12 GB VRAM (+2)
B
This setup is broadly balanced for this model.47.1 tok/s decode
~$329 MSRP
Can Yi Coder 9B run on RTX 3080 10GB?
YES — Tight Fit
B66Good
○Estimated from fit model
Yi Coder 9B needs ~9.2 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~105 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 9.2 GB, 114.4 tok/s, Tight fit
9.2 GB required10.0 GB available
92% VRAM used
Fit status
Tight fit
Decode
114.4 tok/s
TTFT
1692 ms
Safe context
25K
Memory
9.2 GB / 10.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsYi Coder 9B on RTX 3080 10GB
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: 114.4 tok/s decode · 1.7s TTFT (warm) · 286 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 | Tight fit | 105.2 tok/s | 1004 ms | 25K |
| Coding | B | Tight fit | 105.2 tok/s | 1840 ms | 25K |
| Agentic Coding | B | Runs with offload | 69.5 tok/s | 4050 ms | 25K |
| Reasoning | B | Tight fit | 105.2 tok/s | 2175 ms | 25K |
| RAG | B | Runs with offload | 69.5 tok/s | 5063 ms | 25K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B64 |
Q3_K_S | 3 | 4.4 GB | Low | B65 |
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