560 GB/s (+410)
B
Raises estimated decode speed by about 130%.49.9 tok/s decode
Raises estimated decode speed by about 130%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Can Yi Coder 9B run on MacBook Pro M3 Pro 18GB?
YES — Runs Great
B64Good
○Estimated from fit model
Yi Coder 9B needs ~9.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
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) — 9.8 GB, 21.7 tok/s, Runs well
9.8 GB required13.0 GB available
75% VRAM used
Fit status
Runs well
Decode
21.7 tok/s
TTFT
8926 ms
Safe context
51K
Memory
9.8 GB / 13.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.9 GB
See how fast it feels
See how fast it feelsYi Coder 9B on MacBook Pro M3 Pro 18GB
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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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 | 19.9 tok/s | 5294 ms | 51K |
| Coding | B | Runs well | 19.9 tok/s | 9707 ms | 51K |
| Agentic Coding | B | Tight fit | 19.9 tok/s | 14119 ms | 51K |
| Reasoning | B | Runs well | 19.9 tok/s | 11471 ms | 51K |
| RAG | B | Tight fit | 19.9 tok/s | 17648 ms | 51K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B61 |
Q3_K_S | 3 | 4.4 GB | Low | B62 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startUpgrade options
