GLM-4.5-Air
GLM-4.5-Air is a 106-billion-parameter open-source language model from Zhipu AI with 12 billion active parameters. It supports both reasoning and immediate-response modes, handles text generation in English and Chinese, and is released under the MIT license. It achieves competitive benchmark scores (59.8) while maintaining efficiency. The model is suitable for custom LLM applications, RAG systems, and self-hosted deployments.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | zai-org |
| Parameters | 110.5B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 418.4k |
| Likes | 614 |
| Last updated | 2025-08-11 |
| Source | zai-org/GLM-4.5-Air |
What GLM-4.5-Air is
GLM-4.5-Air is a hybrid reasoning mixture-of-experts (MoE) model with 106B total parameters and 12B active parameters. It implements a dual-mode architecture: a thinking mode for complex reasoning and tool usage, and a non-thinking mode for direct responses. The model supports English and Chinese, uses safetensors format, and is compatible with transformers, vLLM, and SGLang inference frameworks. Last modified 2025-08-11. Context length is unknown.
Run GLM-4.5-Air locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="zai-org/GLM-4.5-Air")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
ESTIMATE: FP32 inference ~212 GB VRAM (110B params × 2 bytes approximate), impractical. Quantized (FP8 noted in card) ~53 GB. Production deployment likely requires 2×H100 80GB or equivalent. Context length unknown; actual peak memory depends on sequence length and batch size. Requires verification against inference framework memory profiling.
No explicit fine-tuning guidance in provided data. Hybrid reasoning architecture and MoE design suggest potential complexity for LoRA/QLoRA adaptation. Zhipu AI documentation and transformers implementation likely contain guidance; requires review of GitHub repository and technical report (arxiv:2508.06471).
When to avoid it — and what to weigh
- Extreme Latency-Critical Applications — Thinking mode incurs computational overhead. If sub-500ms responses are mandatory across all queries, consider smaller models (7B-13B) or non-reasoning variants, though this model's non-thinking mode may be viable.
- Limited Hardware Budget — 106B parameters require substantial VRAM even with quantization. Inference on consumer GPUs (24GB VRAM) will be constrained; requires A100/H100 or equivalent for production throughput.
- Proprietary Model Lock-In Required — MIT license enables commercial use and modification, but if your strategy depends on proprietary model differentiation, verify that downstream modifications remain defensible under MIT terms.
- Rare Language Support Outside English/Chinese — Model documentation emphasizes English and Chinese. If you require strong performance in Japanese, Spanish, or other languages, comparative benchmarking against the card data is not provided.
License & commercial use
MIT license. Explicitly permits commercial use and secondary development. No known use restrictions or proprietary clauses flagged in model card.
MIT is a permissive OSI-approved license allowing commercial use, modification, and distribution. The model card explicitly states: "They are released under the MIT open-source license and can be used commercially and for secondary development." No gating or additional commercial agreement required. Verify compliance with any downstream dependencies (e.g., inference framework licenses).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit or vulnerability disclosure policy stated in provided data. As an open-weight model, source code inspection is possible but requires manual review. Inference framework security (vLLM, transformers, SGLang) is separate consideration. Reasoning mode may exhibit emergent behavior warranting evaluation in adversarial/untrusted prompt scenarios. No published red-team results or adversarial robustness data provided. Deployers should evaluate model behavior and sanitize outputs for user-facing applications.
Alternatives to consider
Llama 3.1 (405B or smaller variants)
Also open-source and permissive-licensed, with broader ecosystem; larger variants offer higher benchmark performance but at greater infrastructure cost. Smaller variants (8B, 70B) offer efficiency trade-offs.
Mistral 7B / Mixtral 8×7B
Smaller parameter counts, faster inference, lower VRAM. Mixtral is MoE like GLM-4.5-Air but uses 8×7B sparse architecture. Trade-off: lower reasoning capability.
If reasoning and agent capabilities are prioritized over self-hosting, proprietary models offer mature thinking modes and higher benchmark scores. Cost and data residency are trade-offs.
Ship GLM-4.5-Air with senior software developers
GLM-4.5-Air is production-ready for self-hosted LLM applications, custom AI tools, and RAG systems. MIT licensing enables commercial use and customization. Review the technical report and benchmark details, then contact our AI specialists to evaluate integration with your infrastructure.
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GLM-4.5-Air FAQ
Can I use GLM-4.5-Air commercially?
What GPU hardware do I need to run this model?
How do thinking mode and non-thinking mode differ?
Is this model better than GPT-4 or Llama 3.1?
Software development & web development with DEV.co
DEV.co helps companies turn open-source tools like GLM-4.5-Air into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Deploy GLM-4.5-Air for Your Enterprise AI Needs
GLM-4.5-Air is production-ready for self-hosted LLM applications, custom AI tools, and RAG systems. MIT licensing enables commercial use and customization. Review the technical report and benchmark details, then contact our AI specialists to evaluate integration with your infrastructure.