Can Gemma 4 31B run on NVIDIA B200 180GB?

YES — Runs Great

A85Great

Estimated from fit model

Gemma 4 31B needs ~52.6 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~377 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) — 52.6 GB, 376.8 tok/s, Runs well

52.6 GB required180.0 GB available

29% VRAM used

Fit status

Runs well

Decode

376.8 tok/s

TTFT

514 ms

Safe context

155K

Memory

52.6 GB / 180.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsGemma 4 31B on NVIDIA B200 180GB
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: 376.8 tok/s decode · 514ms TTFT (warm) · 942 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	A	Runs well	376.8 tok/s	350 ms	155K
Coding	A	Runs well	376.8 tok/s	514 ms	155K
Agentic Coding	S	Runs well	376.8 tok/s	747 ms	155K
Reasoning	A	Runs well	376.8 tok/s	607 ms	155K
RAG	S	Runs well	376.8 tok/s	934 ms	155K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A74
Q3_K_S	3	15.0 GB	Low	A74
NVFP4	4	17.2 GB	Medium	A74
Q4_K_M	4	18.7 GB	Medium	A74
Q5_K_M	5	22.1 GB	High	A75
Q6_K	6	25.2 GB	High	A75
Q8_0	8	32.8 GB	Very High	A76
F16Best for your GPU	16	62.9 GB	Maximum	A80

Get started

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

Run

ollama run gemma4:31b

Your hardware

More models your NVIDIA B200 180GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen 3.5 122B A10B	122B	S	270.2 tok/s
DeepSeek V4 Flash	284B	S	144.8 tok/s
Qwen 3.6 35B A3B	35B	S	854 tok/s
Qwen 3.5 35B A3B	35B	S	928.7 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Gemma 4 31B?

Yes, NVIDIA B200 180GB can run Gemma 4 31B with a A grade (Runs well). Expected decode speed: 376.8 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 52.6 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 B200 180GB?

On NVIDIA B200 180GB, Gemma 4 31B achieves approximately 376.8 tokens per second decode speed with a time-to-first-token of 514ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA B200 180GB receives a A grade with 376.8 tok/s and 155K context.

What context window can Gemma 4 31B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Gemma 4 31B can safely use up to 155K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB 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-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: