0xSero/GLM-5.3-569B-EXL3-3.0bpw

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GLM-5.3-569B — REAP keep-192 (EXL3 3.0bpw)

GLM-5.3 with 25% of its experts removed and the rest quantized to ~3-bit — 223 GB, so it runs on 3x 96 GB instead of the ~1.5 TB the full model needs, with a small, measured drop in fidelity.

What this is

GLM-5.3 is a 753B mixture-of-experts model: each token is routed to 8 of 256 expert sub-networks per layer, so only ~40B parameters are active at once. Most of those 256 experts are rarely the best choice. REAP (Router-weighted Expert Activation Pruning) scores how much each expert actually contributes and deletes the least useful ones outright — no retraining. This cut keeps 192 of 256 experts per layer. The survivors are then quantized to EXL3 3.0 bpw; attention, the shared expert, and the router stay full-precision BF16. The result loads in 223 GB and answers almost identically to the original.

Which size should I pick?

Every row is the same model at a different quality/size trade-off — fewer experts is smaller and faster but further from the original:

This cut fits 3x 96 GB. If you have the VRAM, move up the table — each step keeps more experts and lands closer to the original.

How close to the original is it?

KL divergence vs full BF16: 0.361 nats, measured token-by-token over a sealed 25-prompt panel across the full 154k-token vocabulary. KL divergence is the standard “how differently do these two models predict” score: 0 = identical, lower = closer. For scale: the unpruned base scores 0.089, and this cut still reproduces the full model’s single most-likely next token about 82% of the time.

All cuts are measured the same way in the fidelity study.

Why these experts (and not the popular ones)

Naive pruning keeps the experts that fire most often — which quietly deletes the specialists that a single domain (code, a rare language, structured output) leans on. This series instead scores each expert by its largest share of any one domain’s routed work (“max-over-domain”), so every domain keeps its specialists even when they’re globally rare. On the same panel this beats frequency-based pruning by a wide margin — the head-to-head is in the fidelity study.

Running it

pip install exllamav3        # or run via TabbyAPI
# download this repo, then tensor-split it across your GPUs in TabbyAPI/exllamav3

Built for Blackwell / any CUDA GPUs with enough combined VRAM (3x 96 GB).

Credits

• —[Z.AI / zai-org](https://huggingface.co/zai-org) — GLM-5.3, the base model.
• —[Cerebras Research](https://github.com/CerebrasResearch/reap) — REAP (arXiv:2510.13999).
• —[turboderp / exllamav3](https://github.com/turboderp-org/exllamav3) — the EXL3 format and runtime.

Observations: `glm-5.3-reap-observations-v1` · Fidelity study: `glm-5.3-reap-fidelity-study` · Built on 8× NVIDIA RTX PRO 6000 Blackwell.

License

Inherits the GLM-5.3 license.