GLM-5.2-NVFP4
GLM-5.2-NVFP4 is a 744B-parameter Mixture-of-Experts language model quantized to 4-bit precision using NVIDIA's tooling. It uses only 40B active parameters per token through sparse expert selection, making it memory-efficient for inference on high-end GPUs. Supports English and Chinese, with quantization applied only to expert weights while attention and dense layers remain in higher precision.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | lukealonso |
| Parameters | 432B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 89.6k |
| Likes | 29 |
| Last updated | 2026-07-06 |
| Source | lukealonso/GLM-5.2-NVFP4 |
What GLM-5.2-NVFP4 is
744B-parameter MoE architecture with 256 experts per layer (8 activated per token) and DeepSeek Sparse Attention. Quantized via NVIDIA Model Optimizer to NVFP4 (4-bit blockwise FP8 scales per 16 elements) using natural top-k routing calibration across three passes: coding, broad conversational, and long-context (>8K tokens). Non-shared MoE expert MLPs quantized; attention/dense layers and shared experts remain BF16.
Run GLM-5.2-NVFP4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="lukealonso/GLM-5.2-NVFP4")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
Minimum: 8x RTX 6000 Blackwell 96GB GPUs (per model card). Estimated VRAM per GPU: ~12-16 GB active working set with 40B active parameters + attention in BF16; quantized experts in NVFP4 reduce static footprint significantly. Exact per-GPU allocation unknown; verify with model author. Recommended: b12x MoE runner setup (specific configuration unknown, requires review of linked repository).
No explicit fine-tuning guidance in model card. MoE architecture and quantization present challenges: expert routing must be preserved, and NVFP4 quantization may constrain gradient flow. LoRA/QLoRA feasibility is unknown. Recommend consulting NVIDIA Model Optimizer documentation and contacting lukealonso or zai-org before attempting fine-tuning workflows.
When to avoid it — and what to weigh
- Single-GPU or consumer hardware targets — Requires minimum 8x RTX 6000 Blackwell 96GB GPUs. Incompatible with consumer GPUs, single A100s, or laptop/edge deployment. If constrained hardware is required, consider smaller quantized models.
- Real-time, ultra-low-latency inference — MoE expert routing and sparse attention add routing overhead. May not meet sub-100ms SLA requirements. Batch inference and offline processing are better fits.
- Fine-tuning at scale without specialized tooling — No explicit LoRA or QLoRA guidance provided. Fine-tuning an MoE model of this scale requires careful expert routing preservation and likely custom training infrastructure.
- Minimal model uncertainty or interpretability needs — Sparse expert selection and quantization reduce transparency into model internals. Not suitable for high-stakes domains requiring explainability without additional instrumentation.
License & commercial use
MIT license (OSI-approved, permissive). Permits commercial use, modification, and redistribution with attribution. No gating or commercial restrictions stated.
MIT license permits commercial use provided attribution is retained. No enterprise licensing, support, or indemnification terms mentioned. Model is quantized from zai-org/GLM-5.2 base model (license of base model not confirmed in excerpt; verify zai-org's terms independently). Recommend legal review of any derivative licensing or commercial deployment obligations.
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 | Good |
| Assessment confidence | Medium |
Quantized model reduces attack surface vs. full-precision, but quantization itself is not a security control. Model accepts tool-calling and function-calling tasks; validate external tool invocations. Calibration used natural routing rather than adversarial inputs; no mention of robustness to prompt injection or jailbreak attempts. Deployed on private hardware mitigates inference-time data exposure. Recommend security review of calibration data (WildChat, LMSYS-Chat contain user-generated content) and inference logging/filtering in production.
Alternatives to consider
Meta Llama 3.1 405B (full or quantized via GGUF/llama.cpp)
Dense 405B model with broader community support, llama.cpp compatibility, and lower hardware floor. Trade-off: no sparse routing; less efficient per token but simpler deployment.
DeepSeek V2 or DeepSeek-Coder
Also uses MoE with sparse routing and DeepSeek Sparse Attention; native from authors. May have better documentation and community tooling. Check hardware/quantization trade-offs.
Mixtral 8x22B (quantized to 4-bit or 8-bit)
Smaller MoE model (176B) with established quantization paths (GGUF, AWQ). Lower hardware barrier and better tool/framework support, though less capable than GLM-5.2 at scale.
Ship GLM-5.2-NVFP4 with senior software developers
Assess your hardware readiness (8x RTX 6000 Blackwell 96GB minimum), validate licensing terms with legal, and prototype on the community testing setup. Contact Devco for infrastructure planning and custom LLM integration support.
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GLM-5.2-NVFP4 FAQ
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What is the minimum GPU setup required?
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Work with a software development agency
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Ready to Deploy GLM-5.2-NVFP4?
Assess your hardware readiness (8x RTX 6000 Blackwell 96GB minimum), validate licensing terms with legal, and prototype on the community testing setup. Contact Devco for infrastructure planning and custom LLM integration support.