triton
ollama
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triton | ollama | |
---|---|---|
30 | 193 | |
10,981 | 58,943 | |
7.9% | 29.0% | |
9.9 | 9.9 | |
3 days ago | 5 days ago | |
C++ | Go | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
triton
- OpenAI Triton: language and compiler for highly efficient Deep-Learning
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Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
There's a ton of cool opportunity in the runtime layer. I've been keeping my eye on the compiler-based approaches. From what I've gathered many of the larger "production" inference tools use compilers:
- https://github.com/openai/triton
- Core Functionality for AMD #1983
- Project name easily confused with Nvidia triton
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Nvidia's CUDA Monopoly
Does anyone have more inside knowledge from OpenAI or AMD on AMDGPU support for Triton?
I see this:
https://github.com/openai/triton/issues/1073
But it's not clear to me if we will see AMD GPUs as first class citizens for pytorch in the future?
- @soumithchintala (Cofounded and lead @PyTorch at Meta) on Twitter: I'm fairly puzzled by $NVDA skyrocketing... (cont.)
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The tiny corp raised $5.1M
I thought this was a good overview of the idea Triton can circumvent the CUDA moat: https://www.semianalysis.com/p/nvidiaopenaitritonpytorch
It also looks like they added MLIR backend to Triton though I wonder if Mojo has advantages since it was built on MLIR? https://github.com/openai/triton/pull/1004
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Anyone hosting a local LLM server
I'm pretty happy with the setup, because it allows me to keep all the AI stuff and its dozens of conda envs and repos etc. seperate from my normal setup and "portable". It may have some performance impact (although I don't personally notice any significant difference to running it "natively" on windows), and it may enable some extra functionality, such as access to OpenAi's Triton etc., but that's currently neither here nor there.
- Triton: Runtime for highly efficient custom Deep-Learning primitives
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Mojo – a new programming language for all AI developers
Very cool development. There is too much busy work going from development to test to production. This will help to unify everything. OpenAI Triton https://github.com/openai/triton/ is going for a similar goal. But this is a more fundamental approach.
ollama
- Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B
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I Said Goodbye to ChatGPT and Hello to Llama 3 on Open WebUI - You Should Too
I’m a huge fan of open source models, especially the newly release Llama 3. Because of the performance of both the large 70B Llama 3 model as well as the smaller and self-host-able 8B Llama 3, I’ve actually cancelled my ChatGPT subscription in favor of Open WebUI, a self-hostable ChatGPT-like UI that allows you to use Ollama and other AI providers while keeping your chat history, prompts, and other data locally on any computer you control.
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Let’s build AI-tools with the help of AI and Typescript!
Ollama for running LLMs locally
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One LLaMa to rule them all
There are various other interesting options to set, but for those, I will direct you to the link to the documentation. During the OS Day, I had the chance to experiment a bit with the models offered by Ollama; in fact, if you need some inspiration, I invite you to check out the YouTube channel of Shroedinger Hat where you can find the videos of the individual talks, also organized in a single playlist; you will find more than one showing the use of Ollama for various projects and in various ways 😁
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How to Run Llama 3 Locally with Ollama and Open WebUI
That’s where Ollama comes in! Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. Ollama takes advantage of the performance gains of llama.cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. It also includes a sort of package manager, allowing you to download and use LLMs quickly and effectively with just a single command.
- Ollama: Acknowledge the work done by Georgi and team
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Mixtral 8x22B
easiest is probably with ollama [0]. I think the ollama API is OpenAI compatible.
[0]https://ollama.com/
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Ollama 0.1.32: WizardLM 2, Mixtral 8x22B, macOS CPU/GPU model split
They ended up addressing this issue by including it on the last line of their readme as one of the "Supported backends[sic]".
https://github.com/ollama/ollama/issues/3697
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AI Inference now available in Supabase Edge Functions
LLM models are challenging to run directly via ONNX runtime on CPU. For these, we are using a GPU-accelerated Ollama server under the hood:
- Run copilot locally
What are some alternatives?
cuda-python - CUDA Python Low-level Bindings
llama.cpp - LLM inference in C/C++
Halide - a language for fast, portable data-parallel computation
gpt4all - gpt4all: run open-source LLMs anywhere
GPU-Puzzles - Solve puzzles. Learn CUDA.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
web-llm - Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.
llama - Inference code for Llama models
cutlass - CUDA Templates for Linear Algebra Subroutines
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.