Can Gemma 3 12B run on RTX A4000 16GB?

YES — Tight Fit

A81Great

○Estimated from fit model

Gemma 3 12B needs ~15.0 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 15.0 GB, 45.0 tok/s, Tight fit

15.0 GB required16.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

45.0 tok/s

TTFT

4304 ms

Safe context

19K

Memory

15.0 GB / 16.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 3 12B on RTX A4000 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 45.0 tok/s decode · 4.3s TTFT (warm) · 113 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	45.0 tok/s	2348 ms	19K
Coding	A	Tight fit	45.0 tok/s	4304 ms	19K
Agentic Coding	F	Too heavy	21.3 tok/s	13191 ms	19K
Reasoning	A	Tight fit	45.0 tok/s	5086 ms	19K
RAG	F	Too heavy	21.3 tok/s	16488 ms	19K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A78
Q3_K_S	3	5.9 GB	Low	A79
NVFP4	4	6.7 GB	Medium	A80
Q4_K_M	4	7.3 GB	Medium	A81
Q5_K_M	5	8.6 GB	High	A81
Q6_KBest for your GPU	6	9.8 GB	High	A81
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your RTX A4000 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	S	39.7 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	37.6 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	35 tok/s
👁 Mistral Ministral 3 14B	14B	S	39.5 tok/s
👁 Mistral Codestral 2 25.08	22B	A	13.6 tok/s

Frequently asked questions

See all results for RTX A4000 16GB See all hardware for Gemma 3 12B

URL: https://willitrunai.com/can-run/gemma-3-12b-on-a4000-16gb

⇱ Gemma 3 12B on RTX A4000 16GB? TIGHT FIT