Can Qwen 2.5 32B run on Tesla P100 16GB?

Q: What should I upgrade first if Qwen 2.5 32B feels slow on Tesla P100 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

B66Good

Estimated from fit model

Qwen 2.5 32B needs ~19.2 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q2_K quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Qwen 2.5 32B at Q4_K_M needs 26.2 GB — too much for Tesla P100 16GB (16.0 GB). Runs at Q2_K (19.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.2 GB, exceeds 16.0 GB available

26.2 GB required16.0 GB available

164% VRAM needed

10.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.9 tok/s

TTFT

32879 ms

Safe context

Memory

26.2 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 32B on Tesla P100 16GB
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: 5.9 tok/s decode · 32.9s TTFT (warm) · 15 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.0 tok/s	15131 ms	4K
Coding	F	Too heavy	5.5 tok/s	35510 ms	4K
Agentic Coding	F	Too heavy	4.3 tok/s	64873 ms	4K
Reasoning	F	Too heavy	5.9 tok/s	38857 ms	4K
RAG	F	Too heavy	4.3 tok/s	81092 ms	4K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4

Get started

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

Run

ollama run qwen2.5

Upgrade options

Hardware that runs Qwen 2.5 32B well

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+204)

Makes the model fit on the accelerator instead of staying completely out of reach.21.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 259%.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.18.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 217%.

~$1,599 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+16)1792 GB/s (+1060)

Makes the model fit on the accelerator instead of staying completely out of reach.66.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can Tesla P100 16GB run Qwen 2.5 32B?

Yes, Tesla P100 16GB can run Qwen 2.5 32B at Q2_K quantization (Very compromised (needs ~2.1 GB host RAM)). The recommended Q4_K_M requires 26.2 GB which exceeds available memory, but at Q2_K it needs only 19.2 GB. Expected decode speed: 15.6 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 26.2 GB at Q4_K_M quantization. On Tesla P100 16GB, it fits at Q2_K using 19.2 GB.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization is Q4_K_M, but on Tesla P100 16GB the best fitting quantization is Q2_K, which uses 19.2 GB.

What speed will Qwen 2.5 32B run at on Tesla P100 16GB?

On Tesla P100 16GB, Qwen 2.5 32B achieves approximately 15.6 tokens per second decode speed with a time-to-first-token of 12449ms using Q2_K quantization.

Can Tesla P100 16GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on Tesla P100 16GB receives a F grade with 5.5 tok/s and 4K context.

What context window can Qwen 2.5 32B use on Tesla P100 16GB?

On Tesla P100 16GB, Qwen 2.5 32B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Tesla P100 16GB See all hardware for Qwen 2.5 32B

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