GLM 4.7 Flash — Hardware Requirements & GPU Compatibility
ChatFunctions
GLM-4.7-Flash is a Mixture-of-Experts language model from Z.ai, part of the GLM 4 family, with about 31 billion total parameters and roughly 3 billion active per token. It is a chat and agentic model, positioned by Z.ai as a lightweight option for coding, tool use, and multi-turn agent tasks, with configurable reasoning-effort settings and support for extended thinking on harder problems. It supports a 202,752-token context window and is released under the MIT license. At this size, 4-bit quantization needs roughly 18GB of memory, putting it within reach of a single high-end consumer GPU in the 16-24GB range.
Runs great on a 24 GB GPU
Runs from 12 GB (IQ2_M · 16K)Best on 24 GB (Q4_K_M · ~142 tok/s · 64K)Macs from 16 GB (IQ2_M · ~46 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GLM 4.7 Flash
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 4.1 GB free · 16K context
Download: GLM-4.7-Flash-Q4_K_M.gguf18.31 GBunsloth
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3090 (24 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~135 tok/s
- generation speed
- Cost per 1M output tokens
- $0.44
- per 1M output tokens
How Much VRAM Does GLM 4.7 Flash Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.4 GB | 15.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~172 tok/s | |
| 14.8 GB | 15.7 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~169 tok/s | |
| 16.3 GB | 17.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~160 tok/s | |
| 16.7 GB | 17.7 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~158 tok/s | |
| 19.8 GB | 20.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~142 tok/s | |
| 23.3 GB | 24.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~128 tok/s | |
| 26.9 GB | 27.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 27 GB | |
| 32.3 GB | 33.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 32.4 GB |
Selected Q4_K_M
4-bit medium quantization19.9GBat 16K context
- Weights
- 18.7 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 4.1 GB free · ~142 tok/s
≥50 tok/s needs ≥258 GB/s · RTX 5090 ~196, RTX 3090 Ti ~142
Which Hardware Can Run GLM 4.7 Flash?
Q4_K_M · 19.8 GBGLM 4.7 Flash (Q4_K_M) requires 19.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 203K context window can add up to 10.2 GB, bringing total usage to 30.0 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run GLM 4.7 Flash, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
fits · 0.1 GB free · up to 16K ctx
1 GPU
RX 7900 XT~65Simulate output on RX 7900 XT32–40 GB
fits · 12.1 GB free · up to 198K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~196Simulate output on RTX 5090M5 Max36 GB~54Simulate output on Mac Studio M5 Max (36 GB)M532 GB~27Simulate output on MacBook Air 13" M5 (32 GB)Orin~41Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~26Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is GLM 4.7 Flash?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 3 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AIIndependent
Independent60.5%
#43/98 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1364.5
#67/187 open
- AIME 2024/2025
Epoch AIIndependent
Independent58.3%
#39/86 open
Alternatives to GLM 4.7 Flash
GLM 4 family · 18 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
Specifications
- Publisher
- Z.ai
- Family
- GLM 4
- Parameters
- 31.2B
- Active parameters
- 4.6B per token
- Architecture
- Glm4MoeLiteForCausalLM
- Context Length
- 202,752 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-01-19
- License
- MIT
Frequently Asked Questions
How much VRAM does GLM 4.7 Flash need?
GLM 4.7 Flash requires 19.8 GB of VRAM at Q4_K_M, or 63.5 GB at BF16. Full 203K context adds up to 10.2 GB (30.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 31.2B × 4.8 bits ÷ 8 = 18.7 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 11.3 GB (at full 203K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run GLM 4.7 Flash?
Yes, at Q5_K_L (23.7 GB) or lower. Higher quantizations like Q6_K (26.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for GLM 4.7 Flash?
For GLM 4.7 Flash, Q4_K_M (19.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 4.7 Flash on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run GLM 4.7 Flash, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 19.8 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run GLM 4.7 Flash locally?
Yes — GLM 4.7 Flash can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GLM 4.7 Flash?
At Q4_K_M, GLM 4.7 Flash can reach ~100 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~142 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.9 GB active ÷ (8000 × 0.65) = 0.56 ms, plus 47 layers × 0.055 ms = 2.58 ms, so 1000 ÷ 3.14 ms = ~318 tok/s
Estimated speed at Q4_K_M (19.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GLM 4.7 Flash?
At Q4_K_M, the download is about 18.73 GB. The full-precision BF16 version is 62.44 GB. The smallest option (IQ2_XXS) is 8.59 GB.
Which GPUs can run GLM 4.7 Flash?
8 consumer GPUs can run GLM 4.7 Flash at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run GLM 4.7 Flash?
122 devices with unified memory can run GLM 4.7 Flash at Q4_K_M (19.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.