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

NO — Won't Fit

F0Won't run

Estimated from fit model

GLM-4 9B needs ~7.7 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 7.7 GB, exceeds 4.0 GB available

7.7 GB required4.0 GB available

193% VRAM needed

3.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.6 tok/s

TTFT

34346 ms

Safe context

Memory

7.7 GB / 4.0 GB

Offload

50%

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGLM-4 9B on RTX 3050 Ti Laptop 4GB
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: 5.6 tok/s decode · 34.3s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 7.7 GB, but this setup only exposes 4.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.6 tok/s	18818 ms	4K
Coding	F	Too heavy	5.2 tok/s	37566 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	64158 ms	4K
Reasoning	F	Too heavy	5.2 tok/s	44396 ms	4K
RAG	F	Too heavy	4.4 tok/s	80197 ms	4K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	F0
Q3_K_S	3	4.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs GLM-4 9B well

RTX 3050 8GBBudget pick

8 GB VRAM (+4)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.21.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+4)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.40.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

RTX 4060 8GBNVIDIA upgrade

8 GB VRAM (+4)272 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.28.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

RTX 3080 Ti 12GBBiggest leap

12 GB VRAM (+8)912 GB/s (+720)

Makes the model fit on the accelerator instead of staying completely out of reach.126 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,199 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run GLM-4 9B?

No, GLM-4 9B requires more memory than RTX 3050 Ti Laptop 4GB provides.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-4 9B?

The recommended quantization for GLM-4 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-4 9B run at on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, GLM-4 9B achieves approximately 5.2 tokens per second decode speed with a time-to-first-token of 37566ms using Q4_K_M quantization.

Can RTX 3050 Ti Laptop 4GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on RTX 3050 Ti Laptop 4GB receives a F grade with 5.2 tok/s and 4K context.

What context window can GLM-4 9B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, GLM-4 9B can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if GLM-4 9B feels slow on RTX 3050 Ti Laptop 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/glm-4-9b-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: