Open-Weight LLM · Private & Custom AI
GLM-4.5-Air
Compact hybrid-reasoning MoE for private agent automation and operational workflows—12B active parameters, MIT-licensed, runs cost-effectively on standard enterprise GPU infrastructure.
GLM-4.5-Air is a 110B-parameter mixture-of-experts model with 12B active parameters, designed for reasoning, tool use, and agentic tasks. It trades minimal performance drop (59.8 vs. 63.2 on benchmarks) for 3–4× lower inference cost, making it viable for ops teams deploying custom AI agents and workflow automation privately. Bilingual (English/Chinese) and available as FP8 quantized weights.
Model facts
Private deployment
Run GLM-4.5-Air in your own environment
Self-hosting requires ~24–40 GB VRAM (FP16 at 12B active; FP8 quantized ~16–20 GB estimate). Deployment-ready in transformers, vLLM, and SGLang; no API calls = data never leaves your environment. Suitable for companies prioritizing data isolation and avoiding third-party inference cost and latency for high-volume operational tasks.
Operational AI use cases
Support ticket triage & reasoning
Route incoming support tickets using hybrid reasoning mode for complex issue categorization. Thinking mode enables multi-step diagnosis before tool calls to knowledge bases; non-thinking mode provides instant template responses. Reduces hand-off time and training burden on junior staff.
Finance & procurement document automation
Extract and reconcile invoice fields, flag policy exceptions, and draft RFQ responses. MoE efficiency keeps per-document inference cost low; reasoning mode validates compliance logic before routing to approval workflows. Run entirely on-premises to keep financial data local.
Internal knowledge agent & onboarding
Embed GLM-4.5-Air as a Q&A agent over HR policies, engineering docs, and internal wikis. Tool-use capabilities allow it to query internal APIs (HRIS, Confluence, JIRA) and synthesize answers. Thinking mode ensures accuracy on policy questions; low active parameters keep response latency <2s.
Custom AI
As a base for custom AI
Strong foundation for custom applications: reasoning and tool-use layers are built-in, allowing teams to wrap domain data, internal APIs, and business logic without heavy fine-tuning. Bilingual support and MoE sparsity mean you can build multi-tenant or multi-domain custom AI products that monetize per-inference without prohibitive infrastructure spend. FP8 quantized checkpoints enable rapid experimentation.
In the operating system
Where it fits
Operates as the core intelligence layer in an AI OS: reasoning backbone for knowledge retrieval and synthesis, agentic decision-making, and workflow automation. Sits between data ingestion (documents, APIs) and execution layers (tool APIs, business processes). Lower active parameter count allows it to run alongside guardrails, retrieval engines, and monitoring at reasonable total cluster cost.
Data control & security
Private deployment architecture means operational data—support tickets, financial docs, HR records—never leaves your infrastructure. No telemetry to external APIs, no training data exposure. However, model weights themselves are open and auditable; you remain responsible for securing the deployment (access control, inference logs, model versioning). Compliance (HIPAA, SOC 2, etc.) depends on your operational setup, not the model.
Hardware footprint
Estimates (unverified): FP16 (12B active params) ~24–32 GB VRAM; FP8 quantized ~16–20 GB. For batch inference on operational tasks, A100-40GB or H100 sufficient; for single-request latency, 2–4x A10/L40S GPUs. Cost-per-inference 3–4× lower than full-size models due to MoE sparsity.
Integration
Transformer-native implementation (HuggingFace transformers, vLLM, SGLang); integrates via REST APIs (text_generation_webserver, vLLM OpenAI-compatible endpoint) or Python SDK. Tool calling follows standard JSON schema; wire to internal REST/GraphQL APIs. Context length unknown—verify token budget for multi-turn workflows. Deployment agnostic (Docker, Kubernetes, cloud VMs, on-prem).
When it's not the right fit
- —Context window is unstated—unknown whether it supports long-document workflows (100k+ token chains); requires testing before committing to ops pipelines.
- —Reasoning mode latency not benchmarked; if sub-100ms response times are critical (e.g., real-time chat), non-thinking mode may be only option.
- —Multilingual beyond English and Chinese untested; teams requiring Spanish, German, etc. should validate on representative domain data.
- —No published guardrails or safety fine-tuning details; ops teams handling sensitive content should layer external content filtering and output validation.
Alternatives to consider
Meta Llama-3.1-405B (or 70B variant)
Larger, broader multilingual support, but requires 300+ GB VRAM at scale; better for orgs with high-performance cluster budgets. No native reasoning/tool-use integration.
Mistral Large 2 (MoE variant, if available)
Similar sparsity strategy, strong coding/reasoning, but typically API-first; less transparent open-source footprint for private deployment.
Qwen2.5-72B
Dense model, excellent multilingual and coding, lower inference cost than 405B-scale models, but still requires ~40–50 GB VRAM and no native reasoning mode.
Related open models
FAQ
Can I run GLM-4.5-Air entirely on-premises without cloud APIs?
Yes. Deploy via vLLM or SGLang on your own GPU cluster, or containerize in Kubernetes. Data stays on your network; no external calls required. You manage model versioning, inference logging, and access control.
Is this model free to use commercially?
Yes. MIT license permits commercial use, secondary development, and redistribution. No royalties or usage limits. Verify licensing of any fine-tuning or derivations in your own legal review.
What's the difference between thinking and non-thinking mode?
Thinking mode enables multi-step reasoning (e.g., diagnosis before tool calls); slower but more accurate for complex decisions. Non-thinking mode skips reasoning for faster, direct responses. Use thinking for high-stakes ops (approvals, compliance) and non-thinking for high-volume, straightforward tasks.
How do I use GLM-4.5-Air as a foundation for custom AI applications?
Fine-tune or prompt-engineer on your domain data and internal APIs. Tool calling is built-in; define JSON schemas for your business workflows (ticket routing, approval chains, knowledge queries). Sparsity keeps training and inference costs low compared to dense alternatives.
Deploy GLM-4.5-Air as Your Private Ops AI Engine
LLM.co helps companies self-host and customize open-weight models like GLM-4.5-Air for enterprise workflows. From support triage to financial automation, build reasoning-powered agents that run entirely in your environment. Start a conversation with our AI operating system today.