Can Gemma 2 9B run on NVIDIA T4 16GB?

YES — Runs Great

B68Good

Estimated from fit model

Gemma 2 9B needs ~13.1 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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, 30.1 tok/s, Runs well

13.1 GB required16.0 GB available

82% VRAM used

Fit status

Runs well

Decode

30.1 tok/s

TTFT

6422 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 NVIDIA T4 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: 30.1 tok/s decode · 6.4s TTFT (warm) · 75 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	30.1 tok/s	3503 ms	8K
Coding	B	Runs well	30.1 tok/s	6422 ms	8K
Agentic Coding	C	Very compromised (needs ~0.7 GB host RAM)	16.5 tok/s	17083 ms	8K
Reasoning	B	Runs well	30.1 tok/s	7589 ms	8K
RAG	C	Very compromised (needs ~0.7 GB host RAM)	16.5 tok/s	21353 ms	8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on NVIDIA T4 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	5.0 GB	Medium	B63
Q4_K_M	4	5.5 GB	Medium	B64
Q5_K_M	5	6.5 GB	High	B65
Q6_K	6	7.4 GB	High	B66
Q8_0Best for your GPU	8	9.6 GB	Very High	B66
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 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+40)

Raises estimated decode speed by about 35%.40.7 tok/s decode

Raises estimated decode speed by about 35%.

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+616)

Raises estimated decode speed by about 134%.70.4 tok/s decode

Raises estimated decode speed by about 134%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+352)

Raises estimated decode speed by about 172%.81.8 tok/s decode

Raises estimated decode speed by about 172%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run Gemma 2 9B?

Yes, NVIDIA T4 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 30.1 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 NVIDIA T4 16GB?

On NVIDIA T4 16GB, Gemma 2 9B achieves approximately 30.1 tokens per second decode speed with a time-to-first-token of 6422ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on NVIDIA T4 16GB receives a B grade with 30.1 tok/s and 8K context.

What context window can Gemma 2 9B use on NVIDIA T4 16GB?

On NVIDIA T4 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 NVIDIA T4 16GB See all hardware for Gemma 2 9B

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<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-t4-16gb" 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

30.1 tok/s

3503 ms

Coding

Runs well

30.1 tok/s

6422 ms

Agentic Coding

Very compromised (needs ~0.7 GB host RAM)

16.5 tok/s

17083 ms

Reasoning

Runs well

30.1 tok/s

7589 ms

RAG

Very compromised (needs ~0.7 GB host RAM)

16.5 tok/s

21353 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B62

Q3_K_S

4.4 GB

Low

B63

NVFP4

5.0 GB

Medium

B63

Q4_K_M

5.5 GB

Medium

B64

Q5_K_M

6.5 GB

High

B65

Q6_K

7.4 GB

High

B66

Q8_0Best for your GPU

9.6 GB

Very High

B66

F16

18.5 GB

Maximum

Frequently asked questions

Can NVIDIA T4 16GB run Gemma 2 9B?

Yes, NVIDIA T4 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 30.1 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 NVIDIA T4 16GB?

On NVIDIA T4 16GB, Gemma 2 9B achieves approximately 30.1 tokens per second decode speed with a time-to-first-token of 6422ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on NVIDIA T4 16GB receives a B grade with 30.1 tok/s and 8K context.

What context window can Gemma 2 9B use on NVIDIA T4 16GB?

On NVIDIA T4 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.