MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
B
This setup is broadly balanced for this model.38.3 tok/s decode
~$2,499 MSRP
Can Qwen 2.5 Coder 14B run on Radeon Pro W7800 32GB?
YES — Runs Great
B64Good
○Estimated from fit model
Qwen 2.5 Coder 14B needs ~15.6 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~43 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 15.6 GB, 43.0 tok/s, Runs well
15.6 GB required32.0 GB available
49% VRAM used
Fit status
Runs well
Decode
43.0 tok/s
TTFT
4505 ms
Safe context
106K
Memory
15.6 GB / 32.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on Radeon Pro W7800 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: 43.0 tok/s decode · 4.5s TTFT (warm) · 107 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 | 43.0 tok/s | 2457 ms | 106K |
| Coding | B | Runs well | 43.0 tok/s | 4505 ms | 106K |
| Agentic Coding | B | Runs well | 43.0 tok/s | 6552 ms | 106K |
| Reasoning | B | Runs well | 43.0 tok/s | 5324 ms | 106K |
| RAG | B | Runs well | 43.0 tok/s | 8190 ms | 106K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Radeon Pro W7800 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:14bUpgrade options
