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Llama 3.3 70B Instruct (70B parameters) requires approximately 52.7 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 61 GB of VRAM.
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | — |
Q3_K_S | 3 | 34.3 GB | Low | — |
NVFP4 | 4 | 39.2 GB | Medium | — |
Q4_K_M | 4 | 42.7 GB | Medium | — |
Q5_K_M | 5 | 50.4 GB | High | — |
Q6_K | 6 | 57.4 GB | High | — |
Q8_0 | 8 | 74.9 GB | Very High | — |
F16 | 16 | 143.5 GB | Maximum | — |
Hardware compatibility
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Memory breakdown
Frequently asked questions
Llama 3.3 70B Instruct (70B parameters) requires approximately 52.7 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, MacBook Pro M3 Max 128GB can run Llama 3.3 70B Instruct with a compatibility score of 48/100. It provides 128 GB of memory and achieves approximately 5.6 tokens per second.
The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Llama 3.3 70B Instruct: NVIDIA H100 80GB (score: 56/100), NVIDIA H800 80GB (score: 55/100), NVIDIA GH200 96GB (score: 55/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Llama 3.3 70B Instruct is well-suited for chat. It was designed with these use cases in mind.
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