Can Gemma 3 12B run on Quadro RTX 6000 24GB?

YES — Runs Great

A85Great

○Estimated from fit model

Gemma 3 12B needs ~15.8 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~67 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) — 15.8 GB, 66.5 tok/s, Runs well

15.8 GB required24.0 GB available

66% VRAM used

Fit status

Runs well

Decode

66.5 tok/s

TTFT

2911 ms

Safe context

43K

Memory

15.8 GB / 24.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 3 12B on Quadro RTX 6000 24GB
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: 66.5 tok/s decode · 2.9s TTFT (warm) · 166 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	A	Runs well	66.5 tok/s	1588 ms	43K
Coding	A	Runs well	66.5 tok/s	2911 ms	43K
Agentic Coding	A	Tight fit	66.5 tok/s	4234 ms	43K
Reasoning	A	Runs well	66.5 tok/s	3440 ms	43K
RAG	A	Tight fit	66.5 tok/s	5292 ms	43K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A76
NVFP4	4	6.7 GB	Medium	A76
Q4_K_M	4	7.3 GB	Medium	A76
Q5_K_M	5	8.6 GB	High	A77
Q6_K	6	9.8 GB	High	A78
Q8_0Best for your GPU	8	12.8 GB	Very High	A80
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 Quadro RTX 6000 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	30.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.5 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	37.9 tok/s

Frequently asked questions

See all results for Quadro RTX 6000 24GB See all hardware for Gemma 3 12B

URL: https://willitrunai.com/can-run/gemma-3-12b-on-quadro-rtx-6000-24gb

⇱ Gemma 3 12B on Quadro RTX 6000 24GB? YES