Can Gemma 3 27B run on RTX A6000 48GB?

YES — Runs Great

S86Excellent

○Estimated from fit model

Gemma 3 27B needs ~33.7 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 33.7 GB, 37.2 tok/s, Runs well

33.7 GB required48.0 GB available

70% VRAM used

Fit status

Runs well

Decode

37.2 tok/s

TTFT

5203 ms

Safe context

36K

Memory

33.7 GB / 48.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemma 3 27B on RTX A6000 48GB
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: 37.2 tok/s decode · 5.2s TTFT (warm) · 93 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	37.2 tok/s	2838 ms	36K
Coding	S	Runs well	37.2 tok/s	5203 ms	36K
Agentic Coding	A	Tight fit	37.2 tok/s	7568 ms	36K
Reasoning	S	Runs well	37.2 tok/s	6149 ms	36K
RAG	A	Tight fit	37.2 tok/s	9460 ms	36K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A76
Q3_K_S	3	13.2 GB	Low	A76
NVFP4	4	15.1 GB	Medium	A77
Q4_K_M	4	16.5 GB	Medium	A77
Q5_K_M	5	19.4 GB	High	A78
Q6_K	6	22.1 GB	High	A79
Q8_0Best for your GPU	8	28.9 GB	Very High	A81
F16	16	55.4 GB	Maximum	F0

Get started

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

Run

ollama run gemma3

Your hardware

More models your RTX A6000 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	88.3 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	74.2 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	91.3 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	80.7 tok/s
👁 Alibaba Qwen 3 32B	32B	S	32.5 tok/s

Frequently asked questions

See all results for RTX A6000 48GB See all hardware for Gemma 3 27B

URL: https://willitrunai.com/can-run/gemma-3-27b-on-a6000-48gb