Can I run GLM 5.3 (MoE) locally?
GLM 5.3 (MoE) has 753.33 billion parameters (Hugging Face). At the popular Q4_K_M quantization it needs roughly 480.5 GB of GPU memory ESTIMATE. It has been on Hugging Face since 2026-08-25. 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 | 146.9 GB | 155.2 GB |
| IQ2_XXS | 194.0 GB | 204.6 GB |
| IQ2_M | 254.2 GB | 267.9 GB |
| Q2_K | 298.5 GB | 314.4 GB |
| IQ3_XXS | 288.1 GB | 303.5 GB |
| Q3_K_S | 343.7 GB | 361.9 GB |
| IQ3_M | 344.6 GB | 362.8 GB |
| Q3_K_M | 376.7 GB | 396.5 GB |
| IQ4_XS | 400.2 GB | 421.2 GB |
| Q4_0 | 423.7 GB | 445.9 GB |
| Q4_K_M | 456.7 GB | 480.5 GB |
| MXFP4 | 436.0 GB | 458.8 GB |
| Q5_K_M | 535.8 GB | 563.6 GB |
| Q6_K | 617.7 GB | 649.6 GB |
| Q8_0 | 800.4 GB | 841.4 GB |
| F16 | 1506.7 GB | 1583.0 GB |
KV cache by context length ESTIMATE
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 4k | 0.37 GB | 0.20 GB | 0.10 GB |
| 8k | 0.74 GB | 0.39 GB | 0.21 GB |
| 16k | 1.47 GB | 0.78 GB | 0.41 GB |
| 32k | 2.94 GB | 1.56 GB | 0.83 GB |
| 64k | 5.89 GB | 3.13 GB | 1.66 GB |
| 128k | 11.78 GB | 6.26 GB | 3.31 GB |
From config.json: 78 KV-caching attention layers (MLA, 576-dim compressed cache). Max context 1024k. 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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