GLM-5.1-NVFP4
GLM-5.1-NVFP4 is a quantized 754B-parameter mixture-of-experts language model from NVIDIA, optimized for inference on NVIDIA Blackwell GPUs. It uses 4-bit FP4 quantization to reduce memory footprint while maintaining reasoning, coding, and tool-use capabilities. The model is open-source under MIT license and requires enterprise GPU infrastructure (multi-GPU tensor parallelism) for deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | nvidia |
| Parameters | 381.5B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 84.6k |
| Likes | 39 |
| Last updated | 2026-05-27 |
| Source | nvidia/GLM-5.1-NVFP4 |
What GLM-5.1-NVFP4 is
GLM-5.1-NVFP4 is a post-training quantized variant of ZAI's GLM-5.1 base model, quantized using NVIDIA Model Optimizer v0.45.0. It employs FP4 quantization on transformer linear operators within MoE experts (shared expert remains unquantized). Supports 200K context length. Optimized for SGLang and vLLM inference engines on NVIDIA Blackwell hardware. Calibrated on Nemotron synthetic datasets spanning instruction-following, coding, math, science, and agentic tool-use. Evaluated on GPQA Diamond, SciCode, AIME 2026, IFBench, and AA-LCR benchmarks.
Run GLM-5.1-NVFP4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="nvidia/GLM-5.1-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
NVIDIA Blackwell GPUs (B300, B200 validated). Reference deployment uses --tensor-parallel-size 8. FP4 quantization reduces per-GPU VRAM vs. full precision; estimate 40–60 GB aggregate VRAM for serving with 8× parallelism (unconfirmed—requires benchmarking with target hardware). Linux OS required.
No fine-tuning guidance provided in model card. Quantized model (FP4) stability under gradient updates not addressed. LoRA/QLoRA feasibility unknown. Recommend consulting NVIDIA Model Optimizer documentation or running trial fine-tuning with small data subset to validate loss convergence and output quality.
When to avoid it — and what to weigh
- CPU-Only or Limited GPU Infrastructure — Model requires multi-GPU tensor parallelism (reference configs use --tensor-parallel-size 8) on NVIDIA Blackwell. No CPU inference path documented; requires substantial VRAM per GPU.
- Real-Time Low-Latency Applications — 754B total parameters (40B activated per token) with MoE routing overhead may not meet sub-100ms latency SLAs without aggressive batching trade-offs.
- Unsupported Hardware Ecosystems — Explicitly optimized for NVIDIA Blackwell only. No AMD, Intel Arc, or older NVIDIA generation support documented. Deployment on non-Blackwell hardware requires validation.
- Training or Fine-Tuning — Model card explicitly states 'We did not perform training or testing for this Model Optimizer release.' No LoRA/QLoRA guidance provided. Fine-tuning methodology and stability unknown.
License & commercial use
Licensed under MIT License. MIT is an OSI-approved permissive open-source license allowing commercial and non-commercial use with attribution.
Model card explicitly states: 'This model is ready for commercial/non-commercial use.' MIT license permits commercial deployment. However, model is third-party (ZAI's GLM-5.1), quantized by NVIDIA. Users should verify third-party base model license (GLM-5.1 license not provided in excerpt) and review NVIDIA Model Optimizer terms for quantization tooling.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
Quantized model reduces interpretability of weights vs. full precision. FP4 quantization may introduce subtle numerical artifacts in long reasoning chains; validate outputs in safety-critical applications. Model inherits security posture of base GLM-5.1 (properties unknown from excerpt). Requires Linux + trusted GPU infrastructure; no input sanitization or output filtering documented. Use of --trust-remote-code flag in serving commands requires code review.
Alternatives to consider
Meta Llama 3.1 405B (or smaller variants)
Open-source, larger adoption, broader ecosystem support (TGI, Ollama, llama.cpp). Comparable reasoning/coding, no NVIDIA-specific hardware lock-in.
Mistral Large or Mixtral 8x22B
Smaller MoE models, easier to deploy on commodity multi-GPU setups. Permissive license (Apache 2.0), strong math/code benchmarks.
Claude 3.5 Sonnet (Anthropic, API-based)
No infrastructure cost; managed inference with strong tool-use and long-context (200K) capability. Trade-off: vendor lock-in, per-token pricing.
Ship GLM-5.1-NVFP4 with senior software developers
Evaluate this model on your Blackwell infrastructure using SGLang or vLLM. Test long-context reasoning, tool-use, and code generation against your benchmarks. Contact our AI engineering team to architect a multi-GPU serving pipeline.
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GLM-5.1-NVFP4 FAQ
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What GPU hardware do I need?
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Software developers & web developers for hire
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-5.1-NVFP4 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy GLM-5.1-NVFP4?
Evaluate this model on your Blackwell infrastructure using SGLang or vLLM. Test long-context reasoning, tool-use, and code generation against your benchmarks. Contact our AI engineering team to architect a multi-GPU serving pipeline.