Can I run Qwen3 Coder Next (MoE) locally?
Qwen3 Coder Next (MoE) has 79.67 billion parameters (Hugging Face). At the popular Q4_K_M quantization it needs roughly 51.4 GB of GPU memory ESTIMATE. It has been on Hugging Face since 2026-01-30. 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 | 15.5 GB | 17.0 GB |
| IQ2_XXS | 20.5 GB | 22.2 GB |
| IQ2_M | 26.9 GB | 28.9 GB |
| Q2_K | 31.6 GB | 33.9 GB |
| IQ3_XXS | 30.5 GB | 32.7 GB |
| Q3_K_S | 36.4 GB | 38.9 GB |
| IQ3_M | 36.5 GB | 39.0 GB |
| Q3_K_M | 39.8 GB | 42.5 GB |
| IQ4_XS | 42.3 GB | 45.1 GB |
| Q4_0 | 44.8 GB | 47.8 GB |
| Q4_K_M | 48.3 GB | 51.4 GB |
| MXFP4 | 46.1 GB | 49.1 GB |
| Q5_K_M | 56.7 GB | 60.2 GB |
| Q6_K | 65.3 GB | 69.3 GB |
| Q8_0 | 84.7 GB | 89.6 GB |
| F16 | 159.3 GB | 168.0 GB |
KV cache by context length ESTIMATE
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 4k | 0.10 GB | 0.05 GB | 0.03 GB |
| 8k | 0.20 GB | 0.11 GB | 0.06 GB |
| 16k | 0.40 GB | 0.21 GB | 0.11 GB |
| 32k | 0.81 GB | 0.43 GB | 0.23 GB |
| 64k | 1.61 GB | 0.86 GB | 0.45 GB |
| 128k | 3.22 GB | 1.71 GB | 0.91 GB |
From config.json: 12 KV-caching attention layers, 2 KV heads × 256 head dim. Max context 256k. 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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