[!WARNING] This 361-GiB quant requires the fork-specific runtime linked below. It is not supported by an official ExLlamaV3 or upstream vLLM release. The published Docker image is the supported reproducibility path.

GLM-5.2 EXL3 4.0bpw HQ8 MUL1

Runtime and source

• GLM-5.2 vLLM EXL3 fork and Docker guide
• Cleaned EXLlamaV3 GLM-5.2 fork
• Published Docker image
• 3.1-bpw reference checkpoint

Quantization quality comparison

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Base model information

GLM-5.2

Introduction

• Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work
• Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency
• Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20%
• Pure Open: An MIT open-source license — no regional limits, technical access without borders

Benchmark

Serve GLM-5.2 Locally

• SGLang (v0.5.13.post1+) — see cookbook
• vLLM (v0.23.0+) — see recipes
• Transformers (v0.5.12+) — see transformers docs
• KTransformers (v0.5.12+) — see tutorial
• Unsloth (v0.1.47-beta+) — see guide
• For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here.

Footnote

• Humanity’s Last Exam (HLE) & other reasoning tasks: We use sampling parameters of temperature=1.0, top_p=0.95 for evaluation. We evaluate with a maximum generation length of 163,840 tokens. By default, we report the text-only subset; results marked with * are from the full set. For AIME, HMMT and IMOAnswerBench, we evaluate each question using the following system prompt: Your response should be in the following format:\nExplanation: {your explanation for your final answer}\nExact Answer: {your succinct, final answer}\nConfidence: {your confidence score between 0% and 100% for your answer}. We use GPT-5.5 (medium) as the judge model. For HLE-with-tools, we use a maximum context length of 300,000 tokens, with no context management strategy.
• SWE-Bench Pro: We run the SWE-Bench Pro suite with OpenHands using a tailored instruction prompt. Settings: temperature=1, top_p=1, max_new_tokens=32k, with a 400K context window.
• NL2Repo: We evaluated NL2Repo with temperature=1.0, top_p=1.0, and max_new_tokens=48k under 400k context. To prevent hacking, we use rule-based and a LLM-based judgement to prevent malicious behaviors (e.g., unauthorized pip or curl operations).
• DeepSWE: We run DeepSWE with the official pier evaluation framework and the mini-swe-agent harness (temperature=1.0, top_p=1.0, timeout=2h, 400K context). Each task is solved in an isolated container with 2 CPUs, 8 GB RAM, and no internet access.
• ProgramBench: We evaluate ProgramBench (200 instances) with Claude-Code 2.1.156 using temperature=1.0, top_p=1.0, max_tokens=64000, max_turns=2000, sample_timeout=6h, reasoning_effort=max, with a 400K context window. Each instance runs in a (4 CPUs, 8 GB RAM) sandbox with internet access disabled.
• Terminal-Bench 2.1 (Terminus 2): We evaluate Terminal-Bench 2.1 with Terminus-2 framework using parser=json, timeout=4h, temperature=1.0, top_p=1.0, max_new_tokens=48k, max_episodes=500, with a 256K context window. Resource limits are capped at 4 CPUs and 8 GB RAM.
• Terminal-Bench 2.1 (Claude Code): We evaluate in Claude Code 2.1.167 with temperature=1.0, top_p=0.95, max_new_tokens=131072. We override max_new_tokens to 128k via a transparent proxy, bypassing the 64k CLI cap to restore the configurability of CLAUDE_CODE_MAX_OUTPUT_TOKENS. We remove wall-clock time limits, while preserving per-task CPU and memory constraints. Scores are averaged over 5 runs.
• MCP-Atlas: All models were evaluated in think mode on the 500-task public subset with a 10-minute timeout per task. We use Gemini-3.0-Pro as the judge model for evaluation.
• Tool-Decathlon: We use the official evaluation service and set max_token to 128K.
• FrontierSWE: The evaluation was conducted by Proximal with 1M context length, max effort level, and 128K maximum output tokens. Dominance score reported as of 2026/06/16.
• PostTrainBench: The evaluation was conducted by PostTrainBench with 1M context length, max effort level, and 128K maximum output tokens.
• SWE-Marathon: The evaluation was conducted by Abundant AI with 1M context length, max effort level, and 128K maximum output tokens.

Citation