Can Gemma 2 9B run on RTX 4090 Laptop 16GB?

YES — Runs Great

B70Good

Estimated from fit model

Gemma 2 9B needs ~13.1 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~59 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) — 13.1 GB, 59.0 tok/s, Runs well

13.1 GB required16.0 GB available

82% VRAM used

Fit status

Runs well

Decode

59.0 tok/s

TTFT

3282 ms

Safe context

Memory

13.1 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 2 9B on RTX 4090 Laptop 16GB
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: 59.0 tok/s decode · 3.3s TTFT (warm) · 148 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	B	Runs well	59.0 tok/s	1790 ms	8K
Coding	B	Runs well	59.0 tok/s	3282 ms	8K
Agentic Coding	B	Very compromised (needs ~0.7 GB host RAM)	33.6 tok/s	8390 ms	8K
Reasoning	B	Runs well	59.0 tok/s	3879 ms	8K
RAG	B	Very compromised (needs ~0.7 GB host RAM)	33.6 tok/s	10488 ms	8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+224)

Adds memory headroom for longer context windows and future model growth.69.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+64)

Adds memory headroom for longer context windows and future model growth.72.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$2,000 MSRP

Frequently asked questions

Can RTX 4090 Laptop 16GB run Gemma 2 9B?

Yes, RTX 4090 Laptop 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 59.0 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 2 9B achieves approximately 59.0 tokens per second decode speed with a time-to-first-token of 3282ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 4090 Laptop 16GB receives a B grade with 59.0 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for Gemma 2 9B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-rtx-4090-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: