Can Qwen 2.5 Coder 32B run on NVIDIA A100 80GB?

YES — Runs Great

A78Great

Estimated from fit model

Qwen 2.5 Coder 32B needs ~32.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~95 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) — 32.6 GB, 94.8 tok/s, Runs well

32.6 GB required80.0 GB available

41% VRAM used

Fit status

Runs well

Decode

94.8 tok/s

TTFT

2043 ms

Safe context

131K

Memory

32.6 GB / 80.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B 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: 94.8 tok/s decode · 2.0s TTFT (warm) · 237 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	A	Runs well	94.8 tok/s	1114 ms	131K
Coding	A	Runs well	94.8 tok/s	2043 ms	131K
Agentic Coding	A	Runs well	94.8 tok/s	2972 ms	131K
Reasoning	A	Runs well	94.8 tok/s	2414 ms	131K
RAG	A	Runs well	94.8 tok/s	3715 ms	131K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	B68
Q3_K_S	3	15.7 GB	Low	B69
NVFP4	4	17.9 GB	Medium	B69
Q4_K_M	4	19.5 GB	Medium	B70
Q5_K_M	5	23.0 GB	High	A70
Q6_K	6	26.2 GB	High	A71
Q8_0	8	34.2 GB	Very High	A73
F16Best for your GPU	16	65.6 GB	Maximum	A76

Get started

Copy-paste commands to run Qwen 2.5 Coder 32B on your machine.

Run

ollama run qwen2.5-coder

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s
Qwen 3.6 35B A3B	35B	S	217.7 tok/s
Qwen 3.5 35B A3B	35B	S	236.7 tok/s
Mistral Small 4 119B	119B	A	55.3 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 2.5 Coder 32B?

Yes, NVIDIA A100 80GB can run Qwen 2.5 Coder 32B with a A grade (Runs well). Expected decode speed: 94.8 tok/s.

How much VRAM does Qwen 2.5 Coder 32B need?

Qwen 2.5 Coder 32B (32B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 32B?

The recommended quantization for Qwen 2.5 Coder 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 32B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 2.5 Coder 32B achieves approximately 94.8 tokens per second decode speed with a time-to-first-token of 2043ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 2.5 Coder 32B for coding?

For coding workloads, Qwen 2.5 Coder 32B on NVIDIA A100 80GB receives a A grade with 94.8 tok/s and 131K context.

What context window can Qwen 2.5 Coder 32B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 2.5 Coder 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Qwen 2.5 Coder 32B

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