Can Gemma 3 27B run on NVIDIA A30 24GB?

YES — With Q3_K_S

A73Great

Estimated from fit model

Gemma 3 27B needs ~28.1 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q3_K_S quantization, expect ~29 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: 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 NVIDIA A30 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

19.9 tok/s

TTFT

9731 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 NVIDIA A30 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: 19.9 tok/s decode · 9.7s TTFT (warm) · 50 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 (needs ~1.1 GB host RAM)	30.2 tok/s	3501 ms	6K
Coding	F	Too heavy	19.9 tok/s	9731 ms	6K
Agentic Coding	F	Too heavy	10.4 tok/s	26988 ms	6K
Reasoning	F	Too heavy	19.9 tok/s	11500 ms	6K
RAG	F	Too heavy	10.4 tok/s	33736 ms	6K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA A30 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	15.1 GB	Medium	A82
Q4_K_MBest for your GPU	4	16.5 GB	Medium	A82
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

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

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+859)

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

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)

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

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

Can NVIDIA A30 24GB run Gemma 3 27B?

Yes, NVIDIA A30 24GB can run Gemma 3 27B at Q3_K_S quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 31.3 GB which exceeds available memory, but at Q3_K_S it needs only 28.1 GB. Expected decode speed: 29.0 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 31.3 GB at Q4_K_M quantization. On NVIDIA A30 24GB, it fits at Q3_K_S using 28.1 GB.

What is the best quantization for Gemma 3 27B?

The recommended quantization is Q4_K_M, but on NVIDIA A30 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.

What speed will Gemma 3 27B run at on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Gemma 3 27B achieves approximately 29.0 tokens per second decode speed with a time-to-first-token of 6678ms using Q3_K_S quantization.

Can NVIDIA A30 24GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA A30 24GB receives a F grade with 19.9 tok/s and 6K context.

What context window can Gemma 3 27B use on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Gemma 3 27B can safely use up to 10K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 27B feels slow on NVIDIA A30 24GB?

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.

See all results for NVIDIA A30 24GB See all hardware for Gemma 3 27B

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Preview:

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.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs with offload (needs ~1.1 GB host RAM)

30.2 tok/s

3501 ms

Coding

Too heavy

19.9 tok/s

9731 ms

Agentic Coding

Too heavy

10.4 tok/s

26988 ms

Reasoning

Too heavy

19.9 tok/s

11500 ms

RAG

Too heavy

10.4 tok/s

33736 ms

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA A30 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	15.1 GB	Medium	A82
Q4_K_MBest for your GPU	4	16.5 GB	Medium	A82
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A30 24GB run Gemma 3 27B?

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 31.3 GB at Q4_K_M quantization. On NVIDIA A30 24GB, it fits at Q3_K_S using 28.1 GB.

What is the best quantization for Gemma 3 27B?

The recommended quantization is Q4_K_M, but on NVIDIA A30 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.

What speed will Gemma 3 27B run at on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Gemma 3 27B achieves approximately 29.0 tokens per second decode speed with a time-to-first-token of 6678ms using Q3_K_S quantization.

Can NVIDIA A30 24GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA A30 24GB receives a F grade with 19.9 tok/s and 6K context.

What context window can Gemma 3 27B use on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Gemma 3 27B can safely use up to 10K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 27B feels slow on NVIDIA A30 24GB?

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.