RTX 3060 12GBBudget pick12 GB VRAM (+2)
B
This setup is broadly balanced for this model.47.1 tok/s decode
~$329 MSRP
Yi 1.5 9B needs ~9.2 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~114 tok/s.
Operating mode
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
Fit status
Tight fit
Decode
114.4 tok/s
TTFT
1692 ms
Safe context
4K
Memory
9.2 GB / 10.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 114.4 tok/s | 923 ms | 4K |
| Coding | B | Tight fit | 114.4 tok/s | 1692 ms | 4K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 75.6 tok/s | 3724 ms | 4K |
| Reasoning | B | Tight fit | 114.4 tok/s | 2000 ms | 4K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 75.6 tok/s | 4655 ms | 4K |
How Yi 1.5 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B56 |
Q3_K_S | 3 | 4.4 GB | Low | B57 |
NVFP4 | 4 | 5.0 GB | Medium | B57 |
Q4_K_M | 4 | 5.5 GB | Medium | B57 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | B57 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startUpgrade options
RTX 3060 12GBBudget pick~$329 MSRP
RTX 5070 12GBBest value~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgrade~$599 MSRP
Yes, RTX 3080 10GB can run Yi 1.5 9B with a B grade (Tight fit). Expected decode speed: 114.4 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3080 10GB, Yi 1.5 9B achieves approximately 114.4 tokens per second decode speed with a time-to-first-token of 1692ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B on RTX 3080 10GB receives a B grade with 114.4 tok/s and 4K context.
On RTX 3080 10GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-rtx-3080-10gb" 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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