MacBook Pro M3 Pro 18GBOpción económica
18 GB Unified (+2)
B
Este setup está bastante equilibrado para este modelo.21.7 tok/s decodificación
~$1,999 MSRP
Can Yi Coder 9B run on MacBook Pro M2 Pro 16GB?
YES — Tight Fit
B62Good
Estimated from fit model
Yi Coder 9B needs ~9.6 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~28 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) — 9.6 GB, 27.7 tok/s, Tight fit
9.6 GB required11.5 GB available
83% VRAM used
Fit status
Tight fit
Decode
27.7 tok/s
TTFT
6981 ms
Safe context
37K
Memory
9.6 GB / 11.5 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsYi Coder 9B on MacBook Pro M2 Pro 16GB
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: 27.7 tok/s decode · 7.0s TTFT (warm) · 69 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 | 27.7 tok/s | 3808 ms | 37K |
| Coding | B | Tight fit | 27.7 tok/s | 6981 ms | 37K |
| Agentic Coding | B | Runs with offload | 27.7 tok/s | 10154 ms | 37K |
| Reasoning | B | Tight fit | 27.7 tok/s | 8250 ms | 37K |
| RAG | B | Runs with offload | 27.7 tok/s | 12693 ms | 37K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B63 |
Q3_K_S | 3 | 4.4 GB | Low | B64 |
NVFP4 | 4 | 5.0 GB | Medium | B65 |
Q4_K_M | 4 | 5.5 GB | Medium | B65 |
Q5_K_M | 5 | 6.5 GB | High | B64 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B64 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startOpciones de mejora
Hardware que ejecuta bien Yi Coder 9B
MacBook Pro M4 Pro 24GBMejor relación calidad-precio
24 GB Unified (+8)273 GB/s (+73)
B
Sube la velocidad estimada de decodificación alrededor de un 38%.38.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 38%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
MacBook Pro M2 Max 32GBMejora Apple
32 GB Unified (+16)400 GB/s (+200)
B
Sube la velocidad estimada de decodificación alrededor de un 66%.46 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 66%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
Frequently asked questions
Can MacBook Pro M2 Pro 16GB run Yi Coder 9B?
Yes, MacBook Pro M2 Pro 16GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 27.7 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 9B?
The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 9B run at on MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Yi Coder 9B achieves approximately 27.7 tokens per second decode speed with a time-to-first-token of 6981ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 16GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on MacBook Pro M2 Pro 16GB receives a B grade with 27.7 tok/s and 37K context.
What context window can Yi Coder 9B use on MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Yi Coder 9B can safely use up to 37K 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 16GB as fast as VRAM for Yi Coder 9B?
Not always. MacBook Pro M2 Pro 16GB 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-coder-9b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: