Can Gemma 3 4B run on RTX 4090 Laptop 16GB?

YES — Runs Great

A71Great

Estimated from fit model

Gemma 3 4B needs ~7.0 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 7.0 GB, 64.0 tok/s, Runs well

7.0 GB required16.0 GB available

44% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

85K

Memory

7.0 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 3 4B on RTX 4090 Laptop 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	64.0 tok/s	1650 ms	85K
Coding	A	Runs well	64.0 tok/s	3025 ms	85K
Agentic Coding	A	Runs well	64.0 tok/s	4400 ms	85K
Reasoning	A	Runs well	64.0 tok/s	3575 ms	85K
RAG	A	Runs well	64.0 tok/s	5500 ms	85K

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B67
Q3_K_S	3	2.0 GB	Low	B67
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 4B on your machine.

Run

ollama run gemma3:4b

Your hardware

More models your RTX 4090 Laptop 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	87.2 tok/s
Qwen 3 14B	14B	S	66.7 tok/s

Frequently asked questions

Can RTX 4090 Laptop 16GB run Gemma 3 4B?

Yes, RTX 4090 Laptop 16GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 64.0 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 3 4B achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on RTX 4090 Laptop 16GB receives a A grade with 64.0 tok/s and 85K context.

What context window can Gemma 3 4B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 3 4B can safely use up to 85K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for Gemma 3 4B

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