Can Gemma 4 26B A4B run on NVIDIA B200 180GB?

YES — Runs Great

A81Great

Estimated from fit model

Gemma 4 26B A4B needs ~38.2 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~1091 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) — 38.2 GB, 1091.2 tok/s, Runs well

38.2 GB required180.0 GB available

21% VRAM used

Fit status

Runs well

Decode

1091.2 tok/s

TTFT

350 ms

Safe context

256K

Memory

38.2 GB / 180.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B 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: 1091.2 tok/s decode · 350ms TTFT (warm) · 2728 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	1091.2 tok/s	350 ms	256K
Coding	A	Runs well	1091.2 tok/s	350 ms	256K
Agentic Coding	A	Runs well	1091.2 tok/s	350 ms	256K
Reasoning	A	Runs well	1091.2 tok/s	350 ms	256K
RAG	A	Runs well	1091.2 tok/s	350 ms	256K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A72
Q3_K_S	3	12.3 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 26B A4B on your machine.

Run

ollama run gemma4:26b

Your hardware

More models your NVIDIA B200 180GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA B200 180GB run Gemma 4 26B A4B?

Yes, NVIDIA B200 180GB can run Gemma 4 26B A4B with a A grade (Runs well). Expected decode speed: 1091.2 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 38.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Gemma 4 26B A4B achieves approximately 1091.2 tokens per second decode speed with a time-to-first-token of 350ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on NVIDIA B200 180GB receives a A grade with 1091.2 tok/s and 256K context.

What context window can Gemma 4 26B A4B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Gemma 4 26B A4B can safely use up to 256K 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 26B A4B

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