GLM 4.7 Flash
By Zhipu AI · China
Updated 2026-07-13
Overview
Zhipu AI's compact 3B variant of GLM 4.7, MIT-licensed with a 128k context. Optimized for low-latency bilingual Chinese-English chat.
When to pick this model
- Bilingual zh/en chat assistants where latency is critical
- Lightweight chat backends with a strict permissive license requirement
- Long-context summarization on small GPUs
- Cost-sensitive serving at scale where 30B variants are overkill
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 1.7 GB |
| Q5_K_M | 2.1 GB |
| Q8_0 | 3.2 GB |
| FP16 (no quantization) | 6 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, GLM 4.7 Flash fits an 8 GB consumer card at Q4_K_M (1.7 GB). Stepping up to Q8_0 nearly doubles the footprint to 3.2 GB, and unquantized FP16 weights take 6 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, GLM 4.7 Flash needs roughly 3.9 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 50 tokens/sec on entry-level GPUs, on the order of 85 tokens/sec on a mid-range card, and up to 130 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches GLM 4.7 Flash to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for GLM 4.7 Flash |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (6 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (6 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (6 GB used) |
| 32 GB | RTX 5090 | FP16 (6 GB used) |
Which GPU should you buy to run GLM 4.7 Flash?
To run GLM 4.7 Flash locally at Q4, you need ~1.7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- MIT license — among the most permissive in the open ecosystem
- 128k context in a 3B footprint
- Strong Chinese and English performance
- Compact ~1.7GB VRAM at Q4
Limitations
- Gated on Hugging Face despite the open license
- Less versatile than the 30B GLM 4.7 variants
Typical workloads
In our catalog grid, GLM 4.7 Flash is filed under ZH / EN Multilingual, Compact Assistant, Fast Inference — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense transformer · 3B parameters · 128k context
Training: GLM 4.7 family from Zhipu AI / THUDM (Tsinghua). Flash variant optimized for latency, focus on zh/en.
MIT-licensed, fast, and bilingual — the GLM 4.7 to reach for when you need throughput over peak capability.
Quick start
ollama run glm-4.7-flashOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does GLM 4.7 Flash need?
At the recommended Q4_K_M quantization, GLM 4.7 Flash needs about 1.7 GB of VRAM. Q8_0 takes 3.2 GB, and unquantized FP16 weights take 6 GB.
Can GLM 4.7 Flash run without a GPU?
Yes — with roughly 3.9 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does GLM 4.7 Flash support?
GLM 4.7 Flash supports a 125k-token context window (128,000 tokens).
Can I use GLM 4.7 Flash commercially?
Yes. GLM 4.7 Flash is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is GLM 4.7 Flash on consumer hardware?
Our compatibility engine estimates on the order of 85 tokens/sec on a mid-range GPU and up to 130 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of GLM 4.7 Flash should I download first?
Start with Q4_K_M (1.7 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at FP16.