Can Gemma 3 27B run on RTX PRO 4000 Blackwell 24GB?

Q: What should I upgrade first if Gemma 3 27B feels slow on RTX PRO 4000 Blackwell 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.

YES — With Q3_K_S

A72Great

Estimated from fit model

Gemma 3 27B needs ~28.1 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q3_K_S quantization, expect ~23 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 PRO 4000 Blackwell 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

16.0 tok/s

TTFT

12094 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 PRO 4000 Blackwell 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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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)	24.0 tok/s	4407 ms	6K
Coding	F	Too heavy	16.0 tok/s	12094 ms	6K
Agentic Coding	F	Too heavy	8.6 tok/s	32879 ms	6K
Reasoning	F	Too heavy	16.0 tok/s	14293 ms	6K
RAG	F	Too heavy	8.6 tok/s	41099 ms	6K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on RTX PRO 4000 Blackwell 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

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1120)

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)896 GB/s (+224)

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 RTX PRO 4000 Blackwell 24GB run Gemma 3 27B?

Yes, RTX PRO 4000 Blackwell 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: 23.2 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 RTX PRO 4000 Blackwell 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 RTX PRO 4000 Blackwell 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.

What speed will Gemma 3 27B run at on RTX PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 24GB, Gemma 3 27B achieves approximately 23.2 tokens per second decode speed with a time-to-first-token of 8359ms using Q3_K_S quantization.

Can RTX PRO 4000 Blackwell 24GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on RTX PRO 4000 Blackwell 24GB receives a F grade with 16.0 tok/s and 6K context.

What context window can Gemma 3 27B use on RTX PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 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.

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for Gemma 3 27B

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