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	23.9 tok/s	14726 ms	65K

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

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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