No — not on this device
GLM-5.2 744B-A40B at Q4_K_M needs 424.7 GB against 204.8 GB usable, and the shortfall of 219.9 GB is more than 32 GB of system RAM can cover at a tolerable speed. A smaller sibling or a lower quantisation is the honest answer here.
The strongest all-round open-weight model of mid-2026 on most public boards. Listed as a ceiling: 512 GB of unified memory at Q4.
What hardware do I need for GLM-5.2 744B-A40B? →
Fits instead: GLM-4.7-Flash 30B-A3B (18.6 GB)
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 745.1 GB | 746.4 GB | — | ~1.4 | −0.1% ppl | 541.6 GB over |
| Q6_K | 575.1 GB | 576.3 GB | — | ~1.9 | −0.4% ppl | 371.5 GB over |
| Q5_K_M | 497.0 GB | 498.3 GB | — | ~2.2 | −0.8% ppl | 293.5 GB over |
| Q4_K_M | 423.4 GB | 424.7 GB | — | ~2.5 | −1.9% ppl | 219.9 GB over |
| Q3_K_M | 342.8 GB | 344.0 GB | — | ~3.1 | −5.4% ppl | 139.2 GB over |
| Q2_K | 293.7 GB | 294.9 GB | — | ~3.7 | −15% ppl | 90.1 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model uses multi-head latent attention, so its cache is a compressed latent rather than full K and V.
How to run it
$ llama-server \
-hf zai-org/GLM-5.2:Q4_K_M \
-c 8192 -ngl 37
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.