Can Qwen 2.5 Coder 14B run on NVIDIA V100 32GB?

YES — Runs Great

B66Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 15.9 GB, 76.3 tok/s, Runs well

15.9 GB required32.0 GB available

50% VRAM used

Fit status

Runs well

Decode

76.3 tok/s

TTFT

2539 ms

Safe context

104K

Memory

15.9 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on NVIDIA V100 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: 76.3 tok/s decode · 2.5s TTFT (warm) · 191 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	B	Runs well	70.6 tok/s	1496 ms	104K
Coding	B	Runs well	70.6 tok/s	2742 ms	104K
Agentic Coding	B	Runs well	76.3 tok/s	3693 ms	104K
Reasoning	B	Runs well	76.3 tok/s	3000 ms	104K
RAG	B	Runs well	70.6 tok/s	4985 ms	104K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B58
Q3_K_S	3	6.9 GB	Low	B59
NVFP4	4	7.8 GB	Medium	B59
Q4_K_M	4	8.5 GB	Medium	B59
Q5_K_M	5	10.1 GB	High	B60
Q6_K	6	11.5 GB	High	B61
Q8_0Best for your GPU	8	15.0 GB	Very High	B63
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

Frequently asked questions

Can NVIDIA V100 32GB run Qwen 2.5 Coder 14B?

Yes, NVIDIA V100 32GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 70.6 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.9 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 2.5 Coder 14B achieves approximately 70.6 tokens per second decode speed with a time-to-first-token of 2742ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on NVIDIA V100 32GB receives a B grade with 70.6 tok/s and 104K context.

What context window can Qwen 2.5 Coder 14B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 2.5 Coder 14B can safely use up to 104K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Qwen 2.5 Coder 14B

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