MacBook Pro M4 Max 36GB经济之选
36 GB Unified (+4)410 GB/s (+210)
B
Raises estimated decode speed by about 101%.55.8 tok/s 解码
Raises estimated decode speed by about 101%.
~$2,499 MSRP
Can Yi Coder 9B run on MacBook Pro M2 Pro 32GB?
YES — Runs Great
B60Good
Estimated from fit model
Yi Coder 9B needs ~11.3 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~26 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) — 11.3 GB, 27.7 tok/s, Runs well
11.3 GB required23.0 GB available
49% VRAM used
Fit status
Runs well
Decode
27.7 tok/s
TTFT
6981 ms
Safe context
131K
Memory
11.3 GB / 23.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsYi Coder 9B 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: 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 | 25.5 tok/s | 4141 ms | 131K |
| Coding | B | Runs well | 25.5 tok/s | 7592 ms | 131K |
| Agentic Coding | B | Runs well | 25.5 tok/s | 11043 ms | 131K |
| Reasoning | B | Runs well | 25.5 tok/s | 8972 ms | 131K |
| RAG | B | Runs well | 25.5 tok/s | 13803 ms | 131K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B57 |
Q3_K_S | 3 | 4.4 GB | Low | B58 |
NVFP4 | 4 | 5.0 GB | Medium | B58 |
Q4_K_M | 4 | 5.5 GB | Medium | B58 |
Q5_K_M | 5 | 6.5 GB | High | B59 |
Q6_K | 6 | 7.4 GB | High | B59 |
Q8_0 | 8 | 9.6 GB | Very High | B61 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B62 |
Get started
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server start升级选项
能流畅运行 Yi Coder 9B 的硬件
MacBook Pro M4 Max 48GB最佳性价比
48 GB Unified (+16)546 GB/s (+346)
B
Raises estimated decode speed by about 168%.74.3 tok/s 解码
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBApple 升级
64 GB Unified (+32)800 GB/s (+600)
B
Raises estimated decode speed by about 232%.91.9 tok/s 解码
Raises estimated decode speed by about 232%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Frequently asked questions
Can MacBook Pro M2 Pro 32GB run Yi Coder 9B?
Yes, MacBook Pro M2 Pro 32GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 25.5 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 11.3 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 32GB?
On MacBook Pro M2 Pro 32GB, Yi Coder 9B achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7592ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 32GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on MacBook Pro M2 Pro 32GB receives a B grade with 25.5 tok/s and 131K context.
What context window can Yi Coder 9B use on MacBook Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 32GB, Yi Coder 9B can safely use up to 131K 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 Yi Coder 9B?
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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