Can I run DeepSeek V3.2 (MoE) locally?
DeepSeek V3.2 (MoE) has 685.36 billion parameters (Hugging Face). At the popular Q4_K_M quantization it needs roughly 437.2 GB of GPU memory ESTIMATE. It has been on Hugging Face since 2025-12-01. 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 | 133.6 GB | 141.2 GB |
| IQ2_XXS | 176.5 GB | 186.2 GB |
| IQ2_M | 231.3 GB | 243.8 GB |
| Q2_K | 271.6 GB | 286.0 GB |
| IQ3_XXS | 262.1 GB | 276.1 GB |
| Q3_K_S | 312.7 GB | 329.2 GB |
| IQ3_M | 313.6 GB | 330.1 GB |
| Q3_K_M | 342.7 GB | 360.7 GB |
| IQ4_XS | 364.1 GB | 383.2 GB |
| Q4_0 | 385.5 GB | 405.7 GB |
| Q4_K_M | 415.5 GB | 437.2 GB |
| MXFP4 | 396.6 GB | 417.4 GB |
| Q5_K_M | 487.5 GB | 512.7 GB |
| Q6_K | 562.0 GB | 591.0 GB |
| Q8_0 | 728.2 GB | 765.5 GB |
| F16 | 1370.7 GB | 1440.1 GB |
KV cache by context length ESTIMATE
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 4k | 0.29 GB | 0.15 GB | 0.08 GB |
| 8k | 0.58 GB | 0.31 GB | 0.16 GB |
| 16k | 1.15 GB | 0.61 GB | 0.32 GB |
| 32k | 2.30 GB | 1.22 GB | 0.65 GB |
| 64k | 4.61 GB | 2.45 GB | 1.30 GB |
| 128k | 9.21 GB | 4.89 GB | 2.59 GB |
From config.json: 61 KV-caching attention layers (MLA, 576-dim compressed cache). Max context 160k. 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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