RTX 5070 12GBOpción económica12 GB VRAM (+1)672 GB/s (+56)
B
Este setup está bastante equilibrado para este modelo.83.9 tok/s decodificación
~$549 MSRP
Can Yi Coder 9B run on RTX 2080 Ti 11GB?
YES — Runs Great
B68Good
Estimated from fit model
Yi Coder 9B needs ~9.0 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~73 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, 79.3 tok/s, Runs well
9.0 GB required11.0 GB available
82% VRAM used
Fit status
Runs well
Decode
79.3 tok/s
TTFT
2441 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 RTX 2080 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: 79.3 tok/s decode · 2.4s TTFT (warm) · 198 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 | 72.9 tok/s | 1448 ms | 38K |
| Coding | B | Runs well | 72.9 tok/s | 2655 ms | 38K |
| Agentic Coding | B | Tight fit | 72.9 tok/s | 3861 ms | 38K |
| Reasoning | B | Runs well | 72.9 tok/s | 3137 ms | 38K |
| RAG | B | Tight fit | 72.9 tok/s | 4826 ms | 38K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on RTX 2080 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 startOpciones de mejora
Hardware que ejecuta bien Yi Coder 9B
RTX 5070 12GBOpción económicaRTX 3080 12GBMejor relación calidad-precio12 GB VRAM (+1)912 GB/s (+296)
B
Sube la velocidad estimada de decodificación alrededor de un 36%.108 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 36%.
~$799 MSRP
RTX 3080 Ti 12GBMejora NVIDIA12 GB VRAM (+1)912 GB/s (+296)
B
Sube la velocidad estimada de decodificación alrededor de un 36%.108 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 36%.
~$1,199 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run Yi Coder 9B?
Yes, RTX 2080 Ti 11GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 72.9 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 RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Yi Coder 9B achieves approximately 72.9 tokens per second decode speed with a time-to-first-token of 2655ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on RTX 2080 Ti 11GB receives a B grade with 72.9 tok/s and 38K context.
What context window can Yi Coder 9B use on RTX 2080 Ti 11GB?
On RTX 2080 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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