RTX 3060 12GB经济之选12 GB VRAM (+2)
B
This setup is broadly balanced for this model.47.1 tok/s 解码
~$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 ~114 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
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 | 114.4 tok/s | 923 ms | 25K |
| Coding | B | Tight fit | 114.4 tok/s | 1692 ms | 25K |
| Agentic Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 75.6 tok/s | 3724 ms | 25K |
| Reasoning | B | Tight fit | 114.4 tok/s | 2000 ms | 25K |
| RAG | B | Runs with offload (needs ~0.3 GB host RAM) | 75.6 tok/s | 4655 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 | 5.0 GB | Medium | B65 |
Q4_K_M | 4 | 5.5 GB | Medium | B65 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | B64 |
Q6_K | 6 | 7.4 GB | High | F0 |
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 start升级选项
能流畅运行 Yi Coder 9B 的硬件
Frequently asked questions
Can RTX 3080 10GB run Yi Coder 9B?
Yes, RTX 3080 10GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 114.4 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 9.2 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 RTX 3080 10GB?
On RTX 3080 10GB, Yi Coder 9B achieves approximately 114.4 tokens per second decode speed with a time-to-first-token of 1692ms using Q4_K_M quantization.
Can RTX 3080 10GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on RTX 3080 10GB receives a B grade with 114.4 tok/s and 25K context.
What context window can Yi Coder 9B use on RTX 3080 10GB?
On RTX 3080 10GB, Yi Coder 9B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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