Can Gemma 4 31B run on NVIDIA A800 80GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Gemma 4 31B needs ~42.6 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~85 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, 84.6 tok/s, Runs well

42.6 GB required80.0 GB available

53% VRAM used

Fit status

Runs well

Decode

84.6 tok/s

TTFT

2288 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 A800 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: 84.6 tok/s decode · 2.3s TTFT (warm) · 212 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	84.6 tok/s	1248 ms	57K
Coding	S	Runs well	84.6 tok/s	2288 ms	57K
Agentic Coding	S	Runs well	84.6 tok/s	3328 ms	57K
Reasoning	S	Runs well	84.6 tok/s	2704 ms	57K
RAG	S	Runs well	84.6 tok/s	4160 ms	57K

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 A800 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 A800 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	15.5 tok/s
Qwen 3.5 122B A10B	122B	A	45.9 tok/s
Qwen 3.6 35B A3B	35B	S	191.8 tok/s
Qwen 3.5 35B A3B	35B	S	208.6 tok/s
Qwen 3 32B	32B	S	84.1 tok/s

Frequently asked questions

Can NVIDIA A800 80GB run Gemma 4 31B?

Yes, NVIDIA A800 80GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 84.6 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 A800 80GB?

On NVIDIA A800 80GB, Gemma 4 31B achieves approximately 84.6 tokens per second decode speed with a time-to-first-token of 2288ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA A800 80GB receives a S grade with 84.6 tok/s and 57K context.

What context window can Gemma 4 31B use on NVIDIA A800 80GB?

On NVIDIA A800 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 A800 80GB See all hardware for Gemma 4 31B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

84.6 tok/s

1248 ms

57K

Coding

Runs well

84.6 tok/s

2288 ms

57K

Agentic Coding

Runs well

84.6 tok/s

3328 ms

57K

Reasoning

Runs well

84.6 tok/s

2704 ms

57K

RAG

Runs well

84.6 tok/s

4160 ms

57K

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

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

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA A800 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

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

15.5 tok/s

Qwen 3.5 122B A10B

122B

45.9 tok/s

Qwen 3.6 35B A3B

35B

191.8 tok/s

Qwen 3.5 35B A3B

35B

208.6 tok/s

Qwen 3 32B

32B

84.1 tok/s

Frequently asked questions

Can NVIDIA A800 80GB run Gemma 4 31B?

Yes, NVIDIA A800 80GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 84.6 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 A800 80GB?

On NVIDIA A800 80GB, Gemma 4 31B achieves approximately 84.6 tokens per second decode speed with a time-to-first-token of 2288ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA A800 80GB receives a S grade with 84.6 tok/s and 57K context.

What context window can Gemma 4 31B use on NVIDIA A800 80GB?

On NVIDIA A800 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.