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Secure AI virtual data roomsGLM-4.5-Air-AWQ-4bit
GLM-4.5-Air-AWQ-4bit is a quantized 18.6B-parameter language model from Zhipu AI's GLM-4.5 series, released under the MIT license. It is a mixture-of-experts (MoE) variant with 12 billion active parameters, designed for conversational AI, reasoning, and tool use across English and Chinese. This is a 4-bit quantized version optimized for efficient inference. The base model has logged 412k+ downloads with modest community engagement (29 likes).
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | cyankiwi |
| Parameters | 18.6B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 412.1k |
| Likes | 29 |
| Last updated | 2026-05-06 |
| Source | cyankiwi/GLM-4.5-Air-AWQ-4bit |
What GLM-4.5-Air-AWQ-4bit is
GLM-4.5-Air is a hybrid reasoning model supporting both thinking mode (complex reasoning, tool use) and non-thinking mode (immediate responses). The quantized AWQ 4-bit variant reduces memory footprint significantly from the full precision baseline. The model family achieved a 59.8 benchmark score across 12 industry-standard benchmarks. It is compatible with transformers, vLLM, and SGLang inference frameworks. Context length is not specified in available documentation.
Run GLM-4.5-Air-AWQ-4bit locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="cyankiwi/GLM-4.5-Air-AWQ-4bit")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: 4-bit quantization of an 18.6B-parameter model typically requires 9–15 GB VRAM for inference (batch size 1). Precise requirements depend on sequence length and inference framework. Verify with your serving stack (vLLM, TGI) before deployment. Training/fine-tuning memory Unknown; expect significantly higher costs.
LoRA/QLoRA fine-tuning is plausible given the model's availability in transformers and quantized formats. No explicit fine-tuning guidance, recipes, or performance data provided in card. Recommend consulting the technical report (arxiv:2508.06471) or GitHub for implementation details.
When to avoid it — and what to weigh
- Extreme latency sensitivity — Quantization introduces potential inference-time overhead or quality trade-offs. Verify latency against your SLA before production use.
- Unsupported language requirements — Optimized for English and Chinese. Use of other languages is Unknown and likely degraded.
- Context-heavy applications — Context length is not specified. If your workload requires very long context windows (e.g., 100k+ tokens), verify feasibility with documentation or benchmarks.
- Proprietary safety/compliance mandates — No explicit safety training, alignment framework, or compliance certifications (PII handling, HIPAA, SOC 2) are documented. Requires review before regulated use.
License & commercial use
MIT license. Permissive OSI-approved license permitting commercial use, modification, and redistribution with minimal restrictions (retain attribution and license notice).
Commercial use is explicitly permitted under the MIT license. The model card states: 'They are released under the MIT open-source license and can be used commercially and for secondary development.' No runtime licensing fees, usage restrictions, or proprietary API keys required. Review any downstream dependency licenses if integrating with proprietary frameworks.
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 | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security posture or audits documented. Quantization from a base model introduces a potential vector for unintended behavior changes; validate outputs in adversarial or high-stakes contexts. No mention of PII filtering, prompt injection mitigations, or jailbreak resistance. Recommend threat modeling before regulated or safety-critical deployment.
Alternatives to consider
Meta Llama 3.1 (quantized)
Similar parameter count, broader community ecosystem, more extensive fine-tuning documentation. Llama license is permissive but requires review for commercial use specifics.
Mistral 7B or Mistral Medium (Apache 2.0 or proprietary)
Smaller footprint, extensive production deployments, strong inference framework support. Trade-off: lower reasoning capability than GLM-4.5-Air.
Qwen 2.5 series (Apache 2.0)
Open-source, multilingual, quantized variants available. Comparable or better benchmark performance on some tasks; similar license clarity.
Ship GLM-4.5-Air-AWQ-4bit with senior software developers
Verify context length and quantization performance on your hardware. Start with a test deployment on vLLM or transformers. Review the technical report for benchmark details and reasoning mode trade-offs.
Talk to DEV.coRelated open-source tools
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GLM-4.5-Air-AWQ-4bit FAQ
Can I use this model commercially without paying fees or licensing Zhipu AI?
What is the memory footprint for inference?
Is context length specified?
Does this model support fine-tuning?
Build it with a software development company
DEV.co is a software development company delivering production systems to teams building on open source. Our engineers design, integrate, and ship across web, APIs, AI, data, and cloud. If GLM-4.5-Air-AWQ-4bit is part of your open-source llms roadmap, we can implement, customize, migrate, and maintain it.
Ready to Deploy GLM-4.5-Air-AWQ-4bit?
Verify context length and quantization performance on your hardware. Start with a test deployment on vLLM or transformers. Review the technical report for benchmark details and reasoning mode trade-offs.