Can Gemma 3 12B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

A76Great

Estimated from fit model

Gemma 3 12B needs ~27.5 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 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) — 27.5 GB, 168.0 tok/s, Runs well

27.5 GB required141.0 GB available

20% VRAM used

Fit status

Runs well

Decode

168.0 tok/s

TTFT

1152 ms

Safe context

131K

Memory

27.5 GB / 141.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsGemma 3 12B on NVIDIA H200 PCIe 141GB
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 H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B68
Q3_K_S	3	5.9 GB	Low	B68
NVFP4	4

Get started

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

Run

ollama run gemma3:12b

Your hardware

More models your NVIDIA H200 PCIe 141GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	58.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Gemma 3 12B?

Yes, NVIDIA H200 PCIe 141GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 168.0 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 27.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Gemma 3 12B achieves approximately 168.0 tokens per second decode speed with a time-to-first-token of 1152ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on NVIDIA H200 PCIe 141GB receives a A grade with 168.0 tok/s and 131K context.

What context window can Gemma 3 12B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Gemma 3 12B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for Gemma 3 12B

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