Can Gemma 4 31B run on NVIDIA A100 40GB?

YES — With Offload

S90Excellent

Estimated from fit model

Gemma 4 31B needs ~38.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~73 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 38.6 GB, 73.2 tok/s, Runs with offload

38.6 GB required40.0 GB available

97% VRAM used

Fit status

Runs with offload

Decode

73.2 tok/s

TTFT

2643 ms

Safe context

18K

Memory

38.6 GB / 40.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsGemma 4 31B 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: 73.2 tok/s decode · 2.6s TTFT (warm) · 183 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	73.2 tok/s	1442 ms	18K
Coding	S	Runs with offload	73.2 tok/s	2643 ms	18K
Agentic Coding	F	Too heavy	30.1 tok/s	9353 ms	18K
Reasoning	S	Runs with offload	73.2 tok/s	3124 ms	18K
RAG	F	Too heavy	30.1 tok/s	11691 ms	18K

Inference speed

Gemma 4 31B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 31B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~26 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	25.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	25.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	23.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	19.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	18.7	Fits
RTX 5090 32GB	32 GB	Q4_K_M	15.0	Heavy offload
RTX 4090 24GB	24 GB	Q4_K_M	13.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	13.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	11.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	10.2	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	9.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.8	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	5.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A82
Q3_K_S	3	15.0 GB	Low	A83
NVFP4	4	17.2 GB	Medium	A84
Q4_K_M	4	18.7 GB	Medium	A85
Q5_K_M	5	22.1 GB	High	S86
Q6_K	6	25.2 GB	High	S85
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 A100 40GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen 3.5 35B A3B	35B	S	180.5 tok/s
Qwen 3 32B	32B	S	72.8 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Gemma 4 31B?

Yes, NVIDIA A100 40GB can run Gemma 4 31B with a S grade (Runs with offload). Expected decode speed: 73.2 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 38.6 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 40GB?

On NVIDIA A100 40GB, Gemma 4 31B achieves approximately 73.2 tokens per second decode speed with a time-to-first-token of 2643ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA A100 40GB receives a S grade with 73.2 tok/s and 18K context.

What context window can Gemma 4 31B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Gemma 4 31B can safely use up to 18K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 31B feels slow on NVIDIA A100 40GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA A100 40GB See all hardware for Gemma 4 31B

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