Can I run Llama 4 Scout 17B 16E (MoE) locally?
Llama 4 Scout 17B 16E (MoE) has 108.64 billion parameters (Hugging Face). At the popular Q4_K_M quantization it needs roughly 70.6 GB of GPU memory ESTIMATE. It has been on Hugging Face since 2025-04-02. It's a mixture-of-experts model: all weights must fit in memory, but it generates faster than a dense model of the same size.
Memory needed by quantization
| Quant | Weights | Total needed (est.) |
|---|---|---|
| IQ1_S | 21.2 GB | 23.6 GB |
| IQ2_XXS | 28.0 GB | 30.8 GB |
| IQ2_M | 36.7 GB | 39.9 GB |
| Q2_K | 43.0 GB | 46.6 GB |
| IQ3_XXS | 41.6 GB | 45.0 GB |
| Q3_K_S | 49.6 GB | 53.5 GB |
| IQ3_M | 49.7 GB | 53.6 GB |
| Q3_K_M | 54.3 GB | 58.4 GB |
| IQ4_XS | 57.7 GB | 62.0 GB |
| Q4_0 | 61.1 GB | 65.6 GB |
| Q4_K_M | 65.9 GB | 70.6 GB |
| MXFP4 | 62.9 GB | 67.4 GB |
| Q5_K_M | 77.3 GB | 82.5 GB |
| Q6_K | 89.1 GB | 94.9 GB |
| Q8_0 | 115.4 GB | 122.6 GB |
| F16 | 217.3 GB | 229.6 GB |
KV cache by context length ESTIMATE
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 4k | 0.81 GB | 0.43 GB | 0.23 GB |
| 8k | 1.61 GB | 0.86 GB | 0.45 GB |
| 16k | 3.22 GB | 1.71 GB | 0.91 GB |
| 32k | 6.44 GB | 3.42 GB | 1.81 GB |
| 64k | 12.88 GB | 6.85 GB | 3.62 GB |
| 128k | 25.77 GB | 13.69 GB | 7.25 GB |
From config.json: 48 KV-caching attention layers, 8 KV heads × 128 head dim. Max context 10240k. 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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