20 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.35.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Qwen 2.5 Coder 14B run on RTX 4080 Super 16GB?
YES — Tight Fit
B68Good
○Estimated from fit model
Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~81 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 14.0 GB, 81.1 tok/s, Tight fit
14.0 GB required16.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
81.1 tok/s
TTFT
2387 ms
Safe context
27K
Memory
14.0 GB / 16.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on RTX 4080 Super 16GB
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: 81.1 tok/s decode · 2.4s TTFT (warm) · 203 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 | A | Runs well | 81.1 tok/s | 1302 ms | 27K |
| Coding | B | Tight fit | 81.1 tok/s | 2387 ms | 27K |
| Agentic Coding | B | Runs with offload (needs ~0.5 GB host RAM) | 54.2 tok/s | 5193 ms | 27K |
| Reasoning | B | Tight fit | 81.1 tok/s | 2821 ms | 27K |
| RAG | B | Runs with offload (needs ~0.5 GB host RAM) | 54.2 tok/s | 6492 ms |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B64 |
Q3_K_S | 3 | 6.9 GB | Low | B65 |
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
24 GB VRAM (+8)936 GB/s (+200)
B
Adds memory headroom for longer context windows and future model growth.81.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)1008 GB/s (+272)
B
Raises estimated decode speed by about 25%.101.7 tok/s decode
Raises estimated decode speed by about 25%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP