RTX 5070 12GBOpção econômica12 GB VRAM (+1)672 GB/s (+56)
B
This setup is broadly balanced for this model.77.1 tok/s decodificação
~$549 MSRP
Can Yi Coder 9B Chat run on RTX 2080 Ti 11GB?
YES — Runs Great
B56Good
Estimated from fit model
Yi Coder 9B Chat needs ~8.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~73 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 8.8 GB, 72.9 tok/s, Runs well
8.8 GB required11.0 GB available
80% VRAM used
Fit status
Runs well
Decode
72.9 tok/s
TTFT
2655 ms
Safe context
49K
Memory
8.8 GB / 11.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat 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: 72.9 tok/s decode · 2.7s TTFT (warm) · 182 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 | 49K |
| Coding | B | Runs well | 72.9 tok/s | 2655 ms | 49K |
| Agentic Coding | C | Tight fit | 72.9 tok/s | 3861 ms | 49K |
| Reasoning | B | Runs well | 72.9 tok/s | 3137 ms | 49K |
| RAG | C | Tight fit | 72.9 tok/s | 4826 ms | 49K |
Quantization options
How Yi Coder 9B Chat (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 | C51 |
Q3_K_S | 3 | 4.4 GB | Low | C52 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C52 |
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 Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server startOpções de upgrade
Hardware que roda bem Yi Coder 9B Chat
RTX 5070 12GBOpção econômicaRTX 3080 12GBMelhor custo-benefício12 GB VRAM (+1)912 GB/s (+296)
B
Raises estimated decode speed by about 73%.126 tok/s decodificação
Raises estimated decode speed by about 73%.
~$799 MSRP
RTX 3080 Ti 12GBUpgrade NVIDIA12 GB VRAM (+1)912 GB/s (+296)
B
Raises estimated decode speed by about 69%.122.9 tok/s decodificação
Raises estimated decode speed by about 69%.
~$1,199 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run Yi Coder 9B Chat?
Yes, RTX 2080 Ti 11GB can run Yi Coder 9B Chat with a B grade (Runs well). Expected decode speed: 72.9 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 9B Chat?
The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 9B Chat run at on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Yi Coder 9B Chat 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 Chat for coding?
For coding workloads, Yi Coder 9B Chat on RTX 2080 Ti 11GB receives a B grade with 72.9 tok/s and 49K context.
What context window can Yi Coder 9B Chat use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Yi Coder 9B Chat can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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