GLM-4.5
GLM-4.5 is a 355-billion-parameter open-source LLM from Zhipu AI (zai-org) released under MIT license. It features a mixture-of-experts architecture with 32B active parameters, hybrid reasoning modes (thinking and non-thinking), tool-use capabilities, and multilingual support (English, Chinese). The full model and smaller GLM-4.5-Air variant (106B total, 12B active) are available ungated. Requires substantial GPU infrastructure (H100/H200 clusters) for production deployment.
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 | 358.3B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 144.2k |
| Likes | 1.4k |
| Last updated | 2025-08-11 |
| Source | zai-org/GLM-4.5 |
What GLM-4.5 is
GLM-4.5 is a hybrid reasoning foundation model with MoE architecture optimized for agentic workflows. It achieves a score of 63.2 on industry benchmarks (ranked 3rd among proprietary and open-source models). The model supports two inference modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses. Inference is tested on vLLM, SGLang, and transformers with speculative decoding (EAGLE). FP8 quantized variants available for reduced VRAM. Context length stated as 128K in system requirements tables. Last updated 2025-08-11.
Run GLM-4.5 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")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 (verify with actual deployment):** GLM-4.5 BF16 minimum inference: 16x H100 (~16 × 80GB = ~1.28TB VRAM). GLM-4.5 BF16 with full 128K context: 32x H100 (~2.56TB). GLM-4.5-FP8: ~8x H100 minimum (~640GB). GLM-4.5-Air BF16: ~4x H100 (~320GB). GLM-4.5-Air-FP8: ~2x H100 (~160GB). Fine-tuning: LoRA on GLM-4.5 requires 16x H100; on GLM-4.5-Air, 4x H100. CPU offload available in vLLM if memory exhausted. Server memory >1TB recommended.
LoRA fine-tuning is supported and documented via Llama Factory and Swift frameworks. Batch size per GPU is 1 for both LoRA and SFT strategies due to model size. Full SFT and RL training are possible but require substantial GPU counts (e.g., 128x H20 for GLM-4.5 SFT). Efficient fine-tuning via LoRA is feasible for moderate customization; full retraining is infrastructure-intensive.
When to avoid it — and what to weigh
- Limited GPU infrastructure or edge deployment — Minimum inference requires H100 x 8 (FP8) or H100 x 16 (BF16) for GLM-4.5. Even the smaller GLM-4.5-Air requires H100 x 2 (FP8) or H100 x 4 (BF16). Not suitable for single-GPU or consumer hardware deployments.
- Latency-critical applications requiring sub-100ms response times — While speculative decoding is supported, 355B-parameter models with thinking mode incur significant latency. Unknown exact inference speed; not recommended where strict latency SLAs are critical.
- Strict compliance or security auditing requirements — No audit trail, formal security assessment, or compliance certifications mentioned. Security considerations unknown. Unsuitable for regulated environments (healthcare, finance, classified systems) without independent review.
- Languages outside English and Chinese — Model card explicitly lists en, zh support. Multilingual capability for other languages is not documented and should not be assumed.
License & commercial use
MIT License. Permissive OSI-compliant license allowing commercial use, modification, and redistribution with minimal restrictions (attribution required). License is clear and unambiguous.
Commercial use is explicitly permitted under MIT License. Model card states: 'They are released under the MIT open-source license and can be used commercially and for secondary development.' No gating, no proprietary restrictions. Suitable for commercial deployment as-is.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
No formal security assessment, penetration testing, or vulnerability disclosure process mentioned. Model trained on unknown data; potential for memorization or adversarial inputs is undocumented. Deployment should assume standard LLM risks: prompt injection, jailbreaking, information leakage. No guardrails or safety filtering explicitly described. For production use in sensitive contexts, independent security review recommended.
Alternatives to consider
Llama 3.1 (Meta, 405B)
Larger reasoning-capable model, permissive license. Requires similar infrastructure; less documentation on agentic/tool-use specifics. Strong ecosystem, larger community.
Qwen 2.5 (Alibaba, up to 72B)
Smaller, more accessible for medium-scale deployments. Strong multilingual support (including Chinese). Fewer reasoning optimizations than GLM-4.5; no explicit hybrid reasoning mode.
Mixtral 8x22B (Mistral AI)
MoE architecture, smaller total size (~176B parameters). Lower infrastructure barrier. Mature community; less optimized for agentic reasoning than GLM-4.5.
Ship GLM-4.5 with senior software developers
Assess GPU capacity, review the system requirements tables for your use case, and prototype on SGLang or vLLM. Contact us if you need guidance on fine-tuning, safety audits, or production ops.
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GLM-4.5 FAQ
Can I use GLM-4.5 in a commercial product or SaaS offering?
What is the minimum GPU setup to run GLM-4.5?
How does the thinking mode improve reasoning?
Is there support or an SLA if I deploy this in production?
Custom software development services
Need help beyond evaluating GLM-4.5? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy GLM-4.5?
Assess GPU capacity, review the system requirements tables for your use case, and prototype on SGLang or vLLM. Contact us if you need guidance on fine-tuning, safety audits, or production ops.