Can Gemma 4 31B run on NVIDIA A100 80GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Gemma 4 31B needs ~42.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~92 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) — 42.6 GB, 96.0 tok/s, Runs well

42.6 GB required80.0 GB available

53% VRAM used

Fit status

Runs well

Decode

96.0 tok/s

TTFT

2016 ms

Safe context

57K

Memory

42.6 GB / 80.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGemma 4 31B on NVIDIA A100 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: 96.0 tok/s decode · 2.0s TTFT (warm) · 240 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	96.0 tok/s	1100 ms	57K
Coding	S	Runs well	91.5 tok/s	2117 ms	57K
Agentic Coding	S	Runs well	96.0 tok/s	2932 ms	57K
Reasoning	S	Runs well	91.5 tok/s	2502 ms	57K
RAG	S	Runs well	96.0 tok/s	3665 ms	57K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A77
Q3_K_S	3	15.0 GB	Low	A78
NVFP4	4	17.2 GB	Medium	A78
Q4_K_M	4	18.7 GB	Medium	A78
Q5_K_M	5	22.1 GB	High	A79
Q6_K	6	25.2 GB	High	A80
Q8_0	8	32.8 GB	Very High	A81
F16Best for your GPU	16	62.9 GB	Maximum	A85

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s
Qwen 3.6 35B A3B	35B	S	217.7 tok/s
Qwen 3.5 35B A3B	35B	S	236.7 tok/s
Qwen 3 32B	32B	S	95.4 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Gemma 4 31B?

Yes, NVIDIA A100 80GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 91.5 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 42.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Gemma 4 31B achieves approximately 91.5 tokens per second decode speed with a time-to-first-token of 2117ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA A100 80GB receives a S grade with 91.5 tok/s and 57K context.

What context window can Gemma 4 31B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Gemma 4 31B can safely use up to 57K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Gemma 4 31B

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