Can I Run GPT-OSS 120B on a Apple M3 Ultra (512GB)?
Runs at full precision (fp16). Zero quality loss.
5 quantizations fit your 512GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 241.0 GB | 242.5 GB | 240.0 GB | +271.0 GB |
| Q8_0 | 128.5 GB | 130.0 GB | 127.5 GB | +383.5 GB |
| Q6_K | 99.8 GB | 101.3 GB | 98.8 GB | +412.1 GB |
| Q5_K_M | 86.2 GB | 87.7 GB | 85.2 GB | +425.8 GB |
| Q4_K_M | 73.8 GB | 75.3 GB | 72.8 GB | +438.3 GB |
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Top-ranked open-source models that fit in 512GB.
FAQ
Can the Apple M3 Ultra (512GB) run GPT-OSS 120B?
Yes. The Apple M3 Ultra (512GB)'s 512GB of unified memory is enough to run GPT-OSS 120B at fp16 quantization (241.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 512GB. It uses about 241.0GB of memory and 242.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.