Can Gemma 4 31B run on Quadro RTX 8000 48GB?

YES — Runs Great

S89Excellent

Estimated from fit model

Gemma 4 31B needs ~39.1 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~20 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, 19.7 tok/s, Runs well

39.1 GB required48.0 GB available

81% VRAM used

Fit status

Runs well

Decode

19.7 tok/s

TTFT

9826 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 Quadro RTX 8000 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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	19.7 tok/s	5360 ms	26K
Coding	S	Runs well	19.7 tok/s	9826 ms	26K
Agentic Coding	A	Very compromised (needs ~2 GB host RAM)	11.2 tok/s	25094 ms	26K
Reasoning	S	Runs well	19.7 tok/s	11613 ms	26K
RAG	A	Very compromised (needs ~2 GB host RAM)	11.2 tok/s	31368 ms	26K

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 Quadro RTX 8000 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 Quadro RTX 8000 48GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	58.9 tok/s
Qwen 3.5 35B A3B	35B	S	64.1 tok/s
Qwen 3 32B	32B	S	25.8 tok/s
Qwen 2.5 VL 72B	72B	A	6.3 tok/s
Qwen3-Coder-Next	80B	A	16.3 tok/s

Frequently asked questions

Can Quadro RTX 8000 48GB run Gemma 4 31B?

Yes, Quadro RTX 8000 48GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 19.7 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Gemma 4 31B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9826ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on Quadro RTX 8000 48GB receives a S grade with 19.7 tok/s and 26K context.

What context window can Gemma 4 31B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 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 Quadro RTX 8000 48GB See all hardware for Gemma 4 31B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/gemma-4-31b-on-quadro-rtx-8000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

19.7 tok/s

5360 ms

26K

Coding

Runs well

19.7 tok/s

9826 ms

26K

Agentic Coding

Very compromised (needs ~2 GB host RAM)

11.2 tok/s

25094 ms

26K

Reasoning

Runs well

19.7 tok/s

11613 ms

26K

RAG

Very compromised (needs ~2 GB host RAM)

11.2 tok/s

31368 ms

26K

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 Quadro RTX 8000 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

Model

Params

Grade

Decode

Capabilities

Qwen 3.6 35B A3B

35B

58.9 tok/s

Qwen 3.5 35B A3B

35B

64.1 tok/s

Qwen 3 32B

32B

25.8 tok/s

Qwen 2.5 VL 72B

72B

6.3 tok/s

Qwen3-Coder-Next

80B

16.3 tok/s

Frequently asked questions

Can Quadro RTX 8000 48GB run Gemma 4 31B?

Yes, Quadro RTX 8000 48GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 19.7 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Gemma 4 31B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9826ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on Quadro RTX 8000 48GB receives a S grade with 19.7 tok/s and 26K context.

What context window can Gemma 4 31B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 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.