---

license: mit

base_model: zai-org/GLM-5.2

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• llama.cpp
• moe
• glm
• reap
• pruned

---

GLM-5.2-REAP50-Q3_K_M-GGUF

A GGUF build of GLM-5.2, REAP expert-pruned (50%) and quantized to Q3_K_M (~169 GB) — sized to run on 2× 96 GB GPUs (e.g. RTX PRO 6000), ~192 GB VRAM, with room for context.

What this is

• Base: zai-org/GLM-5.2 (glm_moe_dsa, ~753B MoE).
• REAP-50: the 128 most-salient experts per layer kept (of 256) via Cerebras REAP saliency (gate × ‖expert_output‖), MTP layer dropped → ~394B params.
• Quantized to Q3_K_M, split into 5 shards (~45 GB each).
• Runs as full MLA attention (the DSA lightning-indexer is not used at inference — same simplification as the upstream conversion).

⚠️ Requires a patched llama.cpp (for now)

Stock llama.cpp can't load any GLM-5.2 GGUF yet: its GLM-DSA loader requires the DSA indexer tensors on every layer, but GLM-5.2 only ships them on a subset ("full") of layers → missing tensor 'blk.N.indexer.k_norm.weight'. The indexer is loaded-but-unused (the graph is DeepSeek-V2 MLA), so the fix is simply to make those tensors optional.

Apply the included llama.cpp-glm-dsa-indexer-optional.patch (src/models/glm-dsa.cpp) and rebuild, or wait for the upstream GLM-DSA runtime PR. After patching it loads and runs normally.

# in a recent llama.cpp checkout:
git apply llama.cpp-glm-dsa-indexer-optional.patch
cmake -B build -DGGML_CUDA=ON && cmake --build build -j
./build/bin/llama-cli -m GLM-5.2-REAP50-Q3_K_M-00001-of-00005.gguf --jinja -ngl 99 -p "..."

Quality caveat

This is the most aggressive variant: REAP-50 (~+37.5% perplexity vs full GLM-5.2) compounded with Q3_K 3-bit quant. It generates coherently (chain-of-thought intact, correct simple code) but is not a quality champion — it's the "fits 192 GB and runs fast" option. For higher quality at a larger footprint, see the MLX REAP-25 (+2.3% PPL) or the full GLM-5.2 ladder under pipenetwork.

Smoke-tested on Apple Metal (~17 tok/s); not tested on CUDA/RTX 6000.