AI · alternatives · 2026
24 alternatives to fast_trimul
A drop-in, hardware-agnostic library for Fused Triangle Multiplicative Updates across AlphaFold3-family models, powered by CUTLASS CuTe kernels. - tiagomonteiro0715/fast_trimul
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1
Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA / LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…
2023 · github.com · its alternatives →
- 2
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com · its alternatives →
- 3
cuTile Rust provides a safe, tile-based kernel programming DSL for the Rust programming language. It features a safe host-side API for passing tensors to asynchronously executed kernel functions. - NVlabs/cutile-rs
Jun 2026 · github.com · its alternatives →
- 4
Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
2024 · asciinema.org · its alternatives →
- 5
Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that…
2024 · github.com · its alternatives →
- 6
Algebraic enhancements for GEMM & AI accelerators. Contribute to trevorpogue/algebraic-nnhw development by creating an account on GitHub.
2024 · github.com · its alternatives →
- 7
Trim is building a foundation model for physics. We found almost all well-maintained transformer-based architectures are optimized for language models. We’re releasing our own open-source transformer package that is optimized for physics. It achieves a 90% reduction in memory usage and a 3.5x speedup in a standard Navier-Stokes dataset.
2025 · github.com · its alternatives →
- 8
Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator - joennlae/halutmatmul
2023 · github.com · its alternatives →
- 9
Long-simmering side project that is finally ready to see the light. HAMTs are a cool persistent data structure and implementing one has been a lot of fun. Beyond the code, there is likely some value in the extensive and largely complete implementation docs; basic benchmarks are linked in the README, too. Kind of aiming to be "the libavl for HAMTs". That is obviously a high and aspirational bar but a distinct possibility if it stirs up a little interest and/or contribution. Anyways, it's time for this to go out, collect feedback and maybe even some use outside of toy projects. Let me…
2023 · github.com · its alternatives →
- 10
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production - huggingface/tokenizers
2020 · github.com · its alternatives →
- 11
We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com · its alternatives →
- 12
This project presents a new data structure for storing ordered integer maps. The proposed data structure is a compressive, cache-friendly, radix tree that has performance comparable to an unordered map (`std::unordered_map`) and is an order of magnitude faster than an ordered map (`std::map`).
2024 · github.com · its alternatives →
- 13
I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…
2022 · courses.dibya.online · its alternatives →
- 14AG
This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.
2025 · rlafuente.com · its alternatives →
- 15
A pipeline that translates Rust GPU code into formal Coq models, as a foundation for memory model proofs - neelsomani/vericuda
2025 · github.com · its alternatives →
- 16
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is. It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one…
2023 · github.com · its alternatives →
- 17
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
- 18
2024 · gitlab.com · its alternatives →
- 19
Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams - jparkie/PDD
2016 · github.com · its alternatives →
- 20
Absolute minimalistic implementation of a GPT-like transformer using only numpy (<650 lines). - joennlae/tensorli
2023 · github.com · its alternatives →
- 21
A fast and memory-optimized string library for C++ - btwael/SuperString
2018 · github.com · its alternatives →
- 22PO
Our company Vertex.AI has been working on this for a while but this is the first public release. We're starting with using PlaidML to bring OpenCL support to Keras and more frameworks, platforms, etc are coming. Yes, this means you can use use your AMD GPU for deep learning dev. Sorry, no Mac or Windows support yet although the brave can try building from source (it should work). http://vertex.ai/blog/announcing-plaidml https://github.com/plaidml/plaidml
2017 · its alternatives →
- 23
An Erlang library for generating adversarial network conditions for QAing distributed applications/systems on a single Linux box. - lostcolony/damocles
2015 · github.com · its alternatives →
- 24
A simple re-implementation of Google's sparsehash as a learning excercise. - qpfiffer/Simple-Sparsehash
2015 · github.com · its alternatives →
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- 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning alternatives
- Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon alternatives
- cuTile Rust: Safe, data-race-free GPU kernels in Rust alternatives
- Speeding up LLM inference 2x times (possibly) alternatives
- FastGraphRAG – Better RAG using good old PageRank alternatives
- Matrix Multiplication with Half the Multiplications alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →