Search models that run on 64 GB

8 models with published weights that fit in 64 GB — a workstation. Counted at four-bit quantisation, weights only, leaving the machine about a third of its memory and a gigabyte for context: room for roughly 67 billion parameters. A mixture of experts is counted in full, because it is held in full even though only a few experts compute.

A workstation, or a well-specified Mac. Seventy-billion-parameter models fit at four-bit, which is the class where local output stops being obviously worse than the hosted models people pay for. Loading takes real time and the machine will be warm.

Models, tools and agents that go and look something up — the open web, a set of documents, a grounded answer with citations. Pricing is usually per call or per result rather than per token, so the cost follows how often you ask. What differs is freshness, how much of each page you get back, and whether you are handed sources you can check or a summary you must trust.

• ERNIE-4.5-VL-28B-A3B-ThinkingBaidu28.0B≈18.2 GB at 4-bit1 also selling it hosted
• Gemma-3-R1984-12BVIDraft12.2B≈7.9 GB at 4-bit
• Molmo2-8BAllen Institute for AI (Ai2)8.7B≈5.6 GB at 4-bit
• Qwen2.5-Coder-7BAlibaba7.0B≈4.5 GB at 4-bit2 also selling it hosted
• Jan-nanoMenlo Research4.0B≈2.6 GB at 4-bit
• LocateAnything-3BNVIDIA3.8B≈2.5 GB at 4-bit
• Isaac 0.2 2B PreviewPerceptron2.0B≈1.3 GB at 4-bit1 also selling it hosted
• Isaac 0.2 1BPerceptron1.0B≈0.7 GB at 4-bit1 also selling it hosted

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