Can Qwen3-Coder 30B A3B Instruct run on RX 9070 XT 16GB?

YES — With Q3_K_S

A78Great

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~18.9 GB VRAM. RX 9070 XT 16GB has 16.0 GB. With Q3_K_S quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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.

Qwen3-Coder 30B A3B Instruct at Q4_K_M needs 22.6 GB — too much for RX 9070 XT 16GB (16.0 GB). Runs at Q3_K_S (18.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.6 GB, exceeds 16.0 GB available

22.6 GB required16.0 GB available

141% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

23.5 tok/s

TTFT

8246 ms

Safe context

Memory

22.6 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder 30B A3B Instruct on RX 9070 XT 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: 23.5 tok/s decode · 8.2s TTFT (warm) · 59 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 20% 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 2.3 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	25.1 tok/s	4205 ms	4K
Coding	F	Too heavy	23.5 tok/s	8246 ms	4K
Agentic Coding	F	Too heavy	20.7 tok/s	13636 ms	4K
Reasoning	F	Too heavy	23.5 tok/s	9746 ms	4K
RAG	F	Too heavy	20.7 tok/s	17045 ms	4K

Inference speed

Qwen3-Coder 30B A3B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-Coder 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~182 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	181.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	115.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	104.5	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	99.1	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	84.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	70.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	66.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	52.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	52.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	32.7	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	31.8	Fits
RTX 4070 12GB	12 GB	Q4_K_M	11.4	Too big
RTX 3060 12GB	12 GB	Q4_K_M	7.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	F0
Q3_K_S	3	14.9 GB	Low	F0
NVFP4	4	17.1 GB	Medium	F0
Q4_K_M	4	18.6 GB	Medium	F0
Q5_K_M	5	22.0 GB	High	F0
Q6_K	6	25.0 GB	High	F0
Q8_0	8	32.6 GB	Very High	F0
F16	16	62.5 GB	Maximum	F0

Get started

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

Run

ollama run qwen3-coder

アップグレードオプション

Qwen3-Coder 30B A3B Instructを快適に動かすハードウェア

RX 7900 XT 20GBコスパ最良

20 GB VRAM (+4)800 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.40.7 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 73%.

〜$899 MSRP

RX 7900 XTX 24GBコスパ重視

24 GB VRAM (+8)960 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.104.5 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.

〜$999 MSRP

Radeon AI PRO R9700 32GBAMDアップグレード

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.57.1 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.

〜$1,899 MSRP

Frequently asked questions

Can RX 9070 XT 16GB run Qwen3-Coder 30B A3B Instruct?

Yes, RX 9070 XT 16GB can run Qwen3-Coder 30B A3B Instruct at Q3_K_S quantization (Very compromised (needs ~2.3 GB host RAM)). The recommended Q4_K_M requires 22.6 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 39.0 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 22.6 GB at Q4_K_M quantization. On RX 9070 XT 16GB, it fits at Q3_K_S using 18.9 GB.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization is Q4_K_M, but on RX 9070 XT 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.

What speed will Qwen3-Coder 30B A3B Instruct run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Qwen3-Coder 30B A3B Instruct achieves approximately 39.0 tokens per second decode speed with a time-to-first-token of 4965ms using Q3_K_S quantization.

Can RX 9070 XT 16GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on RX 9070 XT 16GB receives a F grade with 23.5 tok/s and 4K context.

What context window can Qwen3-Coder 30B A3B Instruct use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Qwen3-Coder 30B A3B Instruct can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder 30B A3B Instruct feels slow on RX 9070 XT 16GB?

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.

See all results for RX 9070 XT 16GB See all hardware for Qwen3-Coder 30B A3B Instruct

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