Can Gemma 4 31B run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Gemma 4 31B needs ~44.2 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~72 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) — 44.2 GB, 75.2 tok/s, Runs well

44.2 GB required96.0 GB available

46% VRAM used

Fit status

Runs well

Decode

75.2 tok/s

TTFT

2574 ms

Safe context

73K

Memory

44.2 GB / 96.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsGemma 4 31B on RTX PRO 6000 Blackwell Server Edition 96GB
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: 75.2 tok/s decode · 2.6s TTFT (warm) · 188 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	71.6 tok/s	1474 ms	73K
Coding	S	Runs well	71.6 tok/s	2703 ms	73K
Agentic Coding	S	Runs well	71.6 tok/s	3931 ms	73K
Reasoning	S	Runs well	71.6 tok/s	3194 ms	73K
RAG	S	Runs well	71.6 tok/s	4914 ms	73K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A77
Q3_K_S	3	15.0 GB	Low	A77
NVFP4	4

Get started

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

Run

ollama run gemma4:31b

Your hardware

More models your RTX PRO 6000 Blackwell Server Edition 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	19.4 tok/s
Qwen 3.5 122B A10B	122B

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run Gemma 4 31B?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 71.6 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 44.2 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 PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Gemma 4 31B achieves approximately 71.6 tokens per second decode speed with a time-to-first-token of 2703ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX PRO 6000 Blackwell Server Edition 96GB receives a S grade with 71.6 tok/s and 73K context.

What context window can Gemma 4 31B use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Gemma 4 31B can safely use up to 73K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX PRO 6000 Blackwell Server Edition 96GB See all hardware for Gemma 4 31B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-4-31b-on-rtx-pro-6000-blackwell-server-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: