Open-Weight LLM · Private & Custom AI
GLM-5.1-NVFP4
Quantized 754B MoE model (40B active) for private deployment of agentic AI systems, RAG, and operational chatbots on NVIDIA infrastructure—data stays in your environment.
GLM-5.1-NVFP4 is a 4-bit quantized version of ZAI's GLM-5.1, a mixture-of-experts transformer optimized by NVIDIA for inference on Blackwell GPUs via SGLang or vLLM. It handles up to 200K context, tool calling, reasoning, and multilingual tasks. For ops teams, it's a ready-to-deploy foundation for building proprietary agents and automation systems without shipping data to third-party APIs.
Model facts
Private deployment
Run GLM-5.1-NVFP4 in your own environment
Self-hosted on NVIDIA B200/B300 GPUs running Linux + SGLang or vLLM. FP4 quantization cuts memory footprint vs. full precision; shared expert remains unquantized for stability. Data never leaves your infrastructure—critical for regulated industries (finance, healthcare, legal) and IP-sensitive workflows. Requires tensor parallelism (8+ GPUs typical) and CUDA stack expertise.
Operational AI use cases
Internal Support & Ticket Routing
Deploy as private chatbot for employee/customer support. Tool-calling enables ticket creation, knowledge base lookup, and escalation automation. 200K context handles full ticket history + docs. Data stays internal; no SaaS privacy concerns.
Code Review & SWE Task Automation
Nemotron-SWE-v2 training data tunes it for bug localization, test generation, and code repair. Use in CI/CD pipelines to auto-review PRs, suggest fixes, generate test cases—without exposing source code to external APIs.
Reasoning-Heavy Workflows (Finance/Science)
GPQA Diamond + AIME 2026 eval shows math/science reasoning. Route complex financial analysis, regulatory interpretation, or scientific literature synthesis through this model. Long context (200K) fits multi-page documents; reasoning parsers handle step-by-step logic.
Custom AI
As a base for custom AI
Strong foundation for internal AI products. 754B parameters + MoE architecture support fine-tuning on proprietary datasets (customer service tones, domain-specific terminology, internal processes). Tool-call and reasoning parsers baked in—extend with custom tools (APIs, databases, knowledge graphs). Quantization trade-off: lower latency & memory cost vs. slightly reduced accuracy on edge cases.
In the operating system
Where it fits
Sits at the **agent & workflow reasoning layer** of an AI OS. Takes structured inputs (user intent, tool definitions, context) and routes through reasoning+tool-calling parsers. Feeds into orchestration layer (state management, multi-step tasks) and knowledge layer (RAG integration via long context). Inference engine (SGLang/vLLM) is the compute substrate.
Data control & security
Self-hosting means no data transmission to third-party model providers—only to your own GPU cluster. Architecture choice, not a model property: you control access logs, request/response data, fine-tuning datasets. Compliance (HIPAA, SOC 2, GDPR data residency) becomes an infrastructure problem, not a model licensing one. MIT license permits use in regulated contexts; however, responsibility for validation, monitoring, and drift detection remains with you.
Hardware footprint
**Estimate.** FP4 quantized: ~190–220 GB aggregate VRAM (8× H100 80GB or 4× B200 with NVLink). Full precision unquantized would require ~750+ GB. Active params (40B) dominate latency; MoE sparsity reduces compute vs. dense 754B. vLLM / SGLang auto-tune memory; test with your workload profile before production.
Integration
Expose via OpenAI-compatible API (vLLM + SGLang both support this) or custom REST/gRPC endpoints. Feed tool definitions as JSON (standard glm47 parser); integrate with workflow orchestration (Airflow, n8n), knowledge stores (vector DBs, file systems), and business systems (CRM, ticketing, ERP) via API calls. Requires Docker/K8s for reproducibility; tensor parallelism setup is non-trivial—plan for MLOps/infra involvement.
When it's not the right fit
- —You need sub-10ms latency on consumer GPUs—MoE + quantization add router overhead and require tensor parallelism on expensive hardware.
- —Closed-form compliance & certifications are required—model is general-purpose; you must validate safety, bias, hallucination patterns for your use case (model card notes V-model testing is customer's responsibility).
- —Your org lacks GPU infrastructure & MLOps expertise—setup, tuning, and monitoring require in-house or partner support; no managed SaaS alternative from NVIDIA.
- —You need state-of-the-art accuracy on out-of-domain tasks—quantization + mixture-of-experts trade some performance for speed; benchmark your data before committing.
Alternatives to consider
Meta Llama 3.1 405B
Larger, denser, unquantized; better for accuracy-critical tasks but higher memory footprint; also fully open (Apache 2.0), but no built-in reasoning/tool-call parsers.
Mistral Nemo 12B / Large
Much smaller, easier to run on modest GPUs; good for cost-conscious deployments and fine-tuning; weaker reasoning and tool-use out of box.
Deepseek-V3
Competing MoE, strong reasoning; different quantization strategy and inference framework; comparable private-deployment story if Deepseek's license & framework support your stack.
FAQ
Can I run this on my own hardware and keep all data private?
Yes, that is the primary use case. Deploy on your own NVIDIA GPU cluster running Linux + SGLang or vLLM. All inference, fine-tuning, and data stays in your environment. You control backup, logging, and compliance.
Is this licensed for commercial use?
Yes. MIT license permits commercial and non-commercial use without royalties. However, if you fine-tune it on proprietary data or integrate it into a product, test for safety/bias and document your validation—MIT does not waive responsibility for downstream harms.
How does quantization affect accuracy?
FP4 quantization trades ~1–5% accuracy loss (varies by task) for 3–4× lower VRAM and faster inference. Evaluate on your actual workload before deployment. Model card shows eval results; your domain-specific data may see different trade-offs.
What's the difference between this and the base GLM-5.1?
This is the quantized-for-inference version optimized by NVIDIA Model Optimizer. Base GLM-5.1 is heavier and meant for research/fine-tuning. This is production-ready for high-throughput inference on Blackwell GPUs.
Build a Private, Proprietary AI System
GLM-5.1-NVFP4 is a foundation. LLM.co helps you integrate it into your ops—fine-tune on company data, wire it into workflows, and ship a custom AI product that stays in your environment. Let's design your deployment.