RTX 5070 12GB经济之选12 GB VRAM (+1)672 GB/s (+188)
B
Raises estimated decode speed by about 48%.83.9 tok/s 解码
Raises estimated decode speed by about 48%.
~$549 MSRP
Can Yi Coder 9B run on GTX 1080 Ti 11GB?
YES — Runs Great
B67Good
Estimated from fit model
Yi Coder 9B needs ~9.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~57 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 9.0 GB, 56.6 tok/s, Runs well
9.0 GB required11.0 GB available
82% VRAM used
Fit status
Runs well
Decode
56.6 tok/s
TTFT
3423 ms
Safe context
38K
Memory
9.0 GB / 11.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsYi Coder 9B on GTX 1080 Ti 11GB
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: 56.6 tok/s decode · 3.4s TTFT (warm) · 141 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 56.6 tok/s | 1867 ms | 38K |
| Coding | B | Runs well | 56.6 tok/s | 3423 ms | 38K |
| Agentic Coding | B | Tight fit | 56.6 tok/s | 4978 ms | 38K |
| Reasoning | B | Runs well | 56.6 tok/s | 4045 ms | 38K |
| RAG | B | Tight fit | 56.6 tok/s | 6223 ms | 38K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B63 |
Q3_K_S | 3 | 4.4 GB | Low | B64 |
NVFP4 | 4 | 5.0 GB | Medium | B65 |
Q4_K_M | 4 | 5.5 GB | Medium | B65 |
Q5_K_M | 5 | 6.5 GB | High | B64 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B64 |
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 GTX 1080 Ti 11GB run Yi Coder 9B?
Yes, GTX 1080 Ti 11GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 56.6 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 9.0 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 GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Yi Coder 9B achieves approximately 56.6 tokens per second decode speed with a time-to-first-token of 3423ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on GTX 1080 Ti 11GB receives a B grade with 56.6 tok/s and 38K context.
What context window can Yi Coder 9B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Yi Coder 9B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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