Showing 4 open source projects for "rtx"

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  • 1
    Wan2.2

    Wan2.2

    Wan2.2: Open and Advanced Large-Scale Video Generative Model

    ...Wan2.2 also open-sources a 5-billion parameter high-compression VAE-based hybrid text-image-to-video (TI2V) model that supports 720P video generation at 24fps on consumer-grade GPUs like the RTX 4090. It supports multiple video generation tasks including text-to-video.
    Downloads: 165 This Week
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  • 2
    GLM-130B

    GLM-130B

    GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)

    ...Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple benchmarks, while also showing significant improvements on Chinese datasets compared to other large models. The model supports efficient inference via INT8 and INT4 quantization, reducing hardware requirements from 8× A100 GPUs to as little as a single server with 4× RTX 3090s. Built on the SwissArmyTransformer (SAT) framework and compatible with DeepSpeed and FasterTransformer, it supports high-speed inference (up to 2.5× faster) and reproducible evaluation across 30+ benchmark tasks.
    Downloads: 0 This Week
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  • 3
    Devstral Small 2

    Devstral Small 2

    Lightweight 24B agentic coding model with vision and long context

    Devstral Small 2 is a compact agentic language model designed for software engineering workflows, excelling at tool usage, codebase exploration, and multi-file editing. With 24B parameters and FP8 instruct tuning, it delivers strong instruction following while remaining lightweight enough for local and on-device deployment. The model achieves competitive performance on SWE-bench, validating its effectiveness for real-world coding and automation tasks. It introduces vision capabilities,...
    Downloads: 0 This Week
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  • 4
    Nemotron 3.5 Lightning

    Nemotron 3.5 Lightning

    Efficient 30B MoE model for long-running agents and local inference

    ...The model supports context windows up to 1 million tokens, enabling long-running workflows and large-context reasoning. Its NVFP4 quantization reduces deployment requirements while targeting NVIDIA hardware ranging from DGX Spark and RTX 5090 systems to H100, H200, and GB200 accelerators. NVIDIA also provides DSpark, Multi-Token Prediction, and DFlash speculative decoding methods to accelerate text generation. It supports English, coding languages, Spanish, French, German, Italian, and Japanese, and is intended for commercially deployable AI applications. The model can run on a single DGX Spark or H100 and integrates with inference frameworks including vLLM.
    Downloads: 0 This Week
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