Unsloth adds native ARM64 Windows CUDA support on NVIDIA RTX Spark
Overview
Unsloth says its tools now run natively on ARM64 Windows with CUDA on NVIDIA RTX Spark laptops, for running and training diffusion, multimodal, language and decision models.
Unsloth says training on the platform is 2.7 times faster, a figure the company reports and that the reviewed reports have not independently measured. Users can install through winget or from unsloth.ai/download/windows.
In a separate post, Unsloth says RTX Spark lets users run and train models above 120 billion parameters locally on a laptop, with up to 128GB of unified memory. Its chart compares RTX Spark N1X with a premium SoC and shows 2.7x for fine-tuning with Unsloth.
Written by AI from the articles below · updated Oct 16, 8:25 PM ET
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Developments
2 developments
- Oct 16, 8:04 PM ET · 1 articleUnsloth adds native ARM64 Windows CUDA support for RTX Spark, claiming 2.7x speedupDaniel Han: Unsloth adds native Windows ARM64 CUDA support on RTX Spark
- Oct 16, 7:54 PM ET · 1 articleUnsloth announces local 120B+ model training and running on NVIDIA RTX Spark laptopsUnsloth AI: Unsloth says NVIDIA RTX Spark can train 120B-plus models on a laptop
Article timeline
The articles in this story. Times are ET.
Daniel Han@danielhanchenXUnsloth adds native Windows ARM64 CUDA support on RTX SparkAIUnsloth says it now runs natively on ARM64 Windows with CUDA on NVIDIA RTX Spark, covering diffusion, multimodal, language, and decision models. The author reports training is 2.7x faster and gives winget or the Unsloth download page as install options.

Unsloth AI@UnslothAIOfficialUnsloth says NVIDIA RTX Spark can train 120B-plus models on a laptopAIUnsloth says NVIDIA RTX Spark lets users run and train models above 120B parameters locally on a laptop. The post says training diffusion, decision and multimodal models runs about 2.7 times faster with CUDA on ARM, with up to 128GB of unified memory. The attached chart compares RTX Spark N1X with a premium SoC, showing 2.7x for fine-tuning with Unsloth.

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