MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)546 GB/s (+34)
B
Este setup está bastante equilibrado para este modelo.38.3 tok/s decodificación
~$2,499 MSRP
Can Qwen 2.5 Coder 14B run on Radeon Pro W6800 32GB?
YES — Runs Great
B64Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~15.6 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~36 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 15.6 GB, 36.3 tok/s, Runs well
15.6 GB required32.0 GB available
49% VRAM used
Fit status
Runs well
Decode
36.3 tok/s
TTFT
5339 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 W6800 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: 36.3 tok/s decode · 5.3s TTFT (warm) · 91 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 | 36.3 tok/s | 2912 ms | 106K |
| Coding | B | Runs well | 36.3 tok/s | 5339 ms | 106K |
| Agentic Coding | B | Runs well | 36.3 tok/s | 7766 ms | 106K |
| Reasoning | B | Runs well | 36.3 tok/s | 6310 ms | 106K |
| RAG | B | Runs well | 36.3 tok/s | 9708 ms | 106K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Radeon Pro W6800 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:14bOpciones de mejora
Hardware que ejecuta bien Qwen 2.5 Coder 14B
Mac Studio M2 Ultra 64GBMejor relación calidad-precio
64 GB Unified (+32)800 GB/s (+288)
B
Sube la velocidad estimada de decodificación alrededor de un 62%.58.7 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 62%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can Radeon Pro W6800 32GB run Qwen 2.5 Coder 14B?
Yes, Radeon Pro W6800 32GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 36.3 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.6 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 Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Qwen 2.5 Coder 14B achieves approximately 36.3 tokens per second decode speed with a time-to-first-token of 5339ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on Radeon Pro W6800 32GB receives a B grade with 36.3 tok/s and 106K context.
What context window can Qwen 2.5 Coder 14B use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Qwen 2.5 Coder 14B can safely use up to 106K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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