Can Gemma 4 31B run on RTX A5000 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 31B needs ~37.0 GB but RTX A5000 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 37.0 GB, exceeds 24.0 GB available

37.0 GB required24.0 GB available

154% VRAM needed

13.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.1 tok/s

TTFT

21270 ms

Safe context

Memory

37.0 GB / 24.0 GB

Offload

40%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 31B on RTX A5000 24GB
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: 9.1 tok/s decode · 21.3s TTFT (warm) · 23 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 37.0 GB, but this setup only exposes 24.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	14.5 tok/s	7290 ms	4K
Coding	F	Too heavy	9.1 tok/s	21270 ms	4K
Agentic Coding	F	Too heavy	4.5 tok/s	62282 ms	4K
Reasoning	F	Too heavy	9.1 tok/s	25138 ms	4K
RAG	F	Too heavy	4.5 tok/s	77852 ms	4K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	S87
Q3_K_S	3	15.0 GB	Low	S87
NVFP4	4	17.2 GB	Medium	S86
Q4_K_MBest for your GPU	4	18.7 GB	Medium	S86
Q5_K_M	5	22.1 GB	High	F0
Q6_K	6	25.2 GB	High	F0
Q8_0	8	32.8 GB	Very High	F0
F16	16	62.9 GB	Maximum	F0

Upgrade options

Hardware that runs Gemma 4 31B well

RTX 5090 32GBBest value

32 GB VRAM (+8)1792 GB/s (+1024)

Makes the model fit on the accelerator instead of staying completely out of reach.36.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 303%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBNVIDIA upgrade

32 GB VRAM (+8)896 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 153%.

~$2,499 MSRP

RTX A6000 48GBBudget pick

48 GB VRAM (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.32.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,650 MSRP

Frequently asked questions

Can RTX A5000 24GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than RTX A5000 24GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 37.0 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 RTX A5000 24GB?

On RTX A5000 24GB, Gemma 4 31B achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21270ms using Q4_K_M quantization.

Can RTX A5000 24GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX A5000 24GB receives a F grade with 9.1 tok/s and 4K context.

What context window can Gemma 4 31B use on RTX A5000 24GB?

On RTX A5000 24GB, Gemma 4 31B can safely use up to 4K 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 RTX A5000 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX A5000 24GB See all hardware for Gemma 4 31B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

14.5 tok/s

7290 ms

Coding

Too heavy

9.1 tok/s

21270 ms

Agentic Coding

Too heavy

4.5 tok/s

62282 ms

Reasoning

Too heavy

9.1 tok/s

25138 ms

RAG

Too heavy

4.5 tok/s

77852 ms

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	S87
Q3_K_S	3	15.0 GB	Low	S87
NVFP4	4	17.2 GB	Medium	S86
Q4_K_MBest for your GPU	4	18.7 GB	Medium	S86
Q5_K_M	5	22.1 GB	High	F0
Q6_K	6	25.2 GB	High	F0
Q8_0	8	32.8 GB	Very High	F0
F16	16	62.9 GB	Maximum	F0

Frequently asked questions

Can RTX A5000 24GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than RTX A5000 24GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 37.0 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 RTX A5000 24GB?

On RTX A5000 24GB, Gemma 4 31B achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21270ms using Q4_K_M quantization.

Can RTX A5000 24GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX A5000 24GB receives a F grade with 9.1 tok/s and 4K context.

What context window can Gemma 4 31B use on RTX A5000 24GB?

On RTX A5000 24GB, Gemma 4 31B can safely use up to 4K 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 RTX A5000 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.