Can Gemma 2 9B run on RTX 4000 Ada 20GB?

YES — Runs Great

B69Good

Estimated from fit model

Gemma 2 9B needs ~13.8 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 13.8 GB, 53.7 tok/s, Runs well

13.8 GB required20.0 GB available

69% VRAM used

Fit status

Runs well

Decode

53.7 tok/s

TTFT

3605 ms

Safe context

Memory

13.8 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGemma 2 9B on RTX 4000 Ada 20GB
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: 53.7 tok/s decode · 3.6s TTFT (warm) · 134 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	53.7 tok/s	1966 ms	8K
Coding	B	Runs well	53.7 tok/s	3605 ms	8K
Agentic Coding	B	Tight fit	53.7 tok/s	5243 ms	8K
Reasoning	B	Runs well	53.7 tok/s	4260 ms	8K
RAG	B	Tight fit	53.7 tok/s	6554 ms	8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B60
Q3_K_S	3	4.4 GB	Low	B61
NVFP4	4	5.0 GB	Medium	B61
Q4_K_M	4	5.5 GB	Medium	B62
Q5_K_M	5	6.5 GB	High	B62
Q6_K	6	7.4 GB	High	B63
Q8_0Best for your GPU	8	9.6 GB	Very High	B65
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

ollama run gemma2

Frequently asked questions

Can RTX 4000 Ada 20GB run Gemma 2 9B?

Yes, RTX 4000 Ada 20GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 53.7 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.8 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Gemma 2 9B achieves approximately 53.7 tokens per second decode speed with a time-to-first-token of 3605ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 4000 Ada 20GB receives a B grade with 53.7 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, 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 4000 Ada 20GB See all hardware for Gemma 2 9B

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<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

53.7 tok/s

1966 ms

Coding

Runs well

53.7 tok/s

3605 ms

Agentic Coding

Tight fit

53.7 tok/s

5243 ms

Reasoning

Runs well

53.7 tok/s

4260 ms

RAG

Tight fit

53.7 tok/s

6554 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B60

Q3_K_S

4.4 GB

Low

B61

NVFP4

5.0 GB

Medium

B61

Q4_K_M

5.5 GB

Medium

B62

Q5_K_M

6.5 GB

High

B62

Q6_K

7.4 GB

High

B63

Q8_0Best for your GPU

9.6 GB

Very High

B65

F16

18.5 GB

Maximum

Frequently asked questions

Can RTX 4000 Ada 20GB run Gemma 2 9B?

Yes, RTX 4000 Ada 20GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 53.7 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.8 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Gemma 2 9B achieves approximately 53.7 tokens per second decode speed with a time-to-first-token of 3605ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 4000 Ada 20GB receives a B grade with 53.7 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, 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.