Will It Run AI

Can Gemma 4 31B run on RX 7900 XT 20GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Gemma 4 31B needs ~36.3 GB but RX 7900 XT 20GB only has 20.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: 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) — 36.3 GB, exceeds 20.0 GB available
36.3 GB required20.0 GB available
182% VRAM needed

16.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.4 tok/s

TTFT

44321 ms

Safe context

4K

Memory

36.3 GB / 20.0 GB

Offload

40%

Memory breakdown

Weights18.7 GB
KV Cache14.6 GB
Runtime0.9 GB
Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGemma 4 31B on RX 7900 XT 20GB
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: 4.4 tok/s decode · 44.3s 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 36.3 GB, but this setup only exposes 20.0 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 heavy7.0 tok/s15038 ms4K
CodingFToo heavy4.4 tok/s44321 ms4K
Agentic CodingFToo heavy3.1 tok/s92043 ms4K
ReasoningFToo heavy4.4 tok/s52379 ms4K
RAGFToo heavy3.1 tok/s115054 ms4K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.0 GB
LowS87
Q3_K_SBest for your GPU
3
15.0 GB
LowS87
NVFP4
4
17.2 GB
MediumF0
Q4_K_M
4
18.7 GB
MediumF0
Q5_K_M
5
22.1 GB
HighF0
Q6_K
6
25.2 GB
HighF0
Q8_0
8
32.8 GB
Very HighF0
F16
16
62.9 GB
MaximumF0

アップグレードオプション

Gemma 4 31Bを快適に動かすハードウェア

Radeon AI PRO R9700 32GBコスパ最良
32 GB VRAM (+12)
A
Makes the model fit on the accelerator instead of staying completely out of reach.8.9 tok/sデコード

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

Raises estimated decode speed by about 102%.

〜$1,899 MSRP

Radeon Pro W6800 32GBAMDアップグレード
32 GB VRAM (+12)
A
Makes the model fit on the accelerator instead of staying completely out of reach.6.6 tok/sデコード

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

Raises estimated decode speed by about 50%.

〜$2,249 MSRP

Radeon Pro W7900 48GBコスパ重視
48 GB VRAM (+28)864 GB/s (+64)
S
Makes the model fit on the accelerator instead of staying completely out of reach.20.4 tok/sデコード

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.

〜$3,999 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than RX 7900 XT 20GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 36.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 4 31B achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 44321ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RX 7900 XT 20GB receives a F grade with 4.4 tok/s and 4K context.

What context window can Gemma 4 31B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 4 31B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 31B feels slow on RX 7900 XT 20GB?

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.

See all results for RX 7900 XT 20GBSee all hardware for Gemma 4 31B
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