RTX 5070 12GBGünstige Wahl12 GB VRAM (+1)672 GB/s (+188)
B
Raises estimated decode speed by about 48%.77.1 tok/s Dekodierung
Raises estimated decode speed by about 48%.
ca. $549 MSRP
Can Yi Coder 9B Chat run on GTX 1080 Ti 11GB?
YES — Runs Great
B55Good
Estimated from fit model
Yi Coder 9B Chat needs ~8.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~52 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, 52.0 tok/s, Runs well
8.8 GB required11.0 GB available
80% VRAM used
Fit status
Runs well
Decode
52.0 tok/s
TTFT
3722 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 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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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 | 52.0 tok/s | 2030 ms | 49K |
| Coding | B | Runs well | 52.0 tok/s | 3722 ms | 49K |
| Agentic Coding | C | Tight fit | 52.0 tok/s | 5414 ms | 49K |
| Reasoning | B | Runs well | 52.0 tok/s | 4399 ms | 49K |
| RAG | C | Tight fit | 52.0 tok/s | 6767 ms | 49K |
Quantization options
How Yi Coder 9B Chat (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 | 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 startUpgrade-Optionen
Hardware, die Yi Coder 9B Chat gut ausführt
RTX 5070 12GBGünstige WahlRTX 4070 Super 12GBBestes Preis-Leistungs-Verhältnis12 GB VRAM (+1)504 GB/s (+20)
B
Raises estimated decode speed by about 36%.70.7 tok/s Dekodierung
Raises estimated decode speed by about 36%.
ca. $599 MSRP
RTX 4070 12GBNVIDIA-Upgrade12 GB VRAM (+1)504 GB/s (+20)
B
Raises estimated decode speed by about 33%.68.9 tok/s Dekodierung
Raises estimated decode speed by about 33%.
ca. $599 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run Yi Coder 9B Chat?
Yes, GTX 1080 Ti 11GB can run Yi Coder 9B Chat with a B grade (Runs well). Expected decode speed: 52.0 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 GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Yi Coder 9B Chat achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on GTX 1080 Ti 11GB receives a B grade with 52.0 tok/s and 49K context.
What context window can Yi Coder 9B Chat use on GTX 1080 Ti 11GB?
On GTX 1080 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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