Can GLM-4 9B run on RTX 3050 8GB?

YES — With Offload

A71Great

○Estimated from fit model

GLM-4 9B needs ~7.8 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.8 GB, 24.7 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

24.7 tok/s

TTFT

7825 ms

Safe context

21K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGLM-4 9B on RTX 3050 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 24.7 tok/s decode · 7.8s TTFT (warm) · 62 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	26.9 tok/s	3921 ms	21K
Coding	A	Runs with offload	26.9 tok/s	7189 ms	21K
Agentic Coding	A	Runs with offload	18.2 tok/s	15492 ms	21K
Reasoning	A	Runs with offload	26.9 tok/s	8496 ms	21K
RAG	A	Runs with offload	18.2 tok/s	19366 ms	21K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A75
Q3_K_S	3	4.4 GB	Low	A75
NVFP4Best for your GPU

Get started

Copy-paste commands to run GLM-4 9B on your machine.

Run

ollama run glm4

Frequently asked questions

See all results for RTX 3050 8GB See all hardware for GLM-4 9B

URL: https://willitrunai.com/can-run/glm-4-9b-on-rtx-3050-8gb

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