GLM-4.7-Flash-GGUF
GLM-4.7-Flash is a 30B-parameter mixture-of-experts (MoE) language model from Z.ai, quantized and distributed by Unsloth in GGUF format. It supports text generation in English and Chinese, with demonstrated strength in reasoning, code generation, and tool-calling tasks. The model is ungated, MIT-licensed, and can be deployed locally via vLLM, SGLang, or transformers. Recent updates (Jan 2026) fixed llama.cpp output quality issues.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 94k |
| Likes | 660 |
| Last updated | 2026-02-12 |
| Source | unsloth/GLM-4.7-Flash-GGUF |
What GLM-4.7-Flash-GGUF is
GLM-4.7-Flash is a 30B-A3B MoE model (active parameters ~3B per token) based on the GLM architecture. The Unsloth GGUF variant uses dynamic quantization (Unsloth Dynamic 2.0) for improved accuracy across inference frameworks. Supports bfloat16 native precision, speculative decoding (MTP in vLLM, EAGLE in SGLang), and tool-call/reasoning parsers. Context length and exact parameter count not specified in card. Requires transformers from main branch (git) for full compatibility.
Run GLM-4.7-Flash-GGUF 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-GGUF")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: ~30GB VRAM (bfloat16 unquantized); GGUF quantization likely reduces to 10–15GB depending on quant level (not specified in card). Multi-GPU serving recommended: examples show tensor-parallel-size 4. vLLM/SGLang benefit from recent GPU with bfloat16 support (NVIDIA A100, H100, or RTX 40 series). Verify actual GGUF quantization level and memory footprint in model files before deployment.
Unsloth provides free fine-tuning notebook for GLM-4.7-Flash. Likely supports QLoRA (quantized LoRA) for efficient adaptation on limited VRAM. Requires git-main transformers for proper support. No explicit LoRA adapters listed in card; community contributions may exist on HuggingFace hub.
When to avoid it — and what to weigh
- Single-GPU consumer hardware (< 24 GB VRAM) — 30B model size requires significant VRAM. While GGUF quantization helps, vLLM examples show tensor-parallel-size 4 for optimal serving. Unquantized bfloat16 requires ~60 GB+.
- Extremely low-latency production (sub-100ms p95) — MoE routing overhead and multi-GPU communication may introduce latency. Best suited for throughput-optimized or batch inference rather than real-time interactive systems.
- Edge/mobile deployment — 30B parameters and MoE architecture incompatible with mobile or ultra-low-resource edge devices. Consider smaller models for edge.
- Proprietary, non-attribution use cases — MIT license requires attribution. If IP sensitivity around model attribution is a concern, review license obligations.
License & commercial use
MIT License. Permissive open-source; allows commercial use, modification, and distribution with attribution. No known restrictions or special commercial terms.
MIT is a permissive OSI-approved license explicitly allowing commercial use. No gating, no API restrictions, no proprietary terms stated. You may deploy, modify, and monetize applications built on this model. Ensure you comply with MIT attribution requirements (include license notice in derivative works). Z.ai also offers hosted API services; evaluate cost vs. self-hosting for your scale.
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 or red-team report provided in card. Standard LLM risks apply: potential for harmful output generation, prompt injection, jailbreak vulnerability. Self-hosted deployment eliminates third-party data transfer but requires secure infrastructure management (access control, model storage, API endpoint protection). No mention of built-in content filtering; consider guardrails in production. Validate transformers/vLLM/SGLang dependencies are kept updated for security patches.
Alternatives to consider
Qwen3-30B-A3B-Thinking (similar MoE architecture)
Comparable 30B-A3B model; benchmarks show mixed results (GLM-4.7 stronger on SWE-bench 59.2 vs 22.0, GLM weaker on LCB v6 64.0 vs 66.0). Evaluate on your specific benchmarks.
Llama 3.1 70B or Mistral Large (larger dense alternatives)
If you have multi-GPU cluster and need maximum quality, denser models may exceed GLM-4.7's performance. Trade: higher compute, no MoE efficiency gains.
GPT-4o mini / Claude 3.5 Haiku (API-first, managed)
If avoiding self-hosting and prioritizing reliability, hosted APIs reduce ops burden. Trade: vendor lock-in, data handling, latency, cost at scale.
Ship GLM-4.7-Flash-GGUF with senior software developers
Start with the Unsloth how-to guide and vLLM/SGLang examples above. Verify GGUF quant level and VRAM requirements for your hardware. Join the Unsloth Discord for community support and fine-tuning questions.
Talk to DEV.coRelated open-source tools
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GLM-4.7-Flash-GGUF FAQ
Can I use GLM-4.7-Flash commercially?
What VRAM do I need for local deployment?
Which inference framework should I choose?
Is fine-tuning supported?
Work with a software development agency
From first prototype to production, DEV.co delivers software development services around tools like GLM-4.7-Flash-GGUF. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to Deploy GLM-4.7-Flash?
Start with the Unsloth how-to guide and vLLM/SGLang examples above. Verify GGUF quant level and VRAM requirements for your hardware. Join the Unsloth Discord for community support and fine-tuning questions.