Can Gemma 3 27B run on RTX A5000 24GB?

YES — With Q3_K_S

A72Great

○Estimated from fit model

Gemma 3 27B needs ~28.1 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q3_K_S quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Gemma 3 27B at Q4_K_M needs 31.3 GB — too much for RTX A5000 24GB (24.0 GB). Runs at Q3_K_S (28.1 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.3 GB, exceeds 24.0 GB available

31.3 GB required24.0 GB available

130% VRAM needed

7.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.7 tok/s

TTFT

13173 ms

Safe context

Memory

31.3 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 27B on RTX A5000 24GB
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: 14.7 tok/s decode · 13.2s TTFT (warm) · 37 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	21.2 tok/s	4976 ms	6K
Coding	F	Too heavy	14.0 tok/s	13831 ms	6K
Agentic Coding	F	Too heavy	7.3 tok/s	38361 ms	6K
Reasoning	F	Too heavy	14.0 tok/s	16346 ms	6K
RAG	F	Too heavy	7.3 tok/s	47951 ms	6K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A82
Q3_K_S	3	13.2 GB	Low	A83
NVFP4	4

Get started

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

Run

ollama run gemma3

Upgrade options

Hardware that runs Gemma 3 27B well

👁 NVIDIA
RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1024)

Makes the model fit on the accelerator instead of staying completely out of reach.58.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

👁 NVIDIA
RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.36.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

👁 NVIDIA
RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,000 MSRP

Frequently asked questions

See all results for RTX A5000 24GB See all hardware for Gemma 3 27B

URL: https://willitrunai.com/can-run/gemma-3-27b-on-a5000-24gb

⇱

Can Gemma 3 27B run on RTX A5000 24GB?

YES — With Q3_K_S

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Gemma 3 27B well

Frequently asked questions