Can Gemma 4 26B A4B run on NVIDIA V100 32GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Gemma 4 26B A4B needs ~23.4 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~98 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) — 23.4 GB, 97.9 tok/s, Runs well

23.4 GB required32.0 GB available

73% VRAM used

Fit status

Runs well

Decode

97.9 tok/s

TTFT

1977 ms

Safe context

53K

Memory

23.4 GB / 32.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B on NVIDIA V100 32GB
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: 97.9 tok/s decode · 2.0s TTFT (warm) · 245 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	97.9 tok/s	1078 ms	53K
Coding	S	Runs well	97.9 tok/s	1977 ms	53K
Agentic Coding	S	Tight fit	97.9 tok/s	2876 ms	53K
Reasoning	S	Runs well	97.9 tok/s	2337 ms	53K
RAG	S	Tight fit	97.9 tok/s	3595 ms	53K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A81
Q3_K_S	3	12.3 GB	Low	A82
NVFP4	4	14.1 GB	Medium	A83
Q4_K_M	4	15.4 GB	Medium	A84
Q5_K_M	5	18.1 GB	High	A84
Q6_KBest for your GPU	6	20.7 GB	High	A84
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

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 V100 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	39.5 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	39.7 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	76.6 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

See all results for NVIDIA V100 32GB See all hardware for Gemma 4 26B A4B

URL: https://willitrunai.com/can-run/gemma-4-26b-a4b-on-v100-32gb

⇱ Gemma 4 26B A4B on NVIDIA V100 32GB? YES