Can Qwen 2.5 Coder 32B run on NVIDIA A40 48GB?

YES — Runs Great

A79Great

Estimated from fit model

Qwen 2.5 Coder 32B needs ~29.4 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 29.4 GB, 30.0 tok/s, Runs well

29.4 GB required48.0 GB available

61% VRAM used

Fit status

Runs well

Decode

30.0 tok/s

TTFT

6446 ms

Safe context

92K

Memory

29.4 GB / 48.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B on NVIDIA A40 48GB
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: 30.0 tok/s decode · 6.4s TTFT (warm) · 75 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	30.0 tok/s	3516 ms	92K
Coding	A	Runs well	30.0 tok/s	6446 ms	92K
Agentic Coding	A	Runs well	30.0 tok/s	9375 ms	92K
Reasoning	A	Runs well	30.0 tok/s	7617 ms	92K
RAG	A	Runs well	30.0 tok/s	11719 ms	92K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A72
Q3_K_S	3	15.7 GB	Low	A73
NVFP4	4	17.9 GB	Medium	A73
Q4_K_M	4	19.5 GB	Medium	A74
Q5_K_M	5	23.0 GB	High	A75
Q6_K	6	26.2 GB	High	A76
Q8_0Best for your GPU	8	34.2 GB	Very High	A76
F16	16	65.6 GB	Maximum	F0

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 A40 48GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	69 tok/s
Qwen 3.5 35B A3B	35B	S	75 tok/s
Qwen 2.5 VL 72B	72B	A	7.6 tok/s
Qwen3-Coder-Next	80B	A	19.7 tok/s
Llama 3.3 70B	70B	A	8.2 tok/s

Frequently asked questions

Can NVIDIA A40 48GB run Qwen 2.5 Coder 32B?

Yes, NVIDIA A40 48GB can run Qwen 2.5 Coder 32B with a A grade (Runs well). Expected decode speed: 30.0 tok/s.

How much VRAM does Qwen 2.5 Coder 32B need?

Qwen 2.5 Coder 32B (32B parameters) requires approximately 29.4 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 A40 48GB?

On NVIDIA A40 48GB, Qwen 2.5 Coder 32B achieves approximately 30.0 tokens per second decode speed with a time-to-first-token of 6446ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run Qwen 2.5 Coder 32B for coding?

For coding workloads, Qwen 2.5 Coder 32B on NVIDIA A40 48GB receives a A grade with 30.0 tok/s and 92K context.

What context window can Qwen 2.5 Coder 32B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, Qwen 2.5 Coder 32B can safely use up to 92K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GB See all hardware for Qwen 2.5 Coder 32B

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