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URL: https://willitrunai.com/can-run/gemma-3-12b-on-rtx-5060-ti-8gb

⇱ Gemma 3 12B on RTX 5060 Ti 8GB? No — Alternatives

Can Gemma 3 12B run on RTX 5060 Ti 8GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Gemma 3 12B needs ~14.2 GB but RTX 5060 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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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) — 14.2 GB, exceeds 8.0 GB available
14.2 GB required8.0 GB available
178% VRAM needed

6.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.4 tok/s

TTFT

20493 ms

Safe context

4K

Memory

14.2 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights7.3 GB
KV Cache4.9 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGemma 3 12B on RTX 5060 Ti 8GB
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: 9.4 tok/s decode · 20.5s TTFT (warm) · 24 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.2 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy13.9 tok/s7608 ms4K
CodingFToo heavy9.4 tok/s20493 ms4K
Agentic CodingFToo heavy6.0 tok/s47119 ms4K
ReasoningFToo heavy9.4 tok/s24219 ms4K
RAGFToo heavy6.0 tok/s58898 ms4K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
4.7 GB
LowA83
Q3_K_S
3
5.9 GB
LowF0
NVFP4
4
6.7 GB
MediumF0
Q4_K_M
4
7.3 GB
MediumF0
Q5_K_M
5
8.6 GB
HighF0
Q6_K
6
9.8 GB
HighF0
Q8_0
8
12.8 GB
Very HighF0
F16
16
24.6 GB
MaximumF0

Upgrade options

Hardware that runs Gemma 3 12B well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.39.8 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.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.30.2 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.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.31.4 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.

~$625 MSRP

Frequently asked questions

See all results for RTX 5060 Ti 8GBSee all hardware for Gemma 3 12B