# GLM-5.2: Self-Hosting Hardware, Scenarios and Commercial License

> Long-horizon software engineering, tool use and million-token analysis on enterprise infrastructure.

- Verified: 2026-09-12
- Released: 2026-07-12
- Canonical: https://chinaapi.ai/open-model-deployment/glm-5.2/
- Back to directory: https://chinaapi.ai/open-model-deployment/
- Machine-readable dataset: https://chinaapi.ai/data/open-model-deployment.json

## Model facts

- Vendor: Z.ai
- Parameters: 753B total; not independently verified in V1 active
- Native precision: BF16 and official FP8 checkpoint
- Context: 1M tokens
- Category: General and agentic foundation models
- Modalities: text → text
- Serving paths: vLLM, SGLang, xLLM, KTransformers
- Official weights: https://huggingface.co/zai-org/GLM-5.2?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- Model documentation: https://github.com/zai-org/GLM-5?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment

## Deployment evidence

- **Official launch / minimum — evidence B:** KTransformers documents an 8-GPU CPU/GPU-offload launch shape with 96 CPU inference threads; this is a tutorial target, not a claimed absolute minimum. Source: https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/GLM-5.2-Tutorial.md?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- **200-person team — evidence E:** Start from a replicated TP8 service only after measuring the target context mix; no official 200-seat capacity claim exists.
- **Commercial API — evidence E:** Requires redundant replicas or disaggregated prefill/decode; GPU count cannot be inferred from seats alone.

## Scenario evidence

- **Long-horizon software engineering — Vendor-stated:** Official release materials emphasize coding-agent and tool-use workloads. Source: https://github.com/zai-org/GLM-5?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- **Million-token repository and document analysis — Vendor-stated:** The official checkpoint supports a 1M-token context window. Source: https://huggingface.co/zai-org/GLM-5.2?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- **Private enterprise agent service — ChinaAPI inference:** A credible fit when CPU/GPU offload or TP8 infrastructure is already available.

## Commercial-use check

- License: MIT — https://huggingface.co/zai-org/GLM-5.2/blob/main/LICENSE?utm_source=chinaapi&utm_medium=research&utm_campaign=open-model-deployment
- MaaS / hosted service: No model-specific MaaS restriction identified in the MIT license.
- Attribution: Retain the copyright and permission notice.

## Avoid or validate first

- Latency-sensitive single-GPU serving
- Capacity planning based only on employee count

## Known limitations and open questions

- The 8-GPU tutorial is not an official minimum claim
- No public V1 concurrency result

## Evidence boundary

Loading weights, completing a first forward pass and meeting a production latency/SLA are separate thresholds. The 200-person and commercial tiers require workload-specific measurement. License summaries are product research, not legal advice.

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