Can Qwen 2.5 32B run on Tesla P40 24GB?

BARELY — Tight on Memory

B69Good

Estimated from fit model

Qwen 2.5 32B needs ~26.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.7 GB, 6.5 tok/s, Very compromised (needs ~2 GB host RAM)

26.7 GB required24.0 GB available

111% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2 GB host RAM)

Decode

6.5 tok/s

TTFT

29763 ms

Safe context

Memory

26.7 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B on Tesla P40 24GB
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: 6.5 tok/s decode · 29.8s TTFT (warm) · 16 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 10% 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 2.0 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	A	Runs with offload (needs ~0.6 GB host RAM)	7.7 tok/s	13742 ms	5K
Coding	B	Very compromised (needs ~2 GB host RAM)	6.5 tok/s	29763 ms	5K
Agentic Coding	F	Too heavy	4.8 tok/s	58412 ms	5K
Reasoning	B	Very compromised (needs ~2 GB host RAM)	6.5 tok/s	35174 ms	5K
RAG	F	Too heavy	4.8 tok/s	73015 ms	5K

Inference speed

Qwen 2.5 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~66 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	66.4	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	30.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	25.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.8	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	22.9	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	20.9	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	13.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.1	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A84
Q3_K_S	3	15.7 GB	Low	A83
NVFP4Best for your GPU	4	17.9 GB	Medium	A83
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
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

Upgrade options

Hardware that runs Qwen 2.5 32B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1446)

Removes host-memory offload, which is usually the single biggest latency and throughput win.43 tok/s decode

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

Raises estimated decode speed by about 562%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+550)

Removes host-memory offload, which is usually the single biggest latency and throughput win.41.6 tok/s decode

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

Raises estimated decode speed by about 540%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+230)

Removes host-memory offload, which is usually the single biggest latency and throughput win.25.5 tok/s decode

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

Raises estimated decode speed by about 292%.

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Qwen 2.5 32B?

Yes, Tesla P40 24GB can run Qwen 2.5 32B with a B grade (Very compromised (needs ~2 GB host RAM)). Expected decode speed: 6.5 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 26.7 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 Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 32B achieves approximately 6.5 tokens per second decode speed with a time-to-first-token of 29763ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on Tesla P40 24GB receives a B grade with 6.5 tok/s and 5K context.

What context window can Qwen 2.5 32B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 32B can safely use up to 5K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 32B feels slow on Tesla P40 24GB?

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.

See all results for Tesla P40 24GB See all hardware for Qwen 2.5 32B

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Preview:

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 10% 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 2.0 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	A	Runs with offload (needs ~0.6 GB host RAM)	7.7 tok/s	13742 ms	5K
Coding	B	Very compromised (needs ~2 GB host RAM)	6.5 tok/s	29763 ms	5K
Agentic Coding	F	Too heavy	4.8 tok/s	58412 ms	5K
Reasoning	B	Very compromised (needs ~2 GB host RAM)	6.5 tok/s	35174 ms	5K
RAG	F	Too heavy	4.8 tok/s	73015 ms	5K

Qwen 2.5 32B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

66.4

Tight

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

33.2

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

33.2

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

30.8

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

25.7

Fits

RTX 4090 24GB

24 GB

Q4_K_M

24.8

Heavy offload

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

24.3

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

22.9

Heavy offload

RTX 3090 24GB

24 GB

Q4_K_M

21.2

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

20.9

Tight

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

13.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

12.2

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

9.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.1

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A84
Q3_K_S	3	15.7 GB	Low	A83
NVFP4Best for your GPU	4	17.9 GB	Medium	A83
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Frequently asked questions

Can Tesla P40 24GB run Qwen 2.5 32B?

Yes, Tesla P40 24GB can run Qwen 2.5 32B with a B grade (Very compromised (needs ~2 GB host RAM)). Expected decode speed: 6.5 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 26.7 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 Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 32B achieves approximately 6.5 tokens per second decode speed with a time-to-first-token of 29763ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on Tesla P40 24GB receives a B grade with 6.5 tok/s and 5K context.

What context window can Qwen 2.5 32B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 32B can safely use up to 5K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 32B feels slow on Tesla P40 24GB?

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.