Can Gemma 4 31B run on RX 9070 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 31B needs ~35.9 GB but RX 9070 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: 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) — 35.9 GB, exceeds 16.0 GB available

35.9 GB required16.0 GB available

224% VRAM needed

19.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.5 tok/s

TTFT

76548 ms

Safe context

Memory

35.9 GB / 16.0 GB

Offload

60%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 31B on RX 9070 16GB
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: 2.5 tok/s decode · 76.5s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 35.9 GB, but this setup only exposes 16.0 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	4.0 tok/s	26691 ms	4K
Coding	F	Too heavy	2.5 tok/s	76548 ms	4K
Agentic Coding	F	Too heavy	2.5 tok/s	111342 ms	4K
Reasoning	F	Too heavy	2.5 tok/s	90466 ms	4K
RAG	F	Too heavy	2.5 tok/s	139178 ms	4K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	F0
Q3_K_S	3	15.0 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Gemma 4 31B well

Radeon AI PRO R9700 32GBBest value

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.8.9 tok/s decode

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

Raises estimated decode speed by about 256%.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.6.6 tok/s decode

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

Raises estimated decode speed by about 164%.

~$2,249 MSRP

Radeon Pro W7900 48GBBudget pick

48 GB VRAM (+32)864 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.20.4 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.

~$3,999 MSRP

Frequently asked questions

Can RX 9070 16GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than RX 9070 16GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 35.9 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 9070 16GB?

On RX 9070 16GB, Gemma 4 31B achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 76548ms using Q4_K_M quantization.

Can RX 9070 16GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RX 9070 16GB receives a F grade with 2.5 tok/s and 4K context.

What context window can Gemma 4 31B use on RX 9070 16GB?

On RX 9070 16GB, 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 9070 16GB?

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 9070 16GB See all hardware for Gemma 4 31B

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