Can Qwen3-Coder 30B A3B Instruct run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~22.3 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: 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) — 22.3 GB, exceeds 10.0 GB available

22.3 GB required10.0 GB available

223% VRAM needed

12.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.1 tok/s

TTFT

14778 ms

Safe context

Memory

22.3 GB / 10.0 GB

Offload

60%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder 30B A3B Instruct on RTX 3080 10GB
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: 13.1 tok/s decode · 14.8s TTFT (warm) · 33 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.3 GB, but this setup only exposes 10.0 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	13.1 tok/s	8060 ms	4K
Coding	F	Too heavy	13.1 tok/s	14778 ms	4K
Agentic Coding	F	Too heavy	13.1 tok/s	21495 ms	4K
Reasoning	F	Too heavy	13.1 tok/s	17464 ms	4K
RAG	F	Too heavy	13.1 tok/s	26868 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 RTX 3080 10GB (10.0 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

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

RTX 4000 Ada 20GBコスパ最良

20 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 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 77%.

〜$1,250 MSRP

RTX 3090 24GBコスパ重視

24 GB VRAM (+14)936 GB/s (+176)

Makes the model fit on the accelerator instead of staying completely out of reach.99.1 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.

〜$1,499 MSRP

RTX 4090 24GBNVIDIAアップグレード

24 GB VRAM (+14)1008 GB/s (+248)

Makes the model fit on the accelerator instead of staying completely out of reach.115.8 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.

〜$1,599 MSRP

Frequently asked questions

Can RTX 3080 10GB run Qwen3-Coder 30B A3B Instruct?

No, Qwen3-Coder 30B A3B Instruct requires more memory than RTX 3080 10GB provides.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 22.3 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on RTX 3080 10GB?

On RTX 3080 10GB, Qwen3-Coder 30B A3B Instruct achieves approximately 13.1 tokens per second decode speed with a time-to-first-token of 14778ms using Q4_K_M quantization.

Can RTX 3080 10GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on RTX 3080 10GB receives a F grade with 13.1 tok/s and 4K context.

What context window can Qwen3-Coder 30B A3B Instruct use on RTX 3080 10GB?

On RTX 3080 10GB, Qwen3-Coder 30B A3B Instruct 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 Qwen3-Coder 30B A3B Instruct feels slow on RTX 3080 10GB?

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 RTX 3080 10GB See all hardware for Qwen3-Coder 30B A3B Instruct

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