RTX 3060 12GB经济之选12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.36.4 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Yi 9B Coder i1 run on RTX 2070 Super 8GB?
YES — With Offload
C50Usable
Estimated from fit model
Yi 9B Coder i1 needs ~8.2 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~34 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, 34.0 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
34.0 tok/s
TTFT
5697 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 2070 Super 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: 34.0 tok/s decode · 5.7s TTFT (warm) · 85 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.
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
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 | 49.8 tok/s | 2121 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 34.0 tok/s | 5697 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 26.1 tok/s | 10794 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 34.0 tok/s | 6733 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 26.1 tok/s | 13492 ms | 12K |
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 2070 Super 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 start升级选项
能流畅运行 Yi 9B Coder i1 的硬件
RTX 3060 12GB经济之选RTX 5060 Ti 16GB最佳性价比16 GB VRAM (+8)
C
Raises estimated decode speed by about 49%.50.6 tok/s 解码
Raises estimated decode speed by about 49%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA 升级16 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.35.2 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Frequently asked questions
Can RTX 2070 Super 8GB run Yi 9B Coder i1?
Yes, RTX 2070 Super 8GB can run Yi 9B Coder i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 34.0 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 2070 Super 8GB?
On RTX 2070 Super 8GB, Yi 9B Coder i1 achieves approximately 34.0 tokens per second decode speed with a time-to-first-token of 5697ms using Q4_K_M quantization.
Can RTX 2070 Super 8GB run Yi 9B Coder i1 for coding?
For coding workloads, Yi 9B Coder i1 on RTX 2070 Super 8GB receives a C grade with 34.0 tok/s and 12K context.
What context window can Yi 9B Coder i1 use on RTX 2070 Super 8GB?
On RTX 2070 Super 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 2070 Super 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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