triton
llama.cpp
| triton | llama.cpp | |
|---|---|---|
| 30 | 774 | |
| 11,054 | 57,463 | |
| 4.3% | - | |
| 9.9 | 10.0 | |
| 3 days ago | 1 day ago | |
| C++ | C++ | |
| MIT License | MIT License |
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triton
- OpenAI Triton: language and compiler for highly efficient Deep-Learning
-
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.)
-
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
-
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.
llama.cpp
- Xmake: A modern C/C++ build tool
-
Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
-
Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
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Embeddings are a good starting point for the AI curious app developer
Have just done this recently for local chat with pdf feature in https://recurse.chat. (It's a macOS app that has built-in llama.cpp server and local vector database)
Running an embedding server locally is pretty straightforward:
- Get llama.cpp release binary: https://github.com/ggerganov/llama.cpp/releases
- Mixtral 8x22B
What are some alternatives?
cuda-python - CUDA Python Low-level Bindings
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
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
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
web-llm - Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.
ggml - Tensor library for machine learning
cutlass - CUDA Templates for Linear Algebra Subroutines
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM