GLM 4.7 Flash
By Zhipu AI · China
Updated 2026-08-31
Overview
GLM-4.7-Flash is Zhipu AI's MoE coding model with 31B total parameters and ~3B active, delivering the best code performance-per-VRAM in the 30B class under a fully open MIT license.
When to pick this model
- Local coding assistant on a single RTX 3090/4090
- Agentic coding workflows needing top 30B-class benchmarks
- Projects requiring a fully unencumbered MIT license
- Multilingual (Chinese/English) code and chat tasks
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 62 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 wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 62 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 33 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 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 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 | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which hardware should you buy to run GLM 4.7 Flash?
To run GLM 4.7 Flash locally at Q4, you need ~19 GB of VRAM. The best value for this today is a GMKtec EVO-X2 64GB (Ryzen AI Max+ 395 mini PC) (64 GB unified memory, half the price of an RTX 5090).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| SWE-Bench Verified | 59.2 |
| LiveCodeBench v6 | 64 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- SWE-bench Verified 59.2 — top of the 30B class
- MIT license, fully unencumbered
- ~3B active parameters — 60-100 tok/s on a 3090/4090
- 128K context
Limitations
- ~19GB at Q4 — tight fit on 16GB cards
- Less fluent in French than Qwen or Mistral
Typical workloads
In our catalog grid, GLM 4.7 Flash is filed under Code (30B Class), Code Agents, Multilingual ZH/EN — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks; 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: MoE 31.2B total · ~3B active (glm4_moe_lite) · 128K context
Training: GLM 4.7 family from Zhipu AI / Z.ai. Flash variant tuned for code and agents — the strongest model in the 30B class at release.
The strongest coding model in the 30B class right now, MIT-licensed and fast enough for real-time local use.
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 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 62 GB.
Can GLM 4.7 Flash run without a GPU?
Yes — with roughly 33 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 40 tokens/sec on a mid-range GPU and up to 100 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 (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.