GLM-4.7
GLM-4.7 is a 358B parameter open-source language model optimized for coding, agentic tasks, and complex reasoning. Licensed under MIT, it is freely available for download and local deployment. The model emphasizes multi-turn coding workflows with 'thinking' modes and supports frameworks like vLLM and SGLang for self-hosted inference.
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 | 50.9k |
| Likes | 2k |
| Last updated | 2026-01-29 |
| Source | zai-org/GLM-4.7 |
What GLM-4.7 is
GLM-4.7 is a mixture-of-experts (MoE) text-generation model released by zai-org on 2026-01-29. It supports conversational use in English and Chinese, with safetensors weights compatible with the transformers library (v4.57.3+). Context length is unknown. The model integrates interleaved, preserved, and turn-level thinking capabilities. Inference frameworks include vLLM (nightly), SGLang (dev), and direct transformers usage. No gating; 50.9k downloads, 2.0k likes.
Run GLM-4.7 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-4.7")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
Estimated VRAM: ~700GB in bfloat16 (full precision), ~350GB in int8 quantization, ~175GB in int4/GPTQ. Multi-GPU setups (8x H100/H200 or equivalent) recommended for reasonable throughput. Exact quantization trade-offs and speed benchmarks not provided in card; test locally.
Not explicitly documented in card. Model card references transformers v4.57.3+ support and mentions vLLM/SGLang for inference, but no mention of LoRA, QLoRA, or parameter-efficient training. Requires review of official GitHub repository or documentation for fine-tuning guidance.
When to avoid it — and what to weigh
- Unknown Context Length Requirements — Context length is not specified in the model card. If you need ultra-long-context handling (>100k tokens), verify actual window size before deployment.
- Latency-Critical Real-Time Systems — 358B parameters and interleaved/preserved thinking modes add inference overhead. For sub-100ms response SLAs, consider smaller models or cached inference patterns.
- Constrained Hardware Environments — A model of this scale (358B) requires significant VRAM (estimated 700GB+ in bfloat16, less with quantization). Not suitable for CPU-only or resource-limited edge devices without aggressive quantization.
- Non-English/Chinese Workloads — Model card explicitly emphasizes English and Chinese. Multilingual support beyond these languages is not documented.
License & commercial use
MIT License. Permissive OSI-approved open-source license permitting use, modification, and distribution with minimal restrictions (retain copyright notice).
MIT license clearly permits commercial use, modification, and redistribution. No gating, no model cards restrictions, and no vendor lock-in. Commercial deployment is explicitly allowed without special licensing or approval. Verify compliance with upstream dependencies (vLLM, transformers) for full stack licensing.
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 | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, threat model, or safety evaluation detailed in card. Model card does not address prompt injection, jailbreak resistance, or data contamination. Standard LLM considerations apply: validate input guardrails for agentic deployments, monitor tool-use permissions, and isolate inference environments from sensitive data. Perform security review before production use in regulated contexts.
Alternatives to consider
DeepSeek-V3.2
Competitive on coding (73.1% SWE-bench Verified) and reasoning (25.1% HLE w/ tools). Likely comparable inference cost/speed trade-off, but check licensing and deployment terms.
Claude Opus 4.5
Higher scores on several benchmarks (88.2% MMLU-Pro, 91.9% GPQA-Diamond). Proprietary API-first model; no local deployment. Consider for lower infrastructure cost or when API latency acceptable.
Gemini 3.0 Pro
Strong on reasoning (37.5% HLE) and benchmarks (90.1% MMLU-Pro). Google-hosted; different pricing model. Evaluate if you prefer managed inference over self-hosted.
Ship GLM-4.7 with senior software developers
Get started with GLM-4.7 on your infrastructure using vLLM, SGLang, or transformers. MIT-licensed, no gating, and designed for multi-turn coding workflows. Review hardware requirements and context length constraints before production deployment.
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GLM-4.7 FAQ
Can I use GLM-4.7 commercially?
What VRAM do I need to run GLM-4.7 locally?
Does GLM-4.7 support fine-tuning?
What is the maximum context length?
Software developers & web developers for hire
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If GLM-4.7 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Deploy GLM-4.7 for Your Agentic Coding Workloads
Get started with GLM-4.7 on your infrastructure using vLLM, SGLang, or transformers. MIT-licensed, no gating, and designed for multi-turn coding workflows. Review hardware requirements and context length constraints before production deployment.