Can Gemma 3 12B run on NVIDIA GB200 192GB?

YES — Runs Great

A75Great

○Estimated from fit model

Gemma 3 12B needs ~32.6 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~168 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 32.6 GB, 168.0 tok/s, Runs well

32.6 GB required192.0 GB available

17% VRAM used

Fit status

Runs well

Decode

168.0 tok/s

TTFT

1152 ms

Safe context

131K

Memory

32.6 GB / 192.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsGemma 3 12B on NVIDIA GB200 192GB
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: 168.0 tok/s decode · 1.2s TTFT (warm) · 420 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	A	Runs well	168.0 tok/s	629 ms	131K
Coding	A	Runs well	168.0 tok/s	1152 ms	131K
Agentic Coding	A	Runs well	168.0 tok/s	1676 ms	131K
Reasoning	A	Runs well	168.0 tok/s	1362 ms	131K
RAG	A	Runs well	168.0 tok/s	2095 ms	131K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B67
Q3_K_S	3	5.9 GB	Low	B67
NVFP4	4	6.7 GB	Medium	B67
Q4_K_M	4	7.3 GB	Medium	B67
Q5_K_M	5	8.6 GB	High	B67
Q6_K	6	9.8 GB	High	B67
Q8_0	8	12.8 GB	Very High	B67
F16Best for your GPU	16	24.6 GB	Maximum	B68

Get started

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

Run

ollama run gemma3:12b

Your hardware

More models your NVIDIA GB200 192GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	97.4 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	1016.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	378 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	378 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	270.2 tok/s

Frequently asked questions

See all results for NVIDIA GB200 192GB See all hardware for Gemma 3 12B

URL: https://willitrunai.com/can-run/gemma-3-12b-on-gb200-192gb

⇱ Gemma 3 12B on NVIDIA GB200 192GB? YES