RX 7900 XT 20GBBest value
20 GB VRAM (+4)800 GB/s (+600)
B
Raises estimated decode speed by about 208%.95.1 tok/s decode
Raises estimated decode speed by about 208%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Yi 1.5 9B needs ~9.8 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 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
Runs well
Decode
30.9 tok/s
TTFT
6265 ms
Safe context
4K
Memory
9.8 GB / 16.0 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 | C | Runs well | 30.9 tok/s | 3417 ms | 4K |
| Coding | B | Runs well | 30.9 tok/s | 6265 ms | 4K |
| Agentic Coding | B | Runs well | 30.9 tok/s | 9113 ms | 4K |
| Reasoning | B | Runs well | 30.9 tok/s | 7404 ms | 4K |
| RAG | B | Runs well | 30.9 tok/s | 11391 ms | 4K |
How Yi 1.5 9B (9B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C52 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C54 |
Q5_K_M | 5 | 6.5 GB | High | C55 |
Q6_K | 6 | 7.4 GB | High | B56 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B56 |
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
Raises estimated decode speed by about 208%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
RTX A4500 20GBBudget pickRaises estimated decode speed by about 220%.
Adds memory headroom for longer context windows and future model growth.
~$2,000 MSRP
Yes, NVIDIA A2 16GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 30.9 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 9.8 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 NVIDIA A2 16GB, Yi 1.5 9B achieves approximately 30.9 tokens per second decode speed with a time-to-first-token of 6265ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B on NVIDIA A2 16GB receives a B grade with 30.9 tok/s and 4K context.
On NVIDIA A2 16GB, 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-a2-16gb" 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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