Can Qwen 2.5 32B run on RTX 5000 Ada 32GB?

YES — Tight Fit

A83Great

Estimated from fit model

Qwen 2.5 32B needs ~27.8 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 27.8 GB, 25.5 tok/s, Tight fit

27.8 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

25.5 tok/s

TTFT

7594 ms

Safe context

33K

Memory

27.8 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B on RTX 5000 Ada 32GB
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: 25.5 tok/s decode · 7.6s TTFT (warm) · 64 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	25.5 tok/s	4142 ms	33K
Coding	A	Tight fit	23.6 tok/s	8201 ms	33K
Agentic Coding	A	Runs with offload	25.5 tok/s	11045 ms	33K
Reasoning	A	Tight fit	25.5 tok/s	8974 ms	33K
RAG	A	Runs with offload	25.5 tok/s	13807 ms	33K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A81
Q3_K_S	3	15.7 GB	Low	A83
NVFP4	4	17.9 GB	Medium	A83
Q4_K_M	4	19.5 GB	Medium	A83
Q5_K_MBest for your GPU	5	23.0 GB	High	A82
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen 3.5 35B A3B	35B	S	63.7 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Qwen 2.5 32B?

Yes, RTX 5000 Ada 32GB can run Qwen 2.5 32B with a A grade (Tight fit). Expected decode speed: 23.6 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 27.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

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

What speed will Qwen 2.5 32B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Qwen 2.5 32B achieves approximately 23.6 tokens per second decode speed with a time-to-first-token of 8201ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on RTX 5000 Ada 32GB receives a A grade with 23.6 tok/s and 33K context.

What context window can Qwen 2.5 32B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Qwen 2.5 32B can safely use up to 33K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Qwen 2.5 32B

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