Can Qwen3-Coder 30B A3B Instruct run on NVIDIA A100 80GB?

YES — Runs Great

S93Excellent

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~29.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~259 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 29.3 GB, 259.0 tok/s, Runs well

29.3 GB required80.0 GB available

37% VRAM used

Fit status

Runs well

Decode

259.0 tok/s

TTFT

748 ms

Safe context

256K

Memory

29.3 GB / 80.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on NVIDIA A100 80GB
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: 259.0 tok/s decode · 748ms TTFT (warm) · 648 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	259.0 tok/s	408 ms	256K
Coding	S	Runs well	259.0 tok/s	748 ms	256K
Agentic Coding	S	Runs well	259.0 tok/s	1087 ms	256K
Reasoning	S	Runs well	259.0 tok/s	883 ms	256K
RAG	S	Runs well	259.0 tok/s	1359 ms	256K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	A84
Q3_K_S	3	14.9 GB	Low	A84
NVFP4	4	17.1 GB	Medium	A84
Q4_K_M	4	18.6 GB	Medium	A85
Q5_K_M	5	22.0 GB	High	S85
Q6_K	6	25.0 GB	High	S86
Q8_0	8	32.6 GB	Very High	S88
F16Best for your GPU	16	62.5 GB	Maximum	S91

Get started

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

Run

ollama run qwen3-coder

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	17.6 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Qwen3-Coder 30B A3B Instruct?

Yes, NVIDIA A100 80GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 259.0 tok/s.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 29.3 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen3-Coder 30B A3B Instruct achieves approximately 259.0 tokens per second decode speed with a time-to-first-token of 748ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on NVIDIA A100 80GB receives a S grade with 259.0 tok/s and 256K context.

What context window can Qwen3-Coder 30B A3B Instruct use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen3-Coder 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Qwen3-Coder 30B A3B Instruct

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3-coder-30b-a3b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

259.0 tok/s

408 ms

256K

Coding

Runs well

259.0 tok/s

748 ms

256K

Agentic Coding

Runs well

259.0 tok/s

1087 ms

256K

Reasoning

Runs well

259.0 tok/s

883 ms

256K

RAG

Runs well

259.0 tok/s

1359 ms

256K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	A84
Q3_K_S	3	14.9 GB	Low	A84
NVFP4	4	17.1 GB	Medium	A84
Q4_K_M	4	18.6 GB	Medium	A85
Q5_K_M	5	22.0 GB	High	S85
Q6_K	6	25.0 GB	High	S86
Q8_0	8	32.6 GB	Very High	S88
F16Best for your GPU	16	62.5 GB	Maximum	S91

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

17.6 tok/s