RTX 3060 12GBOpção econômica12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.36.4 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Yi 9B Coder i1 run on RTX 3060 Ti 8GB?
YES — With Offload
C50Usable
Estimated from fit model
Yi 9B Coder i1 needs ~8.2 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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) — 8.2 GB, 32.8 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
32.8 tok/s
TTFT
5901 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsYi 9B Coder i1 on RTX 3060 Ti 8GB
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: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 46.6 tok/s | 2266 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 32.8 tok/s | 5901 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 25.5 tok/s | 11058 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 32.8 tok/s | 6973 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 25.5 tok/s | 13822 ms | 12K |
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
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 9B Coder i1 on your machine.
Run
lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem Yi 9B Coder i1
RTX 3060 12GBOpção econômicaRTX 5060 Ti 16GBMelhor custo-benefício16 GB VRAM (+8)
C
Raises estimated decode speed by about 54%.50.6 tok/s decodificação
Raises estimated decode speed by about 54%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBUpgrade NVIDIA16 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.35.2 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Frequently asked questions
Can RTX 3060 Ti 8GB run Yi 9B Coder i1?
Yes, RTX 3060 Ti 8GB can run Yi 9B Coder i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 32.8 tok/s.
How much VRAM does Yi 9B Coder i1 need?
Yi 9B Coder i1 (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 9B Coder i1?
The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 9B Coder i1 run at on RTX 3060 Ti 8GB?
On RTX 3060 Ti 8GB, Yi 9B Coder i1 achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5901ms using Q4_K_M quantization.
Can RTX 3060 Ti 8GB run Yi 9B Coder i1 for coding?
For coding workloads, Yi 9B Coder i1 on RTX 3060 Ti 8GB receives a C grade with 32.8 tok/s and 12K context.
What context window can Yi 9B Coder i1 use on RTX 3060 Ti 8GB?
On RTX 3060 Ti 8GB, Yi 9B Coder i1 can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Yi 9B Coder i1 feels slow on RTX 3060 Ti 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--yi-9b-coder-i1-gguf-on-rtx-3060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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