Can Gemma 4 31B run on NVIDIA A40 48GB?

YES — Runs Great

S89Excellent

Estimated from fit model

Gemma 4 31B needs ~39.1 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 39.1 GB, 23.1 tok/s, Runs well

39.1 GB required48.0 GB available

81% VRAM used

Fit status

Runs well

Decode

23.1 tok/s

TTFT

8393 ms

Safe context

26K

Memory

39.1 GB / 48.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemma 4 31B on NVIDIA A40 48GB
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: 23.1 tok/s decode · 8.4s TTFT (warm) · 58 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	23.1 tok/s	4578 ms	26K
Coding	S	Runs well	23.1 tok/s	8393 ms	26K
Agentic Coding	A	Very compromised (needs ~2 GB host RAM)	13.6 tok/s	20632 ms	26K
Reasoning	S	Runs well	23.1 tok/s	9918 ms	26K
RAG	A	Very compromised (needs ~2 GB host RAM)	13.6 tok/s	25790 ms	26K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A80
Q3_K_S	3	15.0 GB	Low	A81
NVFP4	4	17.2 GB	Medium	A82
Q4_K_M	4	18.7 GB	Medium	A83
Q5_K_M	5	22.1 GB	High	A84
Q6_K	6	25.2 GB	High	A85
Q8_0Best for your GPU	8	32.8 GB	Very High	A85
F16	16	62.9 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:31b

Your hardware

More models your NVIDIA A40 48GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	69 tok/s
Qwen 3.5 35B A3B	35B	S	75 tok/s
Qwen 3 32B	32B	S	30.2 tok/s
Qwen 2.5 VL 72B	72B	A	7.7 tok/s
Qwen3-Coder-Next	80B	A	19.9 tok/s

Frequently asked questions

Can NVIDIA A40 48GB run Gemma 4 31B?

Yes, NVIDIA A40 48GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 23.1 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 39.1 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 A40 48GB?

On NVIDIA A40 48GB, Gemma 4 31B achieves approximately 23.1 tokens per second decode speed with a time-to-first-token of 8393ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA A40 48GB receives a S grade with 23.1 tok/s and 26K context.

What context window can Gemma 4 31B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, Gemma 4 31B can safely use up to 26K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GB See all hardware for Gemma 4 31B

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