NVIDIA A100 40GBGünstige Wahl1555 GB/s (+1009)
B
Raises estimated decode speed by about 331%.165.2 tok/s Dekodierung
Raises estimated decode speed by about 331%.
ca. $10,000 MSRP
Can Qwen 2.5 Coder 14B run on MacBook Pro M4 Max 48GB?
YES — Runs Great
B64Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~17.6 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~38 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) — 17.6 GB, 38.3 tok/s, Runs well
17.6 GB required34.6 GB available
51% VRAM used
Fit status
Runs well
Decode
38.3 tok/s
TTFT
5057 ms
Safe context
109K
Memory
17.6 GB / 34.6 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M4 Max 48GB
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: 38.3 tok/s decode · 5.1s TTFT (warm) · 96 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 | 38.3 tok/s | 2759 ms | 109K |
| Coding | B | Runs well | 38.3 tok/s | 5057 ms | 109K |
| Agentic Coding | B | Runs well | 38.3 tok/s | 7356 ms | 109K |
| Reasoning | B | Runs well | 38.3 tok/s | 5977 ms | 109K |
| RAG | B | Runs well | 38.3 tok/s | 9195 ms | 109K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B58 |
Q3_K_S | 3 | 6.9 GB | Low | B58 |
NVFP4 | 4 | 7.8 GB | Medium | B59 |
Q4_K_M | 4 | 8.5 GB | Medium | B59 |
Q5_K_M | 5 | 10.1 GB | High | B59 |
Q6_K | 6 | 11.5 GB | High | B60 |
Q8_0 | 8 | 15.0 GB | Very High | B62 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | B63 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.
Run
ollama run qwen2.5-coder:14bUpgrade-Optionen
Hardware, die Qwen 2.5 Coder 14B gut ausführt
Frequently asked questions
Can MacBook Pro M4 Max 48GB run Qwen 2.5 Coder 14B?
Yes, MacBook Pro M4 Max 48GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 38.3 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 17.6 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 MacBook Pro M4 Max 48GB?
On MacBook Pro M4 Max 48GB, Qwen 2.5 Coder 14B achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5057ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 48GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M4 Max 48GB receives a B grade with 38.3 tok/s and 109K context.
What context window can Qwen 2.5 Coder 14B use on MacBook Pro M4 Max 48GB?
On MacBook Pro M4 Max 48GB, Qwen 2.5 Coder 14B can safely use up to 109K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Max 48GB as fast as VRAM for Qwen 2.5 Coder 14B?
Not always. MacBook Pro M4 Max 48GB 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-m4-max-48gb" 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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