Can Gemma 2 9B run on RTX 3080 10GB?

YES — With Q3_K_S

B57Good

Estimated from fit model

Gemma 2 9B needs ~11.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q3_K_S quantization, expect ~69 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Gemma 2 9B at Q4_K_M needs 12.8 GB — too much for RTX 3080 10GB (10.0 GB). Runs at Q3_K_S (11.7 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.8 GB, exceeds 10.0 GB available

12.8 GB required10.0 GB available

128% VRAM needed

2.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

49.1 tok/s

TTFT

3940 ms

Safe context

Memory

12.8 GB / 10.0 GB

Offload

20%

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 2 9B on RTX 3080 10GB
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: 49.1 tok/s decode · 3.9s TTFT (warm) · 123 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs with offload (needs ~0.1 GB host RAM)	78.6 tok/s	1344 ms	7K
Coding	F	Too heavy	49.1 tok/s	3940 ms	7K
Agentic Coding	F	Too heavy	24.2 tok/s	11637 ms	7K
Reasoning	F	Too heavy	49.1 tok/s	4656 ms	7K
RAG	F	Too heavy	24.2 tok/s	14546 ms	7K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B66
Q3_K_S	3	4.4 GB	Low	B67
NVFP4	4	5.0 GB	Medium	B67
Q4_K_M	4	5.5 GB	Medium	B67
Q5_K_MBest for your GPU	5	6.5 GB	High	B67
Q6_K	6	7.4 GB	High	F0
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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

RTX 3060 12GBBest value

12 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.28.7 tok/s decode

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

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.53.1 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.40.2 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.

~$499 MSRP

Frequently asked questions

Can RTX 3080 10GB run Gemma 2 9B?

Yes, RTX 3080 10GB can run Gemma 2 9B at Q3_K_S quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 12.8 GB which exceeds available memory, but at Q3_K_S it needs only 11.7 GB. Expected decode speed: 68.5 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 12.8 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at Q3_K_S using 11.7 GB.

What is the best quantization for Gemma 2 9B?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is Q3_K_S, which uses 11.7 GB.

What speed will Gemma 2 9B run at on RTX 3080 10GB?

On RTX 3080 10GB, Gemma 2 9B achieves approximately 68.5 tokens per second decode speed with a time-to-first-token of 2827ms using Q3_K_S quantization.

Can RTX 3080 10GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 3080 10GB receives a F grade with 49.1 tok/s and 7K context.

What context window can Gemma 2 9B use on RTX 3080 10GB?

On RTX 3080 10GB, Gemma 2 9B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 9B feels slow on RTX 3080 10GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3080 10GB See all hardware for Gemma 2 9B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs with offload (needs ~0.1 GB host RAM)

78.6 tok/s

1344 ms

Coding

Too heavy

49.1 tok/s

3940 ms

Agentic Coding

Too heavy

24.2 tok/s

11637 ms

Reasoning

Too heavy

49.1 tok/s

4656 ms

RAG

Too heavy

24.2 tok/s

14546 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B66

Q3_K_S

4.4 GB

Low

B67

NVFP4

5.0 GB

Medium

B67

Q4_K_M

5.5 GB

Medium

B67

Q5_K_MBest for your GPU

6.5 GB

High

B67

Q6_K

7.4 GB

High

Q8_0

9.6 GB

Very High

F16

18.5 GB

Maximum

Frequently asked questions

Can RTX 3080 10GB run Gemma 2 9B?

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 12.8 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at Q3_K_S using 11.7 GB.

What is the best quantization for Gemma 2 9B?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is Q3_K_S, which uses 11.7 GB.

What speed will Gemma 2 9B run at on RTX 3080 10GB?

On RTX 3080 10GB, Gemma 2 9B achieves approximately 68.5 tokens per second decode speed with a time-to-first-token of 2827ms using Q3_K_S quantization.

Can RTX 3080 10GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 3080 10GB receives a F grade with 49.1 tok/s and 7K context.

What context window can Gemma 2 9B use on RTX 3080 10GB?

On RTX 3080 10GB, Gemma 2 9B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 9B feels slow on RTX 3080 10GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.