Open-Weight LLM · Private & Custom AI
GLM-5
744B MoE model for agentic reasoning, code, and complex operational automation—built for private deployment and tool-use workflows.
GLM-5 is a 744B-parameter sparse mixture-of-experts model (40B active) trained on 28.5T tokens, optimized for reasoning, coding, and multi-step agentic tasks. For ops teams, it's a strong candidate for self-hosted deployment of intelligent workflow automation, internal tooling, and custom agents—without relying on external APIs. The model integrates DeepSeek Sparse Attention to cut memory footprint while preserving long-context capacity.
Model facts
Private deployment
Run GLM-5 in your own environment
GLM-5 is deployment-ready across vLLM, SGLang, KTransformers, and Transformers. Self-hosting requires tensor parallelism (8 GPUs suggested in examples) and supports speculative decoding for inference speed. Running privately means your operational data—customer interactions, internal processes, codebase context—stays within your infrastructure; no third-party API calls or data residency concerns. Trade-off: you own the hardware and inference stack.
Operational AI use cases
Agentic Internal Ops Automation
Deploy GLM-5 as the backbone of an autonomous agent system that handles multi-step operational tasks: ticket triage and resolution, cross-system data lookup, and workflow orchestration. The model's strong reasoning and tool-use performance (HLE 30.5, with tools 50.4) makes it suitable for complex decision trees in support, finance approvals, and process automation.
Internal Code Review & Engineering Tooling
Build a self-hosted code assistant for your engineering team. GLM-5 scores 77.8 on SWE-bench Verified, making it viable for PR review automation, refactoring suggestions, and codebase-aware documentation. Since it runs privately, your proprietary source code never leaves your servers.
Knowledge Synthesis & Internal Search
Use GLM-5 to power a private RAG system that synthesizes answers from internal docs, runbooks, and product specs. The 200K+ context window (DeepSeek Sparse Attention) lets it handle large document sets in a single inference pass, enabling faster onboarding, compliance Q&A, and reduced support ticket volume.
Custom AI
As a base for custom AI
GLM-5 is a strong foundation for building domain-specific AI products and internal applications. Its dense reasoning capability and tool-integration support make it suitable as the core of custom workflows: e-commerce agents, internal analytics bots, or vertical-specific reasoning systems. You can fine-tune or prompt-engineer for your exact workflow without sending data to external providers. Start with inference; add LoRA/QLoRA for task-specific adaptation if needed.
In the operating system
Where it fits
In an LLM.co-style AI OS, GLM-5 operates at the **agent and workflow layer**: reasoning engine for multi-step tasks, decision-maker in operational pipelines, and base for custom tool-augmented applications. It sits above the data/knowledge layer (RAG) and feeds into workflow orchestration (task scheduling, approval chains). For simple retrieval or classification, smaller/faster models may be better; GLM-5 is where you need extended reasoning or complex task chaining.
Data control & security
Self-hosting GLM-5 ensures operational and customer data remain in your infrastructure—no third-party inference calls, no data residency compliance questions. This is an **architecture choice**, not a guarantee: you remain responsible for infrastructure security, access controls, and model behavior. The MIT license permits private deployment; data security depends entirely on your environment hardening and operational practices.
Hardware footprint
**Estimate (unverified):** ~1.5 TB VRAM for fp32 full model; ~750 GB for bfloat16; ~375 GB for int8. With MoE (40B active), inference memory is lower (~200–300 GB bfloat16 with KV cache, 8-GPU tensor parallelism). Actual footprint depends on context length, batch size, and quantization. Recommend 4–8 H100/A100 for production inference.
Integration
GLM-5 integrates via OpenAI-compatible APIs (vLLM/SGLang), Hugging Face Transformers, and custom inference frameworks. Wire it into existing ops stacks via REST endpoints (e.g., vLLM serving on port 8000), or call it directly from Python for in-process reasoning. Tool-calling and reasoning parsing are built-in (glm47 tool-call parser, glm45 reasoning parser). Pair with workflow engines (Temporal, Airflow) for multi-step orchestration. For internal RAG, integrate with vector DBs (Weaviate, Milvus) or in-memory stores.
When it's not the right fit
- —Real-time latency is critical: GLM-5's size and long reasoning chains incur inference delays unsuitable for sub-100ms SLA workflows.
- —Token throughput is the priority: MoE efficiency is good, but raw tokens/sec may lag behind dense models on high-concurrency inference.
- —You need a simple, lightweight classifier or retrieval system: over-engineered; use a smaller, faster model (Mistral, Phi) instead.
- —Your ops team lacks GPU/ML infrastructure: deployment complexity and hardware cost may outweigh the benefit for smaller organizations.
Alternatives to consider
DeepSeek-V3
Similar 671B MoE scale, strong reasoning (92.7 AIME), fewer deployment frameworks supported. Slightly lower reasoning/agentic scores but comparable code performance.
Llama 3.3 (405B)
Dense model, wider ecosystem support, simpler deployment. Trades some reasoning/agentic capability for lower memory footprint and faster inference. Better for latency-sensitive ops.
Mixtral 8x22B
Smaller MoE (~141B), dramatically lower memory/compute. Suitable for teams with limited hardware; acceptable performance on standard ops tasks, weaker on complex reasoning.
Related open models
FAQ
Can we run GLM-5 entirely on-premise without any external API calls?
Yes. Deploy it via vLLM, SGLang, or KTransformers on your own GPU infrastructure. All inference, tool-calling, and reasoning happens locally. You own the entire model and inference stack.
What is the commercial/licensing situation for internal use?
GLM-5 is MIT-licensed, which permits commercial use, modification, and redistribution with attribution. You can build and sell products using it. No special licensing needed for internal operational use.
How much can we expect to save by hosting GLM-5 privately vs. using an API?
Depends on inference volume and your cloud costs. At scale (millions of tokens/month), self-hosting typically becomes cheaper than API pricing, but upfront GPU/infrastructure investment is high. Small teams may prefer API-first until volume justifies hardware.
What's the context window, and can we use it for long-document RAG?
Context length is not explicitly stated in the model card; benchmarks reference 200K+ windows in some evaluations. Requires testing; check HF model config or consult the technical blog. DeepSeek Sparse Attention supports long contexts at lower cost.
Build Your Private Ops AI Stack with GLM-5
GLM-5 is ready to power autonomous workflows, internal tooling, and custom AI—all running in your own infrastructure. Let LLM.co help you architect the deployment, integrate with your ops layer, and fine-tune for your domain. Talk to us about building your AI operating system.