Can Qwen 2.5 Coder 14B run on Tesla P40 24GB?

YES — Runs Great

B66Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.1 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 15.1 GB, 25.8 tok/s, Runs well

15.1 GB required24.0 GB available

63% VRAM used

Fit status

Runs well

Decode

25.8 tok/s

TTFT

7499 ms

Safe context

65K

Memory

15.1 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	25.8 tok/s	4090 ms	65K
Coding	B	Runs well	25.8 tok/s	7499 ms	65K
Agentic Coding	B	Runs well	25.8 tok/s	10908 ms	65K
Reasoning	B	Runs well	25.8 tok/s	8863 ms	65K
RAG	B	Runs well	25.8 tok/s	13635 ms	65K

Inference speed

Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 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	151.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	82.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	81.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	29.1	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	17.0	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.3	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 Coder 14B (14B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B61
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 488%.151.8 tok/s decode

Raises estimated decode speed by about 488%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

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

Raises estimated decode speed by about 269%.95.2 tok/s decode

Raises estimated decode speed by about 269%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

NVIDIA V100 32GBNVIDIA upgrade

32 GB VRAM (+8)900 GB/s (+554)

Raises estimated decode speed by about 196%.76.3 tok/s decode

Raises estimated decode speed by about 196%.

Adds memory headroom for longer context windows and future model growth.

~$8,999 MSRP

Frequently asked questions

Can Tesla P40 24GB run Qwen 2.5 Coder 14B?

Yes, Tesla P40 24GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 25.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 Coder 14B achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7499ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Tesla P40 24GB receives a B grade with 25.8 tok/s and 65K context.

What context window can Qwen 2.5 Coder 14B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 Coder 14B can safely use up to 65K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Qwen 2.5 Coder 14B

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-tesla-p40-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

25.8 tok/s

4090 ms

65K

Coding

Runs well

25.8 tok/s

7499 ms

65K

Agentic Coding

Runs well

25.8 tok/s

10908 ms

65K

Reasoning

Runs well

25.8 tok/s

8863 ms

65K

RAG

Runs well

25.8 tok/s

13635 ms

65K

Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

151.8

Fits

RTX 4090 24GB

24 GB

Q4_K_M

96.9

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

87.4

Fits

RTX 3090 24GB

24 GB

Q4_K_M

82.9

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

81.1

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

70.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

58.7

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

55.6

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

38.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

38.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

30.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

29.1

Heavy offload

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

27.8

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

23.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

17.0

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

6.3

Too big

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B61
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
F16	16	28.7 GB	Maximum	F0

Frequently asked questions

Can Tesla P40 24GB run Qwen 2.5 Coder 14B?

Yes, Tesla P40 24GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 25.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 Coder 14B achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7499ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Tesla P40 24GB receives a B grade with 25.8 tok/s and 65K context.

What context window can Qwen 2.5 Coder 14B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 2.5 Coder 14B can safely use up to 65K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.