Can Qwen 2.5 32B run on RX 7700 XT 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 2.5 32B needs ~25.5 GB but RX 7700 XT 12GB only has 12.0 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) — 25.5 GB, exceeds 12.0 GB available

25.5 GB required12.0 GB available

213% VRAM needed

13.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.2 tok/s

TTFT

88165 ms

Safe context

Memory

25.5 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 32B on RX 7700 XT 12GB
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: 2.2 tok/s decode · 88.2s 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 25.5 GB, but this setup only exposes 12.0 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	2.6 tok/s	40671 ms	4K
Coding	F	Too heavy	2.2 tok/s	88165 ms	4K
Agentic Coding	F	Too heavy	2.2 tok/s	130907 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	112531 ms	4K
RAG	F	Too heavy	2.2 tok/s	163634 ms	4K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen 2.5 32B well

RX 7900 XTX 24GBBest value

24 GB VRAM (+12)960 GB/s (+528)

Makes the model fit on the accelerator instead of staying completely out of reach.22.9 tok/s decode

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

Raises estimated decode speed by about 941%.

~$999 MSRP

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+20)640 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.20.9 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.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade

32 GB VRAM (+20)512 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.15.9 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.

~$2,249 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+28)1555 GB/s (+1123)

Makes the model fit on the accelerator instead of staying completely out of reach.72.3 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.

~$10,000 MSRP

Frequently asked questions

Can RX 7700 XT 12GB run Qwen 2.5 32B?

No, Qwen 2.5 32B requires more memory than RX 7700 XT 12GB provides.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 25.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization for Qwen 2.5 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 32B run at on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Qwen 2.5 32B achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 88165ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on RX 7700 XT 12GB receives a F grade with 2.2 tok/s and 4K context.

What context window can Qwen 2.5 32B use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Qwen 2.5 32B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 32B feels slow on RX 7700 XT 12GB?

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 7700 XT 12GB See all hardware for Qwen 2.5 32B

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