Open-Weight LLM · Private & Custom AI
GLM-4.5
A 355B MoE reasoning model (32B active) engineered for agentic workflows, tool use, and hybrid reasoning—built for companies deploying private LLM systems that require complex task automation and controlled inference.
GLM-4.5 is an open-weight foundation model optimized for intelligent agents, with dual reasoning modes (thinking + non-thinking), tool-call parsing, and code generation. It ranks 3rd on industry benchmarks (63.2 score) and comes in multiple precision flavors (BF16, FP8) plus a lighter 106B variant (GLM-4.5-Air). For ops teams, it's a self-hosted alternative to API-dependent agentic systems, enabling document processing, customer support automation, and internal knowledge workflows entirely within your infrastructure.
Model facts
Private deployment
Run GLM-4.5 in your own environment
GLM-4.5 is designed for on-premise or VPC deployment via vLLM, SGLang, or Hugging Face transformers. The full BF16 model requires 8–16 H100s or 4–8 H200s for inference; FP8 quantized versions halve GPU requirements. Fine-tuning feasible on 4–16 H100s with LoRA. The company retains all inference data, conversation logs, and model outputs—no queries to external APIs. This matters for enterprises with strict data residency, IP protection, or regulated industries (healthcare, finance, legal).
Operational AI use cases
Enterprise Support Automation & Tool Routing
Deploy GLM-4.5 as a private agent tier that reads incoming support tickets, calls internal APIs (CRM, knowledge base, billing system), routes to the right team, and drafts responses—all without sending customer data to a third-party API. The hybrid reasoning mode handles complex multi-step decisions (e.g., 'refund eligibility + account history + policy check'). Use tool-call parsing to deterministically invoke Salesforce, Zendesk, or custom webhooks.
Internal Knowledge & Document Intelligence
Index employee handbooks, compliance docs, and operational runbooks into a vector DB; use GLM-4.5 as a retrieval-augmented agent to answer HR, legal, and ops questions. The 128K context window accommodates full documents. Running privately means confidential or sensitive policies never leave your network. Fine-tune on domain terminology and internal jargon using LoRA on your 4–16 H100 cluster.
Workflow Orchestration & RPA Augmentation
Chain GLM-4.5 reasoning steps with your existing RPA/automation stack (UiPath, Blue Prism, custom Python agents). Use the thinking mode to decompose complex finance, procurement, or supply-chain tasks before calling deterministic tools. Example: parse unstructured invoice PDFs, extract line items, validate against PO, and trigger approval workflows—all controlled within your network.
Custom AI
As a base for custom AI
GLM-4.5's MoE architecture and hybrid reasoning make it a strong foundation for custom agentic products. Use the base model to build domain-specific agents (legal doc review, technical support, compliance scanning) via LoRA fine-tuning or prompt engineering. The open weight + MIT license means you own the trained artifact. Embed it in a customer-facing SaaS (e.g., internal Slack bot, embedded chat UI) with full control over data, latency, and cost. The FP8 variants allow efficient multi-tenant deployment on modest GPU clusters.
In the operating system
Where it fits
In an AI operating system, GLM-4.5 sits at the **agent reasoning layer**—the core intelligence for orchestrating tools, handling multi-step logic, and maintaining context. Pair it with a vector DB (document indexing) and a deterministic tool scheduler (call APIs, databases, webhooks). It bridges the knowledge layer (retrieval) and workflow layer (execution), making it ideal for mid-market ops where you need flexible, auditable automation without external API calls.
Data control & security
Self-hosting GLM-4.5 means no inference logs, customer data, or conversation history ever transit to a third party—they remain in your environment. This is an **architecture choice**, not a property of the model itself. No personally identifiable information is exposed to Hugging Face or z.ai once deployed. Compliance-sensitive workflows (HIPAA, PCI-DSS, GDPR) become tractable because you control the full data path. For regulated use, conduct your own security audits and data retention policies; the MIT license permits this.
Hardware footprint
**Estimate (production inference):** - GLM-4.5 BF16 (355B): ~710 GB VRAM (8x H100 80GB = 640 GB → requires offloading or 16x H100) - GLM-4.5 FP8 (355B): ~355 GB VRAM (8x H100 or 4x H200 96GB) - GLM-4.5-Air BF16 (106B): ~212 GB VRAM (4x H100) - GLM-4.5-Air FP8 (106B): ~106 GB VRAM (2x H100 or 1x H200) For full 128K context window, multiply GPU counts by 2. LoRA fine-tuning (1 batch/GPU): GLM-4.5 on 16x H100, GLM-4.5-Air on 4x H100.
Integration
GLM-4.5 integrates via vLLM API (OpenAI-compatible endpoint), SGLang (optimized inference + speculative decoding), or transformers (direct model calls). Use the **glm45 tool-call and reasoning parsers** built into vLLM/SGLang to automatically extract structured tool calls and reasoning traces. Wire outputs to webhooks, message queues (Kafka, RabbitMQ), or your ops platform (Zapier, Make.com, custom FastAPI). The model card documents integration for **Llama Factory** and **Swift** for fine-tuning; use those frameworks to adapt the model to your domain without rebuilding from scratch.
When it's not the right fit
- —**Small inference footprint critical**: If you cannot allocate 2–4 GPUs (H100/H200), smaller 7B–13B models (Llama 3.1, Mistral) are more efficient.
- —**Real-time, ultra-low-latency ops**: At 355B parameters (32B active), even with speculative decoding, latency may exceed sub-100ms SLA. Best for batch/async tasks or agents with think-time tolerance.
- —**No GPU infrastructure**: GLM-4.5 cannot run on consumer GPUs or CPU-only setups. If you lack on-premise GPU investment, API services are simpler.
- —**Proprietary reasoning opacity**: The thinking mode produces internal reasoning chains that may be hard to audit or explain in regulated domains without additional transparency tooling.
Alternatives to consider
Llama 3.1 (Meta, 405B)
Slightly larger, but BF16 requires similar GPU footprint and lacks native MoE efficiency. Stronger multilingual support; simpler fine-tuning ecosystem (Axolotl, Llama Factory). MIT-licensed, fully open. Pick if you prefer simpler reasoning and don't need hybrid thinking modes.
Mistral Large 2 (Mistral AI, 123B)
Smaller, efficient MoE variant (~32B active). Runs on 2–4 H100s. Proprietary weights but commercial license available. Good for ops teams with tighter GPU budgets; less reasoning capability than GLM-4.5.
Grok-2 / xAI (open-weight beta)
Comparable scale, designed for reasoning and agentic workflows. When available, may offer alternate trade-offs in inference cost vs. reasoning depth. License terms TBD; currently limited availability.
Related open models
FAQ
Can I run GLM-4.5 fully private—no calls to external APIs?
Yes. Deploy via vLLM or SGLang on your own GPU cluster. All inference, logs, and outputs stay in your environment. No requests to Hugging Face, z.ai, or Zhipu AI unless you explicitly call their API endpoints. The model weights are open-source under MIT; you own the instance.
Is this commercially usable? Can I build a product on it?
Yes. GLM-4.5 is MIT-licensed, permitting commercial use, modification, and redistribution. You can embed it in a SaaS, resell it, or fine-tune it for proprietary applications without royalties. Review the full MIT license terms and your own legal counsel for compliance, but there are no commercial restrictions in the license itself.
What's the difference between GLM-4.5 and GLM-4.5-Air?
GLM-4.5-Air is a lightweight variant: 106B total parameters (12B active) vs. 355B (32B active). Air runs on 2–4 H100s in BF16, making it 4x more efficient. Trade-off: slightly lower benchmark scores (59.8 vs. 63.2). Choose Air if your ops workflows don't require maximum reasoning depth and you want lower GPU costs.
How do I fine-tune this for my domain (e.g., support tickets, contract review)?
Use Llama Factory or Swift with LoRA strategy. Collect 500–5K domain examples, structure them in instruction-response format, and train on 4–16 H100s (8–24 hours). The model card provides exact CLI commands. LoRA updates are lightweight (~50–100 MB) and can be merged into the base model for inference.
Ready to Build Private, Agentic AI Systems?
GLM-4.5 is built for teams deploying self-hosted LLMs that stay under your control. Let LLM.co help you architect a private reasoning layer—integrate GLM-4.5 with your ops stack, fine-tune it on your data, and automate workflows without external APIs. Get started with a consultation.