Can Qwen 2.5 Coder 32B run on RX 7900 XT 20GB?

YES — With Q3_K_S

B66Good

○Estimated from fit model

Qwen 2.5 Coder 32B needs ~22.5 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q3_K_S quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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.

Qwen 2.5 Coder 32B at Q4_K_M needs 26.3 GB — too much for RX 7900 XT 20GB (20.0 GB). Runs at Q3_K_S (22.5 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.3 GB, exceeds 20.0 GB available

26.3 GB required20.0 GB available

132% VRAM needed

6.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.2 tok/s

TTFT

17334 ms

Safe context

Memory

26.3 GB / 20.0 GB

Offload

20%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 Coder 32B 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: 11.2 tok/s decode · 17.3s TTFT (warm) · 28 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	12.2 tok/s	8682 ms	4K
Coding	F	Too heavy	10.3 tok/s	18721 ms	4K
Agentic Coding	F	Too heavy	7.7 tok/s	36437 ms	4K
Reasoning	F	Too heavy	10.3 tok/s	22125 ms	4K
RAG	F	Too heavy	7.7 tok/s	45546 ms	4K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	12.5 GB	Low	A78
Q3_K_S	3	15.7 GB	Low	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 32B on your machine.

Run

ollama run qwen2.5-coder

Upgrade options

Hardware that runs Qwen 2.5 Coder 32B well

RX 7900 XTX 24GBBest value

24 GB VRAM (+4)960 GB/s (+160)

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 104%.

~$999 MSRP

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+12)

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 (+12)

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
NVIDIA A100 40GBBiggest leap

40 GB VRAM (+20)1555 GB/s (+755)

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

See all results for RX 7900 XT 20GB See all hardware for Qwen 2.5 Coder 32B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-32b-on-rx-7900-xt-20gb

⇱

Can Qwen 2.5 Coder 32B run on RX 7900 XT 20GB?

YES — With Q3_K_S

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Qwen 2.5 Coder 32B well

Frequently asked questions