NVIDIA Cosmos3 64B image model runs locally in INT4 on Apple Silicon and CUDA

A developer has released INT4 quantized weights and code that run NVIDIA's Cosmos3, a 64-billion-parameter image generation model, locally on both Apple Silicon and CUDA hardware. The release, posted to r/LocalLLaMA, covers text-to-image and image-to-video workloads and includes a comparison against xAI's Grok image model. The code lives in a GitHub repository, with the quantized weights hosted on Hugging Face under the name Cosmos3-Super-Text2Image-4Step-INT4-G64-BF16.

The project targets two backends: MLX for Apple Silicon Macs and CUDA for NVIDIA GPUs. The poster, /u/Formal-Swordfish-228, reported that a single clip took roughly five minutes to generate on an M4 Max Mac with 128 GB of unified memory. That figure gives a practical sense of the throughput users can expect on high-end Apple hardware, though the post does not break down generation time by task or resolution.

The model itself is Cosmos3, NVIDIA's 64B-parameter image generation architecture, which the poster describes as state-of-the-art for local image generation. The INT4 quantization is what makes the model feasible on consumer hardware that would otherwise struggle to hold a 64B model in memory. The Hugging Face weight name indicates a four-step text-to-image variant quantized to INT4 with a group size of 64 and BF16 components.

The release is part of a broader pattern in the local AI community of pushing large, formerly cloud-only models onto personal machines through aggressive quantization. Cosmos3 at 64B parameters is far larger than the image models most hobbyists run locally, and the INT4 format plus MLX support reflects the growing appetite for running frontier-class generation on Macs and single-GPU workstations rather than through API calls.

The poster frames the work as a direct comparison point against Grok, xAI's image generator, suggesting the local INT4 build is positioned as a competitive alternative to a proprietary cloud service. No benchmark numbers or side-by-side outputs are included in the post summary, so the comparison rests on the poster's characterization rather than measured results.

Details beyond the headline claims remain thin. The post does not specify which NVIDIA GPUs the CUDA path supports, how much VRAM the INT4 model requires, or whether the image-to-video and text-to-image paths share the same quantization scheme. The five-minute clip time on the M4 Max is the only concrete performance figure offered, and it is unclear whether that reflects a single image, a short video segment, or a full generation run.

Interested users can pull the code from the GitHub repository and the weights from the Hugging Face page to test the build themselves. The project is new and community-shared rather than an official NVIDIA release, so expect rough edges and rapid iteration as others try the quantized model on their own hardware and report back with their own timings and quality comparisons.