GLM-5
GLM-5 is a 744-billion-parameter open-source LLM developed by zai-org, released in April 2026. It uses sparse mixture-of-experts (MoE) architecture with 40B active parameters and integrates DeepSeek Sparse Attention to reduce deployment cost. The model is designed for complex reasoning, coding, and agentic tasks. It supports both English and Chinese, is MIT-licensed, ungated, and can be self-hosted using multiple open-source frameworks.
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 | 753.9B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 64.6k |
| Likes | 2.1k |
| Last updated | 2026-04-05 |
| Source | zai-org/GLM-5 |
What GLM-5 is
GLM-5 is a 744B-parameter MoE transformer trained on 28.5T tokens with post-training via asynchronous RL (slime infrastructure). It uses DeepSeek Sparse Attention (DSA) to enable long-context capacity with reduced VRAM footprint. The model card lists support for vLLM, SGLang, KTransformers, Hugging Face Transformers, and xLLM. Context length is not specified. Distributed inference with tensor-parallel-size 8 is recommended.
Run GLM-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-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: 744B parameters at FP16 ≈ 1.5 TB raw; with MoE sparsity and DSA, active memory likely 300–500 GB. Recommended deployment: 8× A100/H100 GPUs (80 GB each) with tensor parallelism. vLLM deployment recipe specifies tensor-parallel-size 8, --gpu-memory-utilization 0.85. Quantization (GPTQ/AWQ) may reduce to 100–200 GB. Verify with your inference framework and batch size.
Not stated in model card. No mention of LoRA, QLoRA, or fine-tuning infrastructure. GLM series historically supports instruction fine-tuning. Recommend: (1) check GitHub repo for recipes, (2) expect full fine-tuning to require multi-GPU setup due to scale, (3) assess LoRA feasibility empirically or contact zai-org.
When to avoid it — and what to weigh
- Tight Latency SLAs on Limited Hardware — Model requires 8-GPU tensor-parallel setup. Even with MoE sparsity, inference cost is substantial. Not suitable for single-GPU or edge deployments without quantization.
- Production Without Benchmarking Against Your Workload — Published benchmarks are curated; real-world performance on proprietary datasets or specialized domains may differ. Requires custom evaluation before committing.
- Strict IP/Output Control Requirements — MIT license allows redistribution of model outputs. If you need contractual guarantees on derivative works or cannot tolerate community fine-tuned versions, consult legal.
- Lack of Commercial Support Need — zai-org offers API services and Z.ai platform. No information on commercial SLAs, support response times, or enterprise contracts in the model card.
License & commercial use
MIT License. Permissive open-source license allowing commercial use, modification, and redistribution with minimal restrictions (attribution required).
MIT is a permissive OSI-approved license. Commercial use of the model weights and outputs is allowed. However: (1) No explicit warranty or indemnity; (2) zai-org offers commercial API services separately (Z.ai), which may have different terms; (3) If you integrate GLM-5 into a product, ensure compliance with MIT attribution and any downstream license obligations. Consult legal for high-stakes deployments.
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 | Strong |
| Assessment confidence | High |
Standard LLM risks apply: (1) Model outputs may contain harmful content; use content filtering in production, (2) Ungated and open-source; source-code and weights publicly available—review for any embedded artifacts before deploying, (3) Inference on user data: ensure appropriate isolation, rate-limiting, and audit logging, (4) Tool-calling/agentic capability increases attack surface—validate tool specifications and sandbox execution, (5) No security disclosure policy mentioned; file responsibly if vulnerabilities found. No formal adversarial robustness testing cited.
Alternatives to consider
DeepSeek-V3.2
Also uses MoE with sparse attention (DeepSeek Sparse Attention itself). 73.1% SWE-bench vs GLM-5's 77.8%; comparable reasoning. Unclear license and commercial terms; requires review.
Llama 3.3 (Meta)
Open-source, widely deployed, strong on coding (SWE-bench). Llama 3.3 is Llama 2 license (requires review for commercial use). Smaller/less capable on long-context agentic tasks than GLM-5.
Grok-3 or Claude Opus 4.5 (proprietary)
Comparable or better reasoning/agentic performance (Claude achieves 80.9% SWE-bench). No deployment overhead, but closed-source, vendor lock-in, higher per-token costs, no fine-tuning control.
Ship GLM-5 with senior software developers
Start with vLLM or SGLang on 8× A100/H100 GPUs. Benchmark against your workload before production. For managed inference, use Z.ai API Platform. For custom fine-tuning or on-premises deployment, contact zai-org or consult the GitHub repository.
Talk to DEV.coRelated on DEV.co
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GLM-5 FAQ
Can I use GLM-5 commercially without restrictions?
What is the minimum GPU setup to serve GLM-5?
Does GLM-5 support fine-tuning, and if so, what framework?
How long is the context window?
Software development & web development with DEV.co
Need help beyond evaluating GLM-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-5?
Start with vLLM or SGLang on 8× A100/H100 GPUs. Benchmark against your workload before production. For managed inference, use Z.ai API Platform. For custom fine-tuning or on-premises deployment, contact zai-org or consult the GitHub repository.