Can Gemma 3 12B run on RX 7600 8GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Gemma 3 12B needs ~13.9 GB but RX 7600 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 13.9 GB, exceeds 8.0 GB available

13.9 GB required8.0 GB available

174% VRAM needed

5.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.3 tok/s

TTFT

45497 ms

Safe context

Memory

13.9 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 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 RX 7600 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: 4.3 tok/s decode · 45.5s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.9 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.4 tok/s	16528 ms	4K
Coding	F	Too heavy	4.3 tok/s	45497 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	103384 ms	4K
Reasoning	F	Too heavy	4.3 tok/s	53769 ms	4K
RAG	F	Too heavy	2.7 tok/s	129230 ms	4K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RX 7600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	4.7 GB	Low	A83
Q3_K_S	3	5.9 GB	Low	F0
NVFP4	4	6.7 GB	Medium	F0
Q4_K_M	4	7.3 GB	Medium	F0
Q5_K_M	5	8.6 GB	High	F0
Q6_K	6	9.8 GB	High	F0
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Upgrade options

Hardware that runs Gemma 3 12B well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.18.2 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.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+32)

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.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)432 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.14.6 tok/s decode

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

Raises estimated decode speed by about 240%.

~$449 MSRP

👁 NVIDIA
RTX A4500 20GBBiggest leap

20 GB VRAM (+12)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.54.3 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.

~$2,000 MSRP

Frequently asked questions

See all results for RX 7600 8GB See all hardware for Gemma 3 12B

URL: https://willitrunai.com/can-run/gemma-3-12b-on-rx-7600-8gb