GLM-5.2-Int4-Int8Mix
GLM-5.2-Int4-Int8Mix is a quantized version of the GLM-5.2 large language model (785B parameters) optimized for vLLM inference. It uses mixed INT4/INT8 quantization to reduce model size from ~378GB while targeting minimal accuracy loss. Supports 1M-token context, long-horizon tasks, advanced coding, and tool use. Requires 8x H200 GPUs or equivalent for tensor-parallel deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | QuantTrio |
| Parameters | 785B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 60.2k |
| Likes | 7 |
| Last updated | 2026-06-22 |
| Source | QuantTrio/GLM-5.2-Int4-Int8Mix |
What GLM-5.2-Int4-Int8Mix is
Mixed quantization variant of GLM-5.2 (zai-org/GLM-5.2 base) using data-free quantization. Quantization policy: BF16 for first layer and attention components; W8A16 (group size 128) for layers 1–2 and layers 78+ (MTP); W4A16 (group size 128) for MoE expert weights in layers 3–77; FP32 for gate networks. Verified on vLLM 0.23.0 + Transformers 5.12.1 with 8x H200 + TP=8 + EP (expert parallel). Model card cites AIME25 pass@1 as lightweight reference only, not formal benchmark. Supports speculative decoding via MTP block. KV-cache set to FP8. Default reasoning_effort=medium-high (tunable per-request).
Run GLM-5.2-Int4-Int8Mix locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="QuantTrio/GLM-5.2-Int4-Int8Mix")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: 8x H200 (80GB HBM each, 640GB aggregate) for recommended tensor-parallel=8 + expert-parallel deployment. Model files total 378GB; KV-cache set to FP8 (reduces memory per token vs. FP16/BF16). Requires CUDA 12.1+, NVIDIA NCCL. Startup and compilation may take several minutes on first launch. GPU memory utilization target: 0.90 per vLLM startup command. Single-node 8x H200 verified; multi-node scaling not documented.
No LoRA/QLoRA feasibility stated in card. Model is a post-quantization artifact; full training/fine-tuning details unknown. Base model (zai-org/GLM-5.2) may support adaptation, but this quantized variant is not explicitly positioned for downstream fine-tuning. Requires independent testing.
When to avoid it — and what to weigh
- Single-GPU or <100GB VRAM deployments — Model card explicitly targets 8x H200 (640GB aggregate HBM). Smaller setups will require model sharding/offloading not documented here.
- Non-vLLM inference runtimes as primary target — Model explicitly states 'This release is prepared for vLLM' and does not claim compatibility with SGLang or other runtimes. Use via Transformers/KTransformers may work but is not the primary optimization target.
- Strict latency-critical systems without reasoning_effort tuning — Default reasoning_effort=medium-high increases thinking tokens; max setting trades further latency for accuracy. If sub-100ms latency is required, this model may not fit without extensive engineering.
- When full model transparency or RLHF training audit is required — No calibration dataset disclosed; quantization is data-free. No training methodology details provided beyond technical report references (arxiv:2602.15763, arxiv:2603.12201).
License & commercial use
MIT license (permissive, OSI-approved). No regional restrictions, no gating, open source weights. Allows commercial and private use with attribution requirement.
MIT permits commercial use without restriction, provided attribution is included and license text is distributed. No commercial support, SLA, or liability warranties stated. Deployment costs are hardware-only (no licensing fees). Validate internal IP/compliance policies independently, as model may contain training data overlap with proprietary sources (not disclosed in card).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model weights are publicly available and unauthenticated (not gated); no attestation of training data sources or content filtering. Inference via vLLM in trusted network recommended. Quantization reduces attack surface (smaller model file) but does not eliminate risks from adversarial inputs or prompt injection. Code execution risk via tool-calling and MCP integration depends on deployment isolation and input validation. No formal security audit disclosed.
Alternatives to consider
GPT-4 / GPT-4 Turbo (OpenAI)
Closed-source, proprietary inference, no hardware constraint. Trade: higher latency, API dependency, per-token cost, no local control.
Claude 3.5 Sonnet (Anthropic)
Comparable reasoning benchmarks (HLE, AIME), strong coding. Trade: API-only, no open weights, higher cost per token.
DeepSeek-V4-Pro (DeepSeek)
Competitive long-context, coding, and reasoning benchmarks; some quantized variants available. Trade: Chinese developer, regional availability concerns, less documented quantization.
Ship GLM-5.2-Int4-Int8Mix with senior software developers
GLM-5.2-Int4-Int8Mix combines enterprise reasoning with cost-efficient quantization. Verify hardware fit (8x H200+), test vLLM integration, and validate accuracy on your benchmark. Explore our private-LLM and custom-app services to accelerate go-live.
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GLM-5.2-Int4-Int8Mix FAQ
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Custom software development services
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If GLM-5.2-Int4-Int8Mix is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to deploy a production open-source LLM?
GLM-5.2-Int4-Int8Mix combines enterprise reasoning with cost-efficient quantization. Verify hardware fit (8x H200+), test vLLM integration, and validate accuracy on your benchmark. Explore our private-LLM and custom-app services to accelerate go-live.