text model · GLM · Windows

Can I run GLM-4-9B-0414 on 32GB RAM Laptop (CPU/iGPU only)?

Compatibility verdict VRAM threshold engine

Yes, it runs slow on this hardware ~8 tok/s est.

Yes. GLM-4-9B-0414 runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~7.2 GB of ~28 GB usable).

Needs ~7.2 GB Device usable ~28 GB

Runs at Q4_K_M using ~7.2 GB of ~28 GB usable. You have room for FP16 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. 32GB RAM Laptop (CPU/iGPU only) leaves ~20.8 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed: ~7.2 GB
Usable on device: ~28 GB
Device memory: 32 GB
Best quant: Q4_K_M

Share on X Share on Reddit

Which quant fits

Quant ladder vs ~28 GB usable

Q2_K

~5.3 GB

Q3_K_M

~5.9 GB

Q4_K_M

~7.2 GB

Q5_K_M

~7.9 GB

Q6_K

~8.9 GB

Q8_0

~10.8 GB

FP16

~20.3 GB

The line marks 32GB RAM Laptop (CPU/iGPU only)'s ~28 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~28 W
Electricity / 1M tokens: ~$0.15
Pays for itself after: ~3,143M tok

At ~$0.15/kWh and the estimated ~8 tok/s, a million generated tokens costs about $0.15 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,100 32GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 3,143 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf bartowski/THUDM_GLM-4-9B-0414-GGUF:Q4_K_M

LM Studio

$ lms get bartowski/THUDM_GLM-4-9B-0414-GGUF

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model GLM

Parameters: 9B
Q4_K_M size: 5.74 GB
Q8_0 size: 9.31 GB
Context: 32k

Full GLM-4-9B-0414 requirements →

Device Windows

Memory: 32 GB ram
Usable for weights: ~28 GB
Power draw: ~28 W
Best runtime: Ollama (llama.cpp backend)

Best models for 32GB RAM Laptop (CPU/iGPU only) →

You could also run

Run GLM-4-9B-0414 on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Samsung Galaxy S24 Ultra Generic Android Phone (12GB RAM)iPhone 17 Pro iPhone Air

FAQ

Can 32GB RAM Laptop (CPU/iGPU only) run GLM-4-9B-0414?

Yes. GLM-4-9B-0414 runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~7.2 GB of ~28 GB usable).

How much memory does GLM-4-9B-0414 need?

32GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~5.74 GB; with KV cache and runtime overhead, budget ~7.2 GB at a 4k context.

What is the best tool to run GLM-4-9B-0414 on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

Embed this

A live badge for your model card or README, updated as the data is.

Markdown

[![GLM-4-9B-0414 on 32GB RAM Laptop (CPU/iGPU only)](https://localmodel.run/badge/glm-4-9b-0414/laptop-32gb.svg)](https://localmodel.run/can-i-run/glm-4-9b-0414/laptop-32gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.