\n\n# TensorRT-LLM Backend\nThe Triton backend for [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM).\nYou can learn more about Triton backends in the [backend repo](https://github.com/triton-inference-server/backend).\nThe goal of TensorRT-LLM Backend is to let you serve [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)\nmo"},{"ref":"P14","kind":"page","title":"deepinfra/TensorRT-LLM repository metadata","date":"2026-06-11T04:19:25.338298+00:00","date_source":null,"source_url":"https://github.com/deepinfra/TensorRT-LLM","signal_url":null,"signal_json_url":null,"text":"# deepinfra/TensorRT-LLM\n\nDescription: TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.\n\nLanguage: Python\n\nLicense: NOASSERTION\n\nStars: 0\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2024-03-13T18:57:47Z\n\nPushed: 2026-06-04T23:29:39Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: NVIDIA/TensorRT-LLM\n\nArchived: no\n\nREADME:\n\n\nTensorRT LLM\n===========================\n
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports\nstate-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
\n\n[](https://nvidia.github.io/TensorRT-LLM/)\n[](https://deepwiki.com/NVIDIA/TensorRT-LLM)\n[](https://www.python.org/downloads/release/python-3123/)\n[](https://www.python.org/downloads/release/python-31012/)\n[](https://developer.nvidia.com/cuda-downloads)\n[](https://pytorch.org)\n[](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/version.py)\n[](https://github.com/NVIDIA/TensorRT-LLM/blob/main/LICENSE)\n\n[Architecture](https://nvidia.github.io/TensorRT-LLM/developer-guide/overview.html) | [Performance](https://nvidia.github.io/TensorRT-LLM/developer-guide/perf-overview.html) | [Examples](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) | [Documentation](https://nvidia.github.io/TensorRT-LLM/) | &nbs"},{"ref":"P15","kind":"page","title":"deepinfra/cog-llama-2 repository metadata","date":"2026-06-11T04:10:00.373147+00:00","date_source":null,"source_url":"https://github.com/deepinfra/cog-llama-2","signal_url":null,"signal_json_url":null,"text":"# deepinfra/cog-llama-2\n\nDescription: A cog for running llama-2 using llama.cpp server\n\nLanguage: Python\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-08-01T09:33:38Z\n\nPushed: 2023-08-01T09:47:41Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\nSetup\n=====\n\n* download cog: https://github.com/replicate/cog/releases/\n* download a llama.cpp quantization from https://huggingface.co/TheBloke/Llama-2-70B-Chat-GGML/tree/main, place in weights/\n* tweak predict.py `MODEL` variable to match your weights\n* try sample inference with `cog predict -i prompt=\"What came first, the chicken or the egg?\"`\n* make sure to put the right image in `cog.yaml` so the container name is ready to push out-of-the box\n* once you're ready run `cog build`, test it one more time with\n\n```bash\ndocker run --gpus all --rm -it -p 5000:5000 IMAGE_NAME\n# in another terminal, after it starts\ncurl -X POST http://127.0.0.1:5000/predictions \\\n--data '{\"input\": {\"prompt\": \"Hello\"}}' \\\n-H 'Content-Type: application/json' \\\n| python -m json.tool\n```\n\n* (optional) push image with `docker push IMAGE_NAME`\n\nNotes\n=====\n\n* the `EXTRA` flags at the top of `predict.py` are for 70B (and 35B) llama-2 models. For 13B and under, drop `-gqa` and `-eps`, i.e leave `-ngl`.\n* the setup function starts the llama server and waits for it to become available\n* for predictions we query the HTTP server\n* for `cog build --separate-weights` you might need a more recent cog 0.8.4+ with some kinks fixed"},{"ref":"P16","kind":"page","title":"deepinfra/deepctl repository metadata","date":"2026-06-11T04:10:00.292927+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepctl","signal_url":null,"signal_json_url":null,"text":"# deepinfra/deepctl\n\nDescription: Command line tool for Deep Infra cloud ML inference service\n\nLanguage: Rust\n\nLicense: Apache-2.0\n\nStars: 36\n\nForks: 3\n\nOpen issues: 2\n\nCreated: 2022-12-07T22:25:03Z\n\nPushed: 2024-06-10T16:32:04Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# deepctl\nCommand line tool for [Deep Infra cloud ML inference service](https://deepinfra.com/).\n\n## Installation\n\n#### Linux, MacOS\n\n```bash\ncurl https://deepinfra.com/get.sh | sh\n```\n\n#### Download from Github\n\nYou can also download deepctl from [Releases](https://github.com/deepinfra/deepctl/releases).\n\n## Usage\n\n#### Sign in\nYou need a Github account to use deepctl. This command will open your Github Sign-on page in your browser. Follow instructions there.\n```bash\ndeepctl auth login\n```\n#### Auth token\nYou can view your Deep Infra API auth token to use it in your applications using the following command or in the [dashboard](https://deepinfra.com/dash/api_keys).\n```bash\ndeepctl auth token\n```\n\n#### List supported models\n```bash\ndeepctl model list\n```\n\n#### Create a model deployment\n```bash\ndeepctl deploy create -m openai/whisper-small\n```\n\n#### Inference\nTo get information about model, it's inference arguments, response fields and ways to call endpoint, use the following command:\n```bash\ndeepctl model info -m openai/whisper-small\n```\nOutput:\n```\nmodel: openai/whisper-small\ntype: automatic-speech-recognition\nCURL invocation:\n\ncurl -X POST \\\n-H \"Authorization: bearer $AUTH_TOKEN\" \\\n-F audio=@my_voice.mp3 \\\n'https://api.deepinfra.com/v1/inference/openai/whisper-small'\n\ndeepctl invocation:\n\ndeepctl infer \\\n-m 'openai/whisper-small' \\\n-i audio=@my_voice.mp3\n...\n```\nWhen using HTTP inference api make sure to pass your AUTH_TOKEN header.\n```bash\ncurl -X POST \\\n-H \"Authorization: bearer $AUTH_TOKEN\" \\\n-F audio=@/path/to/hello_world.mp3 \\\n'https://api.deepinfra.com/v1/inference/openai/whisper-small'\n```\nOutput:\n```\n{\n\"text\": \"Hello World\",\n\"segments\": [\n...\n],\n\"language\": \"en\"\n}\n\n```\n\n#### List your deployments\n```bash\ndeepctl deploy list\n```\nOutput:\n```\n[\n{\n\"created_at\": \"2023-01-26T19:33:23\",\n\"deploy_id\": \"DpM4BkrjEspUwmTa\",\n\"fail_reason\": \"\",\n\"model_name\": \"openai/whisper-small\",\n\"stat"},{"ref":"P17","kind":"page","title":"deepinfra/deepinfra-node repository metadata","date":"2026-06-11T04:10:00.241619+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepinfra-node","signal_url":null,"signal_json_url":null,"text":"# deepinfra/deepinfra-node\n\nDescription: Official TypeScript wrapper for DeepInfra Inference API\n\nLanguage: TypeScript\n\nLicense: MIT\n\nStars: 20\n\nForks: 3\n\nOpen issues: 8\n\nCreated: 2024-03-12T22:26:38Z\n\nPushed: 2024-05-13T10:21:59Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# DeepInfra Node API Library\n\n
\n\nThis library provides a simple way to interact with the DeepInfra API. \n
\n
\nCheck out our docs [here.](https://deepinfra.github.io/deepinfra-node/)\n\n## Installation\n\n```bash\nnpm install deepinfra\n```\n\n## Usage\n\n### Use [text generation models](https://deepinfra.com/models/text-generation)\n\nThe Mixtral mixture of expert model, developed by Mistral AI, is an innovative experimental machine learning model that\nleverages a mixture of 8 experts (MoE) within 7b models. Its release was facilitated via a torrent, and the model's\nimplementation remains in the experimental phase.\\_\n\n```typescript\nimport {TextGeneration} from \"deepinfra\";\n\nconst modelName = \"mistralai/Mixtral-8x22B-Instruct-v0.1\";\nconst apiKey = \"YOUR_DEEPINFRA_API_KEY\";\nconst main = async () => {\nconst mixtral = new TextGeneration(modelName, apiKey);\nconst body = {\ninput: \"What is the capital of France?\",\n};\nconst output = await mixtral.generate(body);\nconst text = output.results[0].generated_text;\nconsole.log(text);\n};\n\nmain();\n```\n\n### Use [text embedding models](https://deepinfra.com/models/embeddings)\n\nGte Base is an text embedding model that generates embeddings for the input text. The model is trained by Alibaba DAMO Academy.\n\n```typescript\nimport { GteBase } from \"deepinfra\";\n\nconst apiKey = \"YOUR_DEEPINFRA_API_KEY\";\nconst modelName = \"thenlper/gte-base\";\nconst main = async () => {\nconst gteBase = new Embeddings(modelName, apiKey);\nconst body = {\ninputs: [\n\"What is the capital of France?\",\n\"What is the capital of Germany?\",\n\"What is the capital of Italy?\",\n],\n};\nconst output = await gteBase.generate(body);\nconst embeddings = output.embeddings[0];\nconsole.log(embeddings);\n};\n\nmain();\n```\n\n### Use [SDXL](https://deepinfra.com/stability-ai/sdxl) to generate images\n\nSDXL requires unique pa"},{"ref":"P18","kind":"page","title":"deepinfra/deepinfra-chat repository metadata","date":"2026-06-11T04:09:59.662349+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepinfra-chat","signal_url":null,"signal_json_url":null,"text":"# deepinfra/deepinfra-chat\n\nDescription: Sample Next.js ai chat app using Deep Infra inference and Vercel ai sdk\n\nLanguage: TypeScript\n\nStars: 1\n\nForks: 2\n\nOpen issues: 0\n\nCreated: 2025-03-17T09:01:46Z\n\nPushed: 2025-03-17T13:57:12Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\nThis is a sample Next.js ai chat application that uses [Deep Infra models](https://deepinfra.com/models) for inference and [Vercel AI SDK](https://sdk.vercel.ai/docs/introduction).\n\n## Table of Contents\n\n- [Deploy](#deploy)\n- [Getting Started](#getting-started): Use the Deep Infra Vercel integration to quickly setup and run this sample app\n- [Manual Setup](#manual-setup): Detailed instruction for local development\n- [Experiment](#experiment): Try diffenet models and inference options\n\n## Deploy\n\n[](https://vercel.com/new/clone?repository-url=https://github.com/deepinfra/deepinfra-chat&products=[{\"type\":\"integration\",\"integrationSlug\":\"deepinfra\",\"productSlug\":\"api-token\",\"protocol\":\"ai\"}])\n\n## Getting Started\n\nThis section assumes you have set up a Deep Infra account and project using the Vercel Integration (press deploy button above).\n\n### Step 1. Pull environment variables\n\nYou'll need a Deep Infra API key in your environment variables to connect to the model. Run the following command to pull them from Vercel:\n\n```bash\nvercel env pull\n```\n\n### Step 2. Run the app\n\nRun `npm run dev`. You can start chatting with the ai model immediately.\n\n## Manual Setup\n\n### Step 1. Deep Infra account\n\nCreate a Deep Infra account either through the [Vercel marketplace integration](https://vercel.com/marketplace/deepinfra) or by directly registering at [Deep Infra](https://deepinfra.com)\n\n### Step 2. Clone the sample app\n\n```bash\ngit clone git@github.com:deepinfra/deepinfra-chat.git\n```\n\n```bash\ncd deepinfra-chat\n```\n\n```bash\nnpm install\n```\n\n### Step 3. Set up environment variables\n\nCopy the `.env.local.example` file in this directory to `.env.local` (which will be ignored by Git):\n\n```bash\ncp .env.local.example .env.local\n```\n\nFrom the [api keys page](https://deepinfra.com/dash/api_keys) in your Deep Infra dashboard, create a new token or use an e"},{"ref":"P19","kind":"page","title":"deepinfra/ocr-tools repository metadata","date":"2026-06-11T04:09:59.494597+00:00","date_source":null,"source_url":"https://github.com/deepinfra/ocr-tools","signal_url":null,"signal_json_url":null,"text":"# deepinfra/ocr-tools\n\nLanguage: Python\n\nStars: 5\n\nForks: 2\n\nOpen issues: 1\n\nCreated: 2025-08-02T22:31:20Z\n\nPushed: 2025-08-02T23:13:51Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n## ocr-tools\n\nThis document is tutorial how to use olmocr endpoint on DeepInfra to parse texts from pdf\n\n# Install requirements\n\npip install -r requirements.txt\n\n(if linux): sudo apt-get install poppler-utils \n(if macOS): brew install poppler\n\n# Run command\n\npython3 scrape_pdf.py --model allenai/olmOCR-7B-0725-FP8 --api-key DEEPINFRA_API_KEY --pdf-path horribleocr.pdf"},{"ref":"P20","kind":"page","title":"deepinfra/docs repository metadata","date":"2026-06-11T04:09:59.469761+00:00","date_source":null,"source_url":"https://github.com/deepinfra/docs","signal_url":null,"signal_json_url":null,"text":"# deepinfra/docs\n\nDescription: DeepInfra platform documentation\n\nLanguage: MDX\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-11-17T23:21:43Z\n\nPushed: 2026-06-03T10:24:48Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Mintlify Starter Kit\n\nUse the starter kit to get your docs deployed and ready to customize.\n\nClick the green **Use this template** button at the top of this repo to copy the Mintlify starter kit. The starter kit contains examples with\n\n- Guide pages\n- Navigation\n- Customizations\n- API reference pages\n- Use of popular components\n\n**[Follow the full quickstart guide](https://starter.mintlify.com/quickstart)**\n\n## Development\n\nInstall the [Mintlify CLI](https://www.npmjs.com/package/mint) to preview your documentation changes locally. To install, use the following command:\n\n```\nnpm i -g mint\n```\n\nRun the following command at the root of your documentation, where your `docs.json` is located:\n\n```\nmint dev\n```\n\nView your local preview at `http://localhost:3000`.\n\n## Publishing changes\n\nInstall our GitHub app from your [dashboard](https://dashboard.mintlify.com/settings/organization/github-app) to propagate changes from your repo to your deployment. Changes are deployed to production automatically after pushing to the default branch.\n\n## Need help?\n\n### Troubleshooting\n\n- If your dev environment isn't running: Run `mint update` to ensure you have the most recent version of the CLI.\n- If a page loads as a 404: Make sure you are running in a folder with a valid `docs.json`.\n\n### Resources\n- [Mintlify documentation](https://mintlify.com/docs)"},{"ref":"P21","kind":"page","title":"deepinfra/cookbooks repository metadata","date":"2026-06-11T04:09:59.413595+00:00","date_source":null,"source_url":"https://github.com/deepinfra/cookbooks","signal_url":null,"signal_json_url":null,"text":"# deepinfra/cookbooks\n\nDescription: A collection of cookbooks, tutorials, and examples for using AI models on DeepInfra. This repository provides practical guides, performance benchmarks, and production-ready code examples to help developers build with AI models efficiently. Each cookbook includes comprehensive Jupyter notebooks, benchmarking suites, and real-world use case examples.\n\nLanguage: Jupyter Notebook\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-12-15T01:25:49Z\n\nPushed: 2025-12-15T09:13:39Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# DeepInfra Cookbooks\n\nA collection of cookbooks, tutorials, and examples for using AI models on DeepInfra. This repository provides practical guides, performance benchmarks, and production-ready code examples to help developers build with AI models efficiently.\n\n## What's Included\n\n### Models\n\n- **[Nemotron 3 Nano](models/nemotron-3-nano/)** - NVIDIA's open reasoning model with hybrid MoE-Mamba architecture\n\n## Structure\n\nEach model cookbook includes:\n\n- **Tutorial Notebook** - Step-by-step Jupyter notebook guide\n- **Examples** - Ready-to-run use case examples\n- **Benchmarks** - Performance and cost analysis tools\n- **Documentation** - Model-specific README with setup instructions\n\n## Quick Start\n\n1. Navigate to a model directory (e.g., `models/nemotron-3-nano/`)\n2. Install dependencies: `pip install -r requirements.txt`\n3. Set your DeepInfra API key: `export DEEPINFRA_API_KEY=\"your-api-key\"`\n4. Follow the model-specific README for detailed instructions\n\n## Resources\n\n- [DeepInfra Documentation](https://deepinfra.com/docs)\n- [DeepInfra Dashboard](https://deepinfra.com)"},{"ref":"P22","kind":"page","title":"deepinfra/deepctl v0.4.1","date":"2026-06-11T04:05:59.769135+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepctl/releases/tag/v0.4.1","signal_url":null,"signal_json_url":null,"text":"# v0.4.1\n\nRepository: deepinfra/deepctl\n\nTag: v0.4.1\n\nPublished: 2023-07-19T21:36:34Z\n\nPrerelease: no\n\nRelease notes: none published."},{"ref":"P23","kind":"page","title":"deepinfra/deepctl v0.4.3","date":"2026-06-11T04:05:59.742324+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepctl/releases/tag/v0.4.3","signal_url":null,"signal_json_url":null,"text":"# v0.4.3\n\nRepository: deepinfra/deepctl\n\nTag: v0.4.3\n\nPublished: 2023-09-07T14:07:59Z\n\nPrerelease: no\n\nRelease notes: none published."},{"ref":"P24","kind":"page","title":"deepinfra/deepctl v0.3.8","date":"2026-06-11T04:05:59.481423+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepctl/releases/tag/v0.3.8","signal_url":null,"signal_json_url":null,"text":"# v0.3.8\n\nRepository: deepinfra/deepctl\n\nTag: v0.3.8\n\nPublished: 2023-06-02T18:45:21Z\n\nPrerelease: no\n\nRelease notes: none published."},{"ref":"P25","kind":"page","title":"deepinfra/deepctl v0.4.2","date":"2026-06-11T04:05:59.435248+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepctl/releases/tag/v0.4.2","signal_url":null,"signal_json_url":null,"text":"# v0.4.2\n\nRepository: deepinfra/deepctl\n\nTag: v0.4.2\n\nPublished: 2023-07-20T17:47:29Z\n\nPrerelease: no\n\nRelease notes: none published."},{"ref":"P26","kind":"page","title":"deepinfra/deepinfra-node 1.6.2","date":"2026-06-11T04:05:59.16401+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepinfra-node/releases/tag/1.6.2","signal_url":null,"signal_json_url":null,"text":"# 1.6.2 | Fix in payload and responses in text-to-image models\n\nRepository: deepinfra/deepinfra-node\n\nTag: 1.6.2\n\nPublished: 2024-04-04T19:04:30Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* bugfix(text-to-image types): TextToImage fields differ from SDXL fields by @ichernev in https://github.com/ovuruska/deepinfra-api/pull/16\n* Cog models + SDXL by @ichernev in https://github.com/ovuruska/deepinfra-api/pull/15"},{"ref":"P27","kind":"page","title":"deepinfra/deepinfra-node 2.0.0-rc","date":"2026-06-11T04:05:59.04918+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepinfra-node/releases/tag/2.0.0-rc","signal_url":null,"signal_json_url":null,"text":"# 2.0.0-rc\n\nRepository: deepinfra/deepinfra-node\n\nTag: 2.0.0-rc\n\nPublished: 2024-04-18T14:34:59Z\n\nPrerelease: no\n\nRelease notes:\nBetter developer experience is on the way!"},{"ref":"P28","kind":"page","title":"deepinfra/deepinfra-node 2.0.0","date":"2026-06-11T04:05:59.036556+00:00","date_source":null,"source_url":"https://github.com/deepinfra/deepinfra-node/releases/tag/2.0.0","signal_url":null,"signal_json_url":null,"text":"# 2.0.0 | Base models\n\nRepository: deepinfra/deepinfra-node\n\nTag: 2.0.0\n\nPublished: 2024-04-22T15:03:44Z\n\nPrerelease: no\n\nRelease notes:\n**Features**\n\n- DeepInfra API key can be provided from the environment.\n- Image classification models are available.\n- Zero shot image classification models are 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