Can I run Llama 3.3 70B Instruct locally?
Llama 3.3 70B Instruct has 70.55 billion parameters (Hugging Face). At the popular Q4_K_M quantization it needs roughly 46.9 GB of GPU memory ESTIMATE. It has been on Hugging Face since 2024-11-26. This is an older model; see the current list for newer options.
Memory needed by quantization
| Quant | Weights | Total needed (est.) |
|---|---|---|
| IQ1_S | 13.8 GB | 16.4 GB |
| IQ2_XXS | 18.2 GB | 21.0 GB |
| IQ2_M | 23.8 GB | 26.9 GB |
| Q2_K | 28.0 GB | 31.3 GB |
| IQ3_XXS | 27.0 GB | 30.3 GB |
| Q3_K_S | 32.2 GB | 35.7 GB |
| IQ3_M | 32.3 GB | 35.8 GB |
| Q3_K_M | 35.3 GB | 39.0 GB |
| IQ4_XS | 37.5 GB | 41.3 GB |
| Q4_0 | 39.7 GB | 43.6 GB |
| Q4_K_M | 42.8 GB | 46.9 GB |
| MXFP4 | 40.8 GB | 44.8 GB |
| Q5_K_M | 50.2 GB | 54.6 GB |
| Q6_K | 57.9 GB | 62.7 GB |
| Q8_0 | 75.0 GB | 80.7 GB |
| F16 | 141.1 GB | 150.1 GB |
KV cache by context length ESTIMATE
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 4k | 1.34 GB | 0.71 GB | 0.38 GB |
| 8k | 2.68 GB | 1.43 GB | 0.75 GB |
| 16k | 5.37 GB | 2.85 GB | 1.51 GB |
| 32k | 10.74 GB | 5.70 GB | 3.02 GB |
| 64k | 21.47 GB | 11.41 GB | 6.04 GB |
| 128k | 42.95 GB | 22.82 GB | 12.08 GB |
From config.json: 80 KV-caching attention layers, 8 KV heads × 128 head dim. Max context 128k. The tables above assume 4k context.
Which GPUs and Macs can run it?
✅ fits · ⚠️ fits with little headroom · ❌ doesn't fit on this one device (multiple GPUs or RAM offload may work; check your setup). Apple Silicon assumes macOS lets the GPU use ~67–75% of unified memory. All fit verdicts are estimates.
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