Can Gemma 3 27B run on NVIDIA B200 180GB?

YES — Runs Great

A80Great

○Estimated from fit model

Gemma 3 27B needs ~46.9 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~378 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) — 46.9 GB, 378.0 tok/s, Runs well

46.9 GB required180.0 GB available

26% VRAM used

Fit status

Runs well

Decode

378.0 tok/s

TTFT

512 ms

Safe context

131K

Memory

46.9 GB / 180.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsGemma 3 27B 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: 378.0 tok/s decode · 512ms TTFT (warm) · 945 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	378.0 tok/s	350 ms	131K
Coding	A	Runs well	378.0 tok/s	512 ms	131K
Agentic Coding	A	Runs well	378.0 tok/s	745 ms	131K
Reasoning	A	Runs well	378.0 tok/s	605 ms	131K
RAG	A	Runs well	378.0 tok/s	931 ms	131K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B70
Q3_K_S	3	13.2 GB	Low	B70
NVFP4	4	15.1 GB	Medium	B70
Q4_K_M	4	16.5 GB	Medium	B70
Q5_K_M	5	19.4 GB	High	A70
Q6_K	6	22.1 GB	High	A70
Q8_0	8	28.9 GB	Very High	A71
F16Best for your GPU	16	55.4 GB	Maximum	A74

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Your hardware

More models your NVIDIA B200 180GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	97.4 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	1016.1 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	270.2 tok/s
👁 DeepSeek DeepSeek V4 Flash	284B	S	144.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	854 tok/s

Frequently asked questions

See all results for NVIDIA B200 180GB See all hardware for Gemma 3 27B

URL: https://willitrunai.com/can-run/gemma-3-27b-on-b200-180gb