Can Gemma 3 27B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

A84Great

Estimated from fit model

Gemma 3 27B needs ~36.9 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~102 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) — 36.9 GB, 107.1 tok/s, Runs well

36.9 GB required80.0 GB available

46% VRAM used

Fit status

Runs well

Decode

107.1 tok/s

TTFT

1808 ms

Safe context

77K

Memory

36.9 GB / 80.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGemma 3 27B on NVIDIA H100 PCIe 80GB
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: 107.1 tok/s decode · 1.8s TTFT (warm) · 268 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	107.1 tok/s	986 ms	77K
Coding	A	Runs well	102.0 tok/s	1898 ms	77K
Agentic Coding	S	Runs well	107.1 tok/s	2629 ms	77K
Reasoning	A	Runs well	107.1 tok/s	2136 ms	77K
RAG	S	Runs well	107.1 tok/s	3287 ms	77K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A73
Q3_K_S	3	13.2 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Gemma 3 27B?

Yes, NVIDIA H100 PCIe 80GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 102.0 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 36.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Gemma 3 27B achieves approximately 102.0 tokens per second decode speed with a time-to-first-token of 1898ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA H100 PCIe 80GB receives a A grade with 102.0 tok/s and 77K context.

What context window can Gemma 3 27B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Gemma 3 27B can safely use up to 77K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Gemma 3 27B

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