FitMyLLM
Zhipu AI/Mixture of Experts

Zhipu AIGLM 4.7 Flash

GLM 4.7 Flash — fast inference variant for latency-sensitive applications.

chatcodingreasoningtool_useThinking
31B
Parameters (3B active)
193K
Context length
12
Benchmarks
14
Quantizations
1.8M
HF downloads
Architecture
MoE
Released
2026-01-29
Layers
47
KV Heads
1
Head Dim
576
Family
glm

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
13.7 GB
13.1 + 0.6 KV
low
IQ3_XS3.5
14.7 GB
14.1 + 0.6 KV
low
Q3_K_S3.64
15.2 GB
14.6 + 0.6 KV
low
IQ3_M3.76
15.7 GB
15.1 + 0.6 KV
low
Q3_K_M4
16.6 GB
16.0 + 0.6 KV
low
Q3_K_L4.3
17.8 GB
17.2 + 0.6 KV
moderate
IQ4_XS4.46
18.4 GB
17.8 + 0.6 KV
moderate
Q4_K_S4.67
19.2 GB
18.6 + 0.6 KV
moderate
Q4_K_M4.89
20.1 GB
19.4 + 0.6 KV
good
Q5_K_S5.57
22.7 GB
22.1 + 0.6 KV
good
Q5_K_M5.7
23.2 GB
22.6 + 0.6 KV
good
Q6_K6.56
26.5 GB
25.9 + 0.6 KV
excellent
Q8_08.5
34.0 GB
33.4 + 0.6 KV
lossless
FP1616
63.1 GB
62.5 + 0.6 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN GLM 4.7 FLASH NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (12)

τ²-Bench91.8
AIME91.6
GPQA75.2
LiveCodeBench64.0
GPQA Diamond58.1
IFBench46.3
AA Intelligence30.1
AA Coding25.9
SciCode25.5
AA Long Context14.7
HLE14.4
Terminal-Bench3.8

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run glm-4.7-flash

Tag may need adjustment — check ollama.com/library/glm-4.7-flash for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA RTX A4500
20 GB VRAM • 640 GB/s
NVIDIA
$2000
Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for GLM 4.7 Flash

Build Hardware for GLM 4.7 Flash

GLM 4.7 Flash — fast inference variant for latency-sensitive applications.

▸ SPEC SHEET

GLM 4.7 Flash31B MoE.

▸ SPECIFICATIONS
PARAMETERS
31B (3B active)
ARCHITECTURE
Mixture of Experts
CONTEXT LENGTH
193K tokens
CAPABILITIES
chat, coding, reasoning, tool_use
RELEASE DATE
2026-01-29
PROVIDER
Zhipu AI
FAMILY
glm
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2513.1 GB82%
IQ3_XS3.514.1 GB84%
Q3_K_S3.6414.6 GB85%
IQ3_M3.7615.1 GB86%
Q3_K_M416.0 GB88%
Q3_K_L4.317.2 GB90%
IQ4_XS4.4617.8 GB92%
Q4_K_S4.6718.6 GB93%
Q4_K_M4.8919.4 GB94%
Q5_K_S5.5722.1 GB96%
Q5_K_M5.722.6 GB96%
Q6_K6.5625.9 GB97%
Q8_08.533.4 GB100%
FP161662.5 GB100%
§ 01BENCHMARK SCORES
GPQA75.2
GPQA Diamond58.1
LiveCodeBench64.0
AIME91.6
HLE14.4
AA Intelligence30.1
AA Coding25.9
aa_ifbench46.3
aa_terminal_bench3.8
aa_tau291.8
aa_scicode25.5
aa_lcr14.7
§ 02RUN COMMAND

Run GLM 4.7 Flash locally with Ollama — needs 19.4 GB VRAM at Q4_K_M:

$ollama run glm-4.7-flash
§ 03COMPATIBLE GPUs
30 @ Q4_K_M