16 GB VRAM (+4)896 GB/s (+608)
B
Raises estimated decode speed by about 155%.113.6 tok/s decode
Raises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Can Yi Coder 9B run on RTX A2000 12GB?
YES — Runs Great
B66Good
○Estimated from fit model
Yi Coder 9B needs ~9.4 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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) — 9.4 GB, 44.5 tok/s, Runs well
9.4 GB required12.0 GB available
78% VRAM used
Fit status
Runs well
Decode
44.5 tok/s
TTFT
4351 ms
Safe context
45K
Memory
9.4 GB / 12.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsYi Coder 9B on RTX A2000 12GB
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: 44.5 tok/s decode · 4.4s TTFT (warm) · 111 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 40.9 tok/s | 2581 ms | 45K |
| Coding | B | Runs well | 40.9 tok/s | 4731 ms | 45K |
| Agentic Coding | B | Tight fit | 40.9 tok/s | 6882 ms | 45K |
| Reasoning | B | Runs well | 40.9 tok/s | 5592 ms | 45K |
| RAG | B | Tight fit | 40.9 tok/s | 8603 ms | 45K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B62 |
Q3_K_S | 3 | 4.4 GB | Low | B63 |
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
Hardware that runs Yi Coder 9B well
16 GB VRAM (+4)672 GB/s (+384)
B
Raises estimated decode speed by about 139%.106.5 tok/s decode
Raises estimated decode speed by about 139%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
16 GB VRAM (+4)736 GB/s (+448)
B
Raises estimated decode speed by about 172%.121 tok/s decode
Raises estimated decode speed by about 172%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP