllm-serve meta-llama\u002FMeta-Llama-3-8B \\\n    --tp_size 4 \\              # Tensor parallelism (4 GPUs)\n    --max_batch_size 256 \\\n    --max_num_tokens 4096\n\n# Client request\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FMeta-Llama-3-8B\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Key features\n\n### Performance optimizations\n- **In-flight batching**: Dynamic batching during generation\n- **Paged KV cache**: Efficient memory management\n- **Flash Attention**: Optimized attention kernels\n- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference\n- **CUDA graphs**: Reduced kernel launch overhead\n\n### Parallelism\n- **Tensor parallelism (TP)**: Split model across GPUs\n- **Pipeline parallelism (PP)**: Layer-wise distribution\n- **Expert parallelism**: For Mixture-of-Experts models\n- **Multi-node**: Scale beyond single machine\n\n### Advanced features\n- **Speculative decoding**: Faster generation with draft models\n- **LoRA serving**: Efficient multi-adapter deployment\n- **Disaggregated serving**: Separate prefill and generation\n\n## Common patterns\n\n### Quantized model (FP8)\n\n```python\nfrom tensorrt_llm import LLM\n\n# Load FP8 quantized model (2× faster, 50% memory)\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-70B\",\n    dtype=\"fp8\",\n    max_num_tokens=8192\n)\n\n# Inference same as before\noutputs = llm.generate([\"Summarize this article...\"])\n```\n\n### Multi-GPU deployment\n\n```python\n# Tensor parallelism across 8 GPUs\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-405B\",\n    tensor_parallel_size=8,\n    dtype=\"fp8\"\n)\n```\n\n### Batch inference\n\n```python\n# Process 100 prompts efficiently\nprompts = [f\"Question {i}: ...\" for i in range(100)]\n\noutputs = llm.generate(\n    prompts,\n    sampling_params=SamplingParams(max_tokens=200)\n)\n\n# Automatic in-flight batching for maximum throughput\n```\n\n## Performance benchmarks\n\n**Meta Llama 3-8B** (H100 GPU):\n- Throughput: 24,000 tokens\u002Fsec\n- Latency: ~10ms per token\n- vs PyTorch: **100× faster**\n\n**Llama 3-70B** (8× A100 80GB):\n- FP8 quantization: 2× faster than FP16\n- Memory: 50% reduction with FP8\n\n## Supported models\n\n- **LLaMA family**: Llama 2, Llama 3, CodeLlama\n- **GPT family**: GPT-2, GPT-J, GPT-NeoX\n- **Qwen**: Qwen, Qwen2, QwQ\n- **DeepSeek**: DeepSeek-V2, DeepSeek-V3\n- **Mixtral**: Mixtral-8x7B, Mixtral-8x22B\n- **Vision**: LLaVA, Phi-3-vision\n- **100+ models** on HuggingFace\n\n## References\n\n- **[Optimization Guide](references\u002Foptimization.md)** - Quantization, batching, KV cache tuning\n- **[Multi-GPU Setup](references\u002Fmulti-gpu.md)** - Tensor\u002Fpipeline parallelism, multi-node\n- **[Serving Guide](references\u002Fserving.md)** - Production deployment, monitoring, autoscaling\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fnvidia.github.io\u002FTensorRT-LLM\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=tensorrt_llm\n\n\n",{"name":5,"author":43,"license":14,"version":44,"metadata":45,"description":7,"dependencies":56},"Orchestra Research","1.0.0",{"hermes":46},{"tags":47},[33,48,34,49,50,51,52,35,53,54,55],"TensorRT-LLM","Inference Optimization","High Throughput","Low Latency","Production","INT4","In-Flight Batching","Multi-GPU",[5,57],"torch","main","93bbc269671fbb340afa15b36a8766f15dc30df2","hermes-agent\u002Foptional-skills\u002Fmlops\u002Ftensorrt-llm","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch","2026-04-27 02:51:28",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill tensorrt-llm",{"tool":70,"command":71},"bunx","bunx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill tensorrt-llm",{"tool":73,"command":74},"pnpm","pnpm dlx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill tensorrt-llm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ftensorrt-llm\u002FSKILL.md",true,[78,84,88],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fmulti-gpu.md","REFERENCE","参考文档",6699,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ftensorrt-llm\u002Freferences\u002Fmulti-gpu.md",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Foptimization.md",5613,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ftensorrt-llm\u002Freferences\u002Foptimization.md",{"path":89,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fserving.md",9798,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ftensorrt-llm\u002Freferences\u002Fserving.md",[93,133,161,176,195,216],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":115,"publishedAt":38},"01M71G6TJ2RAQ1J0PV3K6D9GQA","writing-plans","Use when you have a spec or requirements for a multi-step task. Creates comprehensive implementation plans with bite-sized tasks, exact file paths, and complete code examples.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fwriting-plans\u002FSKILL.md",[99,104,107,112],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":105,"categoryName":106,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":113,"categoryName":114,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000036","内容创作者",[116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,25,23,132,21],"writing","writing plans","plans","design","review","workflow","documentation","tasks","development","code","planning","code-review","subagent","superpowers","paths","test-driven-development","test-driven",{"skillNo":134,"name":131,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":146,"publishedAt":38},"01M71G6R81013R3T7W5VP56GCB","Use when implementing any feature or bugfix, before writing implementation code. 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Dispatches fresh delegate_task per task with two-stage review (spec compliance then code quality).","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fsubagent-driven-development\u002FSKILL.md",[182,183,184,185],{"categoryNo":105,"categoryName":106,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},{"categoryNo":113,"categoryName":114,"taxonomyType":110,"taxonomyTypeDesc":111},{"categoryNo":186,"categoryName":187,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000037","律师与法务",[124,189,128,149,116,120,121,190,123,125,191,127,192,193,129,118,194,131,132,21],"subagent driven development","code quality","delegation","parallel","delegate","fresh",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":203,"publishedAt":38},"01M71G6EFFBQX6634E6XHB09Y1","requesting-code-review","Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Frequesting-code-review\u002FSKILL.md",[201,202],{"categoryNo":139,"categoryName":140,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[204,120,125,205,116,206,127,207,208,209,210,128,211,212,213,129,131,214,215,132],"code review","requesting code review","commit","verification","security scan","pipeline","loop","reviewer","fix","auto","static","auto-fix",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":220,"categories":221,"keywords":222,"publishedAt":38},"01M71G6CM8ANSWVFXCN9AAD58Y","plan","Plan mode for Hermes — inspect context, write a markdown plan into the active workspace's `.hermes\u002Fplans\u002F` directory, and do not execute the work.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fplan\u002FSKILL.md",[],[218,116,223,121,124,126,224,128,225,226,227,228,229,230,118,231,232,233,23,234],"directory","markdown","workspace","work","write","implementation","do","active","plan mode","inspect","mode","plan-mode"]