MacBook Pro M4 32GB经济之选
32 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.9.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Can Qwen 2.5 Coder 14B run on Mac mini M2 24GB?
YES — Tight Fit
B61Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~15.0 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~8 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.0 GB, 8.2 tok/s, Tight fit
15.0 GB required17.3 GB available
87% VRAM used
Fit status
Tight fit
Decode
8.2 tok/s
TTFT
23552 ms
Safe context
29K
Memory
15.0 GB / 17.3 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on Mac mini M2 24GB
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: 8.2 tok/s decode · 23.6s TTFT (warm) · 21 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 | 8.2 tok/s | 12846 ms | 29K |
| Coding | B | Tight fit | 8.2 tok/s | 23552 ms | 29K |
| Agentic Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 7.7 tok/s | 36723 ms | 29K |
| Reasoning | B | Tight fit | 7.6 tok/s | 30061 ms | 29K |
| RAG | B | Runs with offload (needs ~0.3 GB host RAM) | 7.7 tok/s | 45903 ms | 29K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B63 |
Q3_K_S | 3 | 6.9 GB | Low | B64 |
NVFP4 | 4 | 7.8 GB | Medium | B65 |
Q4_K_M | 4 | 8.5 GB | Medium | B66 |
Q5_K_M | 5 | 10.1 GB | High | B65 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | B65 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
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
Mac mini M4 32GB最佳性价比
32 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.9.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX A4500 20GB最大飞跃640 GB/s (+540)
B
Raises estimated decode speed by about 670%.63.1 tok/s 解码
Raises estimated decode speed by about 670%.
~$2,000 MSRP
Mac Studio M2 Ultra 64GBApple 升级
64 GB Unified (+40)800 GB/s (+700)
B
Raises estimated decode speed by about 616%.58.7 tok/s 解码
Raises estimated decode speed by about 616%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Frequently asked questions
Can Mac mini M2 24GB run Qwen 2.5 Coder 14B?
Yes, Mac mini M2 24GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 8.2 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.0 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 Mac mini M2 24GB?
On Mac mini M2 24GB, Qwen 2.5 Coder 14B achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23552ms using Q4_K_M quantization.
Can Mac mini M2 24GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on Mac mini M2 24GB receives a B grade with 8.2 tok/s and 29K context.
What context window can Qwen 2.5 Coder 14B use on Mac mini M2 24GB?
On Mac mini M2 24GB, Qwen 2.5 Coder 14B can safely use up to 29K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Is unified memory on Mac mini M2 24GB as fast as VRAM for Qwen 2.5 Coder 14B?
Not always. Mac mini M2 24GB 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-m2-24gb" 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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