GLM-5.1-FP8
GLM-5.1-FP8 is a 754B-parameter open-source language model optimized for agentic and coding tasks. It achieves state-of-the-art performance on code-generation benchmarks (SWE-Bench Pro: 58.4%) and excels at long-horizon reasoning and tool use. The FP8 quantization reduces memory footprint while maintaining quality. Licensed under MIT with no gating, it is freely available for research and commercial use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | zai-org |
| Parameters | 753.9B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 849.3k |
| Likes | 118 |
| Last updated | 2026-04-16 |
| Source | zai-org/GLM-5.1-FP8 |
What GLM-5.1-FP8 is
GLM-5.1-FP8 is a mixture-of-experts (glm_moe_dsa) model with 753.9B parameters, quantized to FP8 precision. It supports both English and Chinese, is compatible with standard Hugging Face Transformers, and has been validated on endpoints. Context length is not specified. The model incorporates enhancements for sustained optimization over extended reasoning horizons and is optimized for agentic workflows involving repeated iteration and tool calls. Last updated 16 April 2026.
Run GLM-5.1-FP8 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="zai-org/GLM-5.1-FP8")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
Estimated 400–500 GB VRAM for FP8 inference (quantized from 754B parameters). Assumes efficient loading and batch size 1. Multi-GPU setups (e.g., 8× H100 or equivalent) strongly recommended. BF16 inference would require ~1.5 TB VRAM. Exact memory footprint should be validated against chosen serving framework (vLLM, SGLang).
Model card does not provide explicit guidance on fine-tuning, LoRA, or QLoRA feasibility. Mixture-of-experts architecture may complicate parameter-efficient tuning. Consult zai-org GitHub repository and framework-specific documentation (SGLang cookbook, vLLM recipes) for tuning examples. Community feedback suggests limited public fine-tuning guides; expect to adapt standard PEFT approaches or rely on framework tooling.
When to avoid it — and what to weigh
- Resource-Constrained Edge Deployment — At 754B parameters, even in FP8 quantization, this model requires significant VRAM (estimated 400–500 GB for inference). Not suitable for edge devices, mobile, or single-GPU consumer hardware.
- Real-Time, Low-Latency Applications — Large model size and mixture-of-experts routing introduce latency. Applications requiring sub-100ms response times should consider smaller alternatives.
- Production Without Operational Expertise — Requires careful deployment infrastructure (SGLang, vLLM, or equivalent). Teams without GPU cluster management and LLM serving experience will face significant operational overhead.
- Strict Context-Window Requirements — Context length is not specified in available documentation. If applications require defined, large context windows (>100K tokens), capability must be verified independently.
License & commercial use
Licensed under MIT. MIT is a permissive open-source license (OSI-approved) that permits use, modification, distribution, and commercial incorporation with minimal restrictions, provided the license and copyright notice are retained.
MIT license explicitly permits commercial use. GLM-5.1-FP8 may be used in commercial products, SaaS, and services without special permission. No gating or signup walls apply. However, as with any large open-source model, ensure your deployment, fine-tuning, and data handling comply with applicable laws (data privacy, export controls, etc.). No warranty or support guarantees are provided by the license; enterprise support or SLAs would need to be arranged separately via zai-org or third-party vendors.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, adversarial robustness data, or prompt-injection mitigations are documented. As a large agentic model designed for tool use and long-horizon reasoning, it may be susceptible to prompt injection, reward hacking, or unintended tool calls if not carefully sandboxed. Self-hosted deployment eliminates third-party data exposure but places full responsibility for input validation, rate limiting, and access control on the operator. Review zai-org GitHub and community discussions for known vulnerabilities or safety reports before production deployment.
Alternatives to consider
DeepSeek-V3.2
Similar scale and cost-efficiency; strong on coding tasks but lower SWE-Bench Pro score (vs. 58.4%). Smaller or less-mature agent capabilities. Consider if cost is priority.
Claude 3.5 Opus (Proprietary API)
Closed-source, managed inference. Slightly higher NL2Repo (49.8 vs. 42.7) and Terminal-Bench scores. No self-hosting complexity or VRAM overhead. Trade-off: vendor lock-in, per-token costs, data handling.
Qwen3.6-Plus
Smaller, potentially faster inference. Competitive on some benchmarks (HMMT Feb: 87.8 vs. 82.6 for GLM-5.1). Good alternative if latency or VRAM constraints are critical.
Ship GLM-5.1-FP8 with senior software developers
Start with SGLang or vLLM on your GPU cluster. Review the technical report (arxiv:2602.15763), verify context length and fine-tuning constraints for your use case, and engage the zai-org community on GitHub or Discord. Contact Devco for custom LLM application architecture and deployment support.
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GLM-5.1-FP8 FAQ
Can I use GLM-5.1-FP8 in a commercial product?
What GPU setup do I need to run this?
Does the model card specify a maximum context length?
Are there pre-built Docker images or cloud deployment templates?
Software developers & web developers for hire
Need help beyond evaluating GLM-5.1-FP8? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy GLM-5.1-FP8?
Start with SGLang or vLLM on your GPU cluster. Review the technical report (arxiv:2602.15763), verify context length and fine-tuning constraints for your use case, and engage the zai-org community on GitHub or Discord. Contact Devco for custom LLM application architecture and deployment support.