baa-ai/GLM-5.2-RAM-307GB-GGUF

GLM-5.2 — 307GB (GGUF)

Mixed-precision quantized version of zai-org/GLM-5.2 using a proprietary quantization method by baa.ai.

Per-tensor bit-width allocation via advanced sensitivity analysis and budget-constrained optimisation — no calibration data required. Built at the efficiency knee (best quality-per-GB).

Metrics

Metric	Value
Size	307 GB (7 shards)
Average bits	3.50
Format	llama.cpp (GGUF)
Architecture	MoE (256 experts, MLA + sparse attention)

Routed experts Q3_K; attention Q4_K; DSA indexer / shared experts Q6_K–Q8_0; first/last-layer and protected tensors F16; output and token-embeddings Q6_K.

Requirements

llama.cpp build ≥ b9820 is required — GLM-5.2's sparse-attention shared-indexer layout is only handled by recent builds.

Usage

brew install llama.cpp

hf download baa-ai/GLM-5.2-RAM-307GB-GGUF --include "*.gguf" --local-dir ./glm-5.2-ram-307gb

# The model is split into 7 shards — point -m at the first; llama.cpp loads the rest automatically
llama-cli -m ./glm-5.2-ram-307gb/GLM-5.2-RAM-local-knee-00001-of-00007.gguf -p "Hello!" -n 256 -ngl 99

Or via llama-server for an OpenAI-compatible HTTP API:

llama-server -m ./glm-5.2-ram-307gb/GLM-5.2-RAM-local-knee-00001-of-00007.gguf --port 8080 -ngl 99 --ctx-size 8192

For fast inference use a host with ≥ ~310 GB RAM/VRAM; otherwise it runs via mmap paging. The chat template is embedded; GLM-5.2 supports a thinking mode. No importance matrix (imatrix) was used — per-tensor sensitivity allocation provides the primary quality signal.

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Quantized by baa.ai

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