Intel Arc Pro B60 24GB最佳性价比456 GB/s (+256)
B
Raises estimated decode speed by about 76%.31.1 tok/s 解码
Raises estimated decode speed by about 76%.
~$599 MSRP
Can Qwen 2.5 Coder 14B run on MacBook Pro M2 Pro 32GB?
YES — Runs Great
B66Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~15.8 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 15.8 GB, 17.7 tok/s, Runs well
15.8 GB required23.0 GB available
69% VRAM used
Fit status
Runs well
Decode
17.7 tok/s
TTFT
10935 ms
Safe context
55K
Memory
15.8 GB / 23.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M2 Pro 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: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 17.7 tok/s | 5964 ms | 55K |
| Coding | B | Runs well | 17.7 tok/s | 10935 ms | 55K |
| Agentic Coding | B | Runs well | 17.7 tok/s | 15905 ms | 55K |
| Reasoning | B | Runs well | 17.7 tok/s | 12923 ms | 55K |
| RAG | B | Runs well | 17.7 tok/s | 19881 ms | 55K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B60 |
Q3_K_S | 3 | 6.9 GB | Low | B61 |
NVFP4 | 4 | 7.8 GB | Medium | B62 |
Q4_K_M | 4 | 8.5 GB | Medium | B62 |
Q5_K_M | 5 | 10.1 GB | High | B63 |
Q6_K | 6 | 11.5 GB | High | B64 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | B64 |
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升级选项
能流畅运行 Qwen 2.5 Coder 14B 的硬件
Frequently asked questions
Can MacBook Pro M2 Pro 32GB run Qwen 2.5 Coder 14B?
Yes, MacBook Pro M2 Pro 32GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 17.7 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.8 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 MacBook Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 32GB, Qwen 2.5 Coder 14B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10935ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 32GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M2 Pro 32GB receives a B grade with 17.7 tok/s and 55K context.
What context window can Qwen 2.5 Coder 14B use on MacBook Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 32GB, Qwen 2.5 Coder 14B can safely use up to 55K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Qwen 2.5 Coder 14B?
Not always. MacBook Pro M2 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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