RTX 5070 Ti 16GBコスパ重視16 GB VRAM (+4)896 GB/s (+536)
B
Raises estimated decode speed by about 141%.113.6 tok/sデコード
Raises estimated decode speed by about 141%.
Adds memory headroom for longer context windows and future model growth.
〜$749 MSRP
Can Yi 1.5 9B run on RTX 3060 12GB?
YES — Runs Great
B59Good
Estimated from fit model
Yi 1.5 9B needs ~9.4 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~47 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.4 GB, 47.1 tok/s, Runs well
9.4 GB required12.0 GB available
78% VRAM used
Fit status
Runs well
Decode
47.1 tok/s
TTFT
4113 ms
Safe context
4K
Memory
9.4 GB / 12.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsYi 1.5 9B on RTX 3060 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 47.1 tok/s decode · 4.1s TTFT (warm) · 118 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 47.1 tok/s | 2244 ms | 4K |
| Coding | B | Runs well | 47.1 tok/s | 4113 ms | 4K |
| Agentic Coding | B | Tight fit | 47.1 tok/s | 5983 ms | 4K |
| Reasoning | B | Runs well | 47.1 tok/s | 4861 ms | 4K |
| RAG | B | Tight fit | 47.1 tok/s | 7479 ms | 4K |
Quantization options
How Yi 1.5 9B (9B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C54 |
Q3_K_S | 3 | 4.4 GB | Low | B56 |
NVFP4 | 4 | 5.0 GB | Medium | B56 |
Q4_K_M | 4 | 5.5 GB | Medium | B57 |
Q5_K_M | 5 | 6.5 GB | High | B57 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B56 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startアップグレードオプション
Yi 1.5 9Bを快適に動かすハードウェア
RTX 5070 Ti 16GBコスパ重視RTX 4070 Ti Super 16GBコスパ最良16 GB VRAM (+4)672 GB/s (+312)
B
Raises estimated decode speed by about 126%.106.5 tok/sデコード
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
〜$799 MSRP
RTX 4080 Super 16GBNVIDIAアップグレード16 GB VRAM (+4)736 GB/s (+376)
B
Raises estimated decode speed by about 157%.121 tok/sデコード
Raises estimated decode speed by about 157%.
Adds memory headroom for longer context windows and future model growth.
〜$999 MSRP
Frequently asked questions
Can RTX 3060 12GB run Yi 1.5 9B?
Yes, RTX 3060 12GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 47.1 tok/s.
How much VRAM does Yi 1.5 9B need?
Yi 1.5 9B (9B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 9B?
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 9B run at on RTX 3060 12GB?
On RTX 3060 12GB, Yi 1.5 9B achieves approximately 47.1 tokens per second decode speed with a time-to-first-token of 4113ms using Q4_K_M quantization.
Can RTX 3060 12GB run Yi 1.5 9B for coding?
For coding workloads, Yi 1.5 9B on RTX 3060 12GB receives a B grade with 47.1 tok/s and 4K context.
What context window can Yi 1.5 9B use on RTX 3060 12GB?
On RTX 3060 12GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-rtx-3060-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: