VOOZH about

URL: https://willitrunai.com/can-run/qwen-3-coder-30b-a3b-on-rx-7900-xt-20gb


Can Qwen3-Coder 30B A3B Instruct run on RX 7900 XT 20GB?

BARELY — Tight on Memory

A84Great
Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~23.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

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

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) — 23.0 GB, 40.7 tok/s, Very compromised (needs ~2.4 GB host RAM)
23.0 GB required20.0 GB available
115% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.4 GB host RAM)

Decode

40.7 tok/s

TTFT

4760 ms

Safe context

4K

Memory

23.0 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom2.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct 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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatAVery compromised40.0 tok/s2637 ms4K
CodingAVery compromised37.4 tok/s5176 ms4K
Agentic CodingFToo heavy32.8 tok/s8576 ms4K
ReasoningAVery compromised37.4 tok/s6117 ms4K
RAGFToo heavy32.8 tok/s10719 ms4K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowS93
Q3_K_SBest for your GPU
3
14.9 GB
LowS93

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Frequently asked questions

See all results for RX 7900 XT 20GBSee all hardware for Qwen3-Coder 30B A3B Instruct
NVFP4
4
17.1 GB
Medium
F0
Q4_K_M
4
18.6 GB
MediumF0
Q5_K_M
5
22.0 GB
HighF0
Q6_K
6
25.0 GB
HighF0
Q8_0
8
32.6 GB
Very HighF0
F16
16
62.5 GB
MaximumF0

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.