Can Gemma 4 31B run on RX 7900 XTX 24GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Gemma 4 31B needs ~36.7 GB but RX 7900 XTX 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: 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) — 36.7 GB, exceeds 24.0 GB available

36.7 GB required24.0 GB available

153% VRAM needed

12.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.8 tok/s

TTFT

24828 ms

Safe context

Memory

36.7 GB / 24.0 GB

Offload

30%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 31B on RX 7900 XTX 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: 7.8 tok/s decode · 24.8s TTFT (warm) · 20 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 36.7 GB, but this setup only exposes 24.0 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	12.5 tok/s	8473 ms	4K
Coding	F	Too heavy	7.8 tok/s	24828 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	73262 ms	4K
Reasoning	F	Too heavy	7.8 tok/s	29342 ms	4K
RAG	F	Too heavy	3.8 tok/s	91578 ms	4K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	S87
Q3_K_S	3	15.0 GB	Low	S87
NVFP4	4	17.2 GB	Medium	S86
Q4_K_MBest for your GPU	4	18.7 GB	Medium	S86
Q5_K_M	5	22.1 GB	High	F0
Q6_K	6	25.2 GB	High	F0
Q8_0	8	32.8 GB	Very High	F0
F16	16	62.9 GB	Maximum	F0