GPUs tracked39Price history3 dayssince Sep 3, 2026Price points (24h)11,753 pointsBiggest 24h dropBiggest 24h riseData updated Sep 5, 2026

How Much VRAM Does GLM 4.7 Flash Need to Run Locally?

See devices that run it

GLM 4.7 Flash is a MoE model with 30B total parameters and 3B active per token. At Q4 it needs 19GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 4GB of VRAM plus 17GB of RAM. The cheapest device that holds it is the Radeon RX 7900 XT, with no used-price data yet, at roughly 113.7 tokens/s. In offload mode the cheapest card that runs it is the GeForce RTX 2080 Ti, at roughly 37 tokens/s.

GLM 4.7 Flash details

Launch dateJan 19, 2026
Hugging Face repozai-org/GLM-4.7-Flash
Revision
LicenseMIT
ArchitectureMoE
Ollama tag
Downloads (30d)1,935,018
Min VRAM at Q419 GB · Q4_K_M · 8K
Experts offloaded4 GB VRAM + 17 GB RAM
Parameters30B
MoE3B
Layers47
Hidden size2,048
KV heads20
Head dim256
Max context198K
VendorZ.ai / Zhipu AI

VRAM needed for GLM 4.7 Flash by quantization and context

Total at 8K

MoE: all 30B parameters must stay resident in memory, while speed is set by the 3B active per token.

QuantizationWeightsGBTotal at 8KGBTotal at 32KGBTotal at 128KGB
Q4_K_M18.3GGUF18.119.424.3
Q5_K_M21.4GGUF21.722.927.9
Q8_031.8GGUF32.533.738.7
FP1659.9GGUF60.161.366.3

Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 198K limit show a dash.

Which GPUs can run GLM 4.7 Flash?

Q4_K_M · 8K context · sorted by eBay used price
DeviceVRAMGBUsed priceEst. t/sSuggested context
Radeon RX 7900 XT20No listings113.732K
Arc Pro B6024No listings7532K
GeForce RTX 309024No listings144.432K
GeForce RTX 409024No listings148.932K
GeForce RTX 5090 D V224No listings165.632K
Radeon RX 7900 XTX24No listings125.132K
RTX PRO 4000 Blackwell24No listings123.832K
Arc Pro B6532No listings90.9128K
Arc Pro B7032No listings90.9128K
GeForce RTX 4080 SUPER 32GB (modded)32No listings129.6128K
GeForce RTX 509032No listings180.9128K
GeForce RTX 5090 D32No listings180.9128K
Instinct MI10032No listings140.5128K
Radeon AI PRO R970032No listings100.1128K
Radeon PRO W780032No listings93.9128K
RTX PRO 4500 Blackwell32No listings141.7128K
Tesla V100 32GB32No listings142128K
A100 40GB PCIe40No listings173.6128K
CMP 170HX 40GB (modded)40No listings173.8128K
GeForce RTX 4090 48GB (modded)48No listings148.9128K
Radeon PRO W790048No listings118.5128K
RTX PRO 5000 Blackwell 48GB48No listings165.6128K
CMP 170HX 64GB (modded)64No listings171.4128K
Instinct MI21064No listings157.7128K
RTX PRO 5000 Blackwell 72GB72No listings165.6128K
RTX PRO 6000D84No listings174128K
RTX PRO 6000 Blackwell96No listings180.9128K
Mac Studio M4 Max 128GB128No listings96.3128K
Mac Studio M5 Max 128GB128No listings103.4128K
MacBook Pro M4 Max 128GB128No listings96.3128K
MacBook Pro M5 Max 128GB128No listings103.4128K
Mac Studio M3 Ultra 512GB512No listings121.1128K

Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.

GPUs that run GLM 4.7 Flash with experts offloaded to RAM

Expert weights live in system RAM (needs at least 17 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 4 GB at Q4. Speed is bound by RAM bandwidth (estimated at 70 GB/s) and is far slower than a full-VRAM setup.

DeviceVRAMGBUsed priceRAM neededGBEst. t/s
GeForce RTX 2080 Ti111737
GeForce RTX 4080161737.4
GeForce RTX 5070 Ti161737.9
GeForce RTX 5080161738.1
Radeon RX 7800 XT161736
Radeon RX 9070 XT161736.1
Tesla V100 16GB161737.9

Run GLM 4.7 Flash with Ollama, llama.cpp or vLLM

Ollama

The Ollama library has no official tag for it yet.

llama.cpp

llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.

llama-server -hf unsloth/GLM-4.7-Flash-GGUF:Q4_K_M -c 8192
vLLM

vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.

vllm serve zai-org/GLM-4.7-Flash

File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology

FAQ

How much VRAM does GLM 4.7 Flash need?

At Q4_K_M with an 8K context GLM 4.7 Flash needs about 19GB of VRAM; 24GB leaves comfortable headroom.

What is the cheapest GPU that runs GLM 4.7 Flash?

The Radeon RX 7900 XT: 20GB of VRAM at roughly 113.7 tokens/s. We have no used-price data for it yet.

Can you run GLM 4.7 Flash with Ollama?

Not yet: the Ollama library has no official tag for it. You can load the GGUF with llama.cpp instead.