Showing 3 open source projects for "amd gpu memory tester"

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  • 1
    FlashAttention

    FlashAttention

    Fast and memory-efficient exact attention

    FlashAttention is a high-performance deep learning optimization library that reimplements the attention mechanism used in transformer models to be significantly faster and more memory-efficient than standard implementations. It achieves this by using IO-aware algorithms that minimize memory reads and writes, reducing the quadratic memory overhead typically associated with attention operations. The project provides implementations of FlashAttention, FlashAttention-2, and newer iterations optimized for modern GPU architectures such as NVIDIA Hopper and AMD accelerators. ...
    Downloads: 1 This Week
    Last Update:
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  • 2
    whichllm

    whichllm

    Find the local LLM that actually runs and performs best

    whichllm is a command-line tool for finding local large language models that can realistically run on a user’s hardware. It detects the machine’s available resources, including GPU, CPU, memory, and storage, then recommends models based on practical fit rather than parameter count alone. The project is useful for users who are unsure which local LLM will perform well on their system. It focuses on real, recency-aware benchmarks so recommendations better reflect current model performance. whichllm is especially helpful for developers, AI hobbyists, and researchers comparing local inference options across NVIDIA, AMD, Apple Silicon, and CPU-only environments. ...
    Downloads: 2 This Week
    Last Update:
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  • 3
    WanGP

    WanGP

    AI video generator optimized for low VRAM and older GPUs use

    Wan2GP is an open source AI video generation toolkit designed to make modern generative models accessible on consumer-grade hardware with limited GPU memory. It acts as a unified interface for running multiple video, image, and audio generation models, including Wan-based models as well as other systems like Hunyuan Video, Flux, and Qwen. A key focus of the project is reducing VRAM requirements, enabling some workflows to run on as little as 6 GB while still supporting older Nvidia and certain AMD GPUs. ...
    Downloads: 43 This Week
    Last Update:
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