Home / Can I run it? / Llama 3.3 70B / Llama 3.3 70B on a Mac Studio 512GB

Yes. Llama 3.3 70B at FP16 needs about 163.3 GB, and the Mac Studio M5 Ultra 512GB has 384.0 GB usable at a ~4k context.

Can I run Llama 3.3 70B on a Mac Studio 512GB?

Llama 3.3 70B at FP16 needs about 163.3 GB, and the Mac Studio M5 Ultra 512GB has 384.0 GB usable. That figure is 512 GB of unified memory on the spec sheet, of which 384.0 GB is usable for weights and cache, at 1200 GB/s memory bandwidth. Meta’s flagship 70B instruct model, the usual target for a high-end local chat box. These are estimates from our formula, not measured benchmarks.

Quant that fits

FP16 Fits with headroom

Memory needed

163.3 GB at FP16, ~4k context

Usable unified memory

384.0 GB of 512 GB unified memory

Est. tokens/sec

4–7 estimate

Llama-3.3-70B-Instruct · Mac Studio M5 Ultra 512GB · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 4–7 tokens/sec at FP16. This is a bandwidth-derived range, not a bench. Efficiency is a 45–80% unified-memory band from How we calculate.

Context headroom

At FP16, this machine has headroom out to about 128k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

• See the best GPU for this model family and the Llama-3.3-70B-Instruct model page.

Other hardware for Llama 3.3 70B

• Llama 3.3 70B on an RTX 3060 12GBWon’t fit
• Llama 3.3 70B on an RTX 3070 TiWon’t fit
• Llama 3.3 70B on an RTX 3080Won’t fit
• Llama 3.3 70B on an RTX 3090Won’t fit
• Llama 3.3 70B on an RTX 4060 Ti 16GBWon’t fit
• Llama 3.3 70B on an RTX 4070 Ti SuperWon’t fit

Other models on a Mac Studio 512GB

• gpt-oss-120b on a Mac Studio 512GBFits
• Qwen3-235B on a Mac Studio 512GBFits
• DeepSeek-R1 on a Mac Studio 512GBWon’t fit
• DeepSeek-V3.2 on a Mac Studio 512GBWon’t fit