GLM-4.7-Flash
GLM-4.7-Flash is a 30B mixture-of-experts language model optimized for efficient deployment while maintaining competitive performance on reasoning and coding tasks. It supports multiple inference frameworks (vLLM, SGLang, transformers) and is available under the MIT license without gating restrictions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | 31.2B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 329.8k |
| Likes | 15 |
| Last updated | 2026-02-12 |
| Source | unsloth/GLM-4.7-Flash |
What GLM-4.7-Flash is
GLM-4.7-Flash is a 31.2B-parameter MoE (mixture-of-experts) model with active parameters estimated at 3B (A3B architecture). It integrates Unsloth chat template fixes and supports both bf16 and quantized inference. The model includes tool-call and reasoning parsers for structured output. Context length is not publicly stated. Based on arxiv:2508.06471 (GLM-4.5 technical report).
Run GLM-4.7-Flash locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="unsloth/GLM-4.7-Flash")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: ~60–80 GB GPU VRAM for bf16 inference on a single GPU (or ~15–20 GB per GPU with 4× tensor parallelism). Quantized 4-bit variants could reduce to ~15–20 GB single-GPU. Verify with vLLM/SGLang benchmarks for your hardware and throughput target. The model card recommends tensor-parallel-size=4 in example configs.
Unknown. The card does not mention LoRA/QLoRA support, adapter compatibility, or fine-tuning infrastructure. Base model is zai-org/GLM-4.7-Flash with finetune tag present, but fine-tuning procedure and parameter efficiency techniques are not detailed. Requires review of official GLM GitHub repository.
When to avoid it — and what to weigh
- Unknown context window requirements — Context length is not stated in the model card. Long-document processing or extended multi-turn conversations require verification against your use case before production deployment.
- Strict real-time latency SLAs — MoE routing and the need for main-branch vLLM/SGLang versions introduce complexity. If sub-100ms inference is critical, benchmark thoroughly or choose simpler dense models.
- Non-English, non-Chinese languages — Benchmarks and documentation indicate English and Chinese focus. Use on other languages is not validated.
- Edge/mobile deployment — 30B active parameters and bf16 precision require GPU or TPU. Even quantized, this model is not suitable for phones, browsers, or constrained edge devices.
License & commercial use
MIT license. OSI-approved permissive license permitting commercial and private use, modification, and distribution with attribution.
MIT is a permissive OSI license. Commercial use, closed-source applications, and proprietary modification are permitted. No usage restrictions stated. However, verify with your legal team that deployment of open-source models aligns with your compliance and data-residency policies. No commercial support, SLA, or indemnification is implied by the license.
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 explicit security audit, red-teaming results, or safety testing is documented in the card. Like all LLMs, consider: prompt injection risks, data privacy if used with sensitive inputs, model poisoning if fine-tuning on untrusted data, and potential for generating harmful or biased content. Deployment on private infrastructure mitigates some data-leakage risks compared to API services. Unsloth's quantization may or may not preserve adversarial properties of the full model; not stated.
Alternatives to consider
Qwen3-30B-A3B-Thinking
Comparable 30B MoE size; lower AIME (85.0 vs 91.6) but stronger on LCB v6 (66.0 vs 64.0). Choose if you prioritize different benchmarks or prefer Alibaba ecosystem.
Llama 3.3 70B (Meta, permissive license)
Dense 70B for higher absolute performance on reasoning tasks, though larger footprint and slower inference. Suitable if you have more GPU budget and lower latency is acceptable.
Mistral 7B or 8x7B MoE (Apache 2.0)
Smaller footprint and faster inference, but lower benchmark scores. Use if you prioritize speed and cost over raw reasoning accuracy, or as a baseline for your evaluation.
Ship GLM-4.7-Flash with senior software developers
Compare GPU requirements and inference latency with vLLM or SGLang on your target hardware. Review the official GLM-4.5 GitHub and technical report for context length, fine-tuning, and production best practices. Start a proof-of-concept on a private cluster to validate performance on your workload.
Talk to DEV.coRelated open-source tools
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GLM-4.7-Flash FAQ
Can I use GLM-4.7-Flash commercially?
What GPU(s) do I need to run this locally?
What is the context window size?
Can I fine-tune GLM-4.7-Flash?
Software development & web development with DEV.co
DEV.co helps companies turn open-source tools like GLM-4.7-Flash 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.
Ready to deploy GLM-4.7-Flash?
Compare GPU requirements and inference latency with vLLM or SGLang on your target hardware. Review the official GLM-4.5 GitHub and technical report for context length, fine-tuning, and production best practices. Start a proof-of-concept on a private cluster to validate performance on your workload.