Can Gemma 3 27B run on NVIDIA A100 40GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Gemma 3 27B needs ~32.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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, 51.7 tok/s, Runs well

32.6 GB required40.0 GB available

82% VRAM used

Fit status

Runs well

Decode

51.7 tok/s

TTFT

3741 ms

Safe context

27K

Memory

32.6 GB / 40.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsGemma 3 27B on NVIDIA A100 40GB
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: 51.7 tok/s decode · 3.7s TTFT (warm) · 129 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	S	Runs well	51.7 tok/s	2041 ms	27K
Coding	S	Runs well	49.3 tok/s	3928 ms	27K
Agentic Coding	A	Very compromised (needs ~1.4 GB host RAM)	32.0 tok/s	8796 ms	27K
Reasoning	S	Runs well	51.7 tok/s	4421 ms	27K
RAG	A	Very compromised (needs ~1.4 GB host RAM)	32.0 tok/s	10995 ms

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A77
Q3_K_S	3	13.2 GB	Low	A78
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 A100 40GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Gemma 3 27B?

Yes, NVIDIA A100 40GB can run Gemma 3 27B with a S grade (Runs well). Expected decode speed: 49.3 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 32.6 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 A100 40GB?

On NVIDIA A100 40GB, Gemma 3 27B achieves approximately 49.3 tokens per second decode speed with a time-to-first-token of 3928ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA A100 40GB receives a S grade with 49.3 tok/s and 27K context.

What context window can Gemma 3 27B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Gemma 3 27B can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Gemma 3 27B

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