Can Qwen3-Coder 30B A3B Instruct run on NVIDIA A100 40GB?

YES — Runs Great

S99Excellent

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~25.3 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~198 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) — 25.3 GB, 197.5 tok/s, Runs well

25.3 GB required40.0 GB available

63% VRAM used

Fit status

Runs well

Decode

197.5 tok/s

TTFT

980 ms

Safe context

177K

Memory

25.3 GB / 40.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on NVIDIA A100 40GB
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: 197.5 tok/s decode · 980ms TTFT (warm) · 494 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	197.5 tok/s	535 ms	177K
Coding	S	Runs well	197.5 tok/s	980 ms	177K
Agentic Coding	S	Runs well	197.5 tok/s	1426 ms	177K
Reasoning	S	Runs well	197.5 tok/s	1158 ms	177K
RAG	S	Runs well	197.5 tok/s	1782 ms	177K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	S88
Q3_K_S	3	14.9 GB	Low	S89
NVFP4	4	17.1 GB	Medium	S90
Q4_K_M	4	18.6 GB	Medium	S91
Q5_K_M	5	22.0 GB	High	S92
Q6_K	6	25.0 GB	High	S92
Q8_0Best for your GPU	8	32.6 GB	Very High	S91
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

Frequently asked questions

Can NVIDIA A100 40GB run Qwen3-Coder 30B A3B Instruct?

Yes, NVIDIA A100 40GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 197.5 tok/s.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 25.3 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 40GB?

On NVIDIA A100 40GB, Qwen3-Coder 30B A3B Instruct achieves approximately 197.5 tokens per second decode speed with a time-to-first-token of 980ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on NVIDIA A100 40GB receives a S grade with 197.5 tok/s and 177K context.

What context window can Qwen3-Coder 30B A3B Instruct use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Qwen3-Coder 30B A3B Instruct can safely use up to 177K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Qwen3-Coder 30B A3B Instruct

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