Can Gemma 2 9B run on RTX A5000 24GB?

YES — Runs Great

B69Good

Estimated from fit model

Gemma 2 9B needs ~14.2 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 14.2 GB, 102.8 tok/s, Runs well

14.2 GB required24.0 GB available

59% VRAM used

Fit status

Runs well

Decode

102.8 tok/s

TTFT

1883 ms

Safe context

Memory

14.2 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 2 9B on RTX A5000 24GB
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: 102.8 tok/s decode · 1.9s TTFT (warm) · 257 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	102.8 tok/s	1027 ms	8K
Coding	B	Runs well	102.8 tok/s	1883 ms	8K
Agentic Coding	A	Runs well	102.8 tok/s	2739 ms	8K
Reasoning	B	Runs well	102.8 tok/s	2225 ms	8K
RAG	A	Runs well	102.8 tok/s	3423 ms	8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B59
Q3_K_S	3	4.4 GB	Low	B60
NVFP4	4	5.0 GB	Medium	B60
Q4_K_M	4	5.5 GB	Medium	B60
Q5_K_M	5	6.5 GB	High	B61
Q6_K	6	7.4 GB	High	B61
Q8_0	8	9.6 GB	Very High	B63
F16Best for your GPU	16	18.5 GB	Maximum	B64

Get started

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

Run

ollama run gemma2

Frequently asked questions

Can RTX A5000 24GB run Gemma 2 9B?

Yes, RTX A5000 24GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 102.8 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 14.2 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 A5000 24GB?

On RTX A5000 24GB, Gemma 2 9B achieves approximately 102.8 tokens per second decode speed with a time-to-first-token of 1883ms using Q4_K_M quantization.

Can RTX A5000 24GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX A5000 24GB receives a B grade with 102.8 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX A5000 24GB?

On RTX A5000 24GB, 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 A5000 24GB See all hardware for Gemma 2 9B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-a5000-24gb" 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

102.8 tok/s

1027 ms

Coding

Runs well

102.8 tok/s

1883 ms

Agentic Coding

Runs well

102.8 tok/s

2739 ms

Reasoning

Runs well

102.8 tok/s

2225 ms

RAG

Runs well

102.8 tok/s

3423 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B59

Q3_K_S

4.4 GB

Low

B60

NVFP4

5.0 GB

Medium

B60

Q4_K_M

5.5 GB

Medium

B60

Q5_K_M

6.5 GB

High

B61

Q6_K

7.4 GB

High

B61

Q8_0

9.6 GB

Very High

B63

F16Best for your GPU

18.5 GB

Maximum

B64

Frequently asked questions

Can RTX A5000 24GB run Gemma 2 9B?

Yes, RTX A5000 24GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 102.8 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 14.2 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 A5000 24GB?

On RTX A5000 24GB, Gemma 2 9B achieves approximately 102.8 tokens per second decode speed with a time-to-first-token of 1883ms using Q4_K_M quantization.

Can RTX A5000 24GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX A5000 24GB receives a B grade with 102.8 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX A5000 24GB?

On RTX A5000 24GB, 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.