80 GB VRAM (+16)2039 GB/s (+1439)
B
Raises estimated decode speed by about 231%.196 tok/s decode
Raises estimated decode speed by about 231%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
Can Qwen 2.5 Coder 14B run on NVIDIA A16 64GB?
YES — Runs Great
B61Good
○Estimated from fit model
Qwen 2.5 Coder 14B needs ~19.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 19.1 GB, 59.2 tok/s, Runs well
19.1 GB required64.0 GB available
30% VRAM used
Fit status
Runs well
Decode
59.2 tok/s
TTFT
3271 ms
Safe context
131K
Memory
19.1 GB / 64.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on NVIDIA A16 64GB
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: 59.2 tok/s decode · 3.3s TTFT (warm) · 148 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 | 59.2 tok/s | 1784 ms | 131K |
| Coding | B | Runs well | 54.8 tok/s | 3533 ms | 131K |
| Agentic Coding | B | Runs well | 59.2 tok/s | 4758 ms | 131K |
| Reasoning | B | Runs well | 59.2 tok/s | 3866 ms | 131K |
| RAG | B | Runs well | 59.2 tok/s | 5947 ms | 131K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C55 |
Q3_K_S | 3 | 6.9 GB | Low | B55 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.
Run
ollama run qwen2.5-coder:14bUpgrade options
Hardware that runs Qwen 2.5 Coder 14B well
80 GB VRAM (+16)1935 GB/s (+1335)
B
Raises estimated decode speed by about 222%.190.9 tok/s decode
Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
80 GB VRAM (+16)3000 GB/s (+2400)
B
Raises estimated decode speed by about 231%.196 tok/s decode
Raises estimated decode speed by about 231%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP