Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Yi Coder 9B needs ~8.7 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 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
0.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
35.8 tok/s
TTFT
5402 ms
Safe context
9K
Memory
8.7 GB / 8.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload | 55.0 tok/s | 1919 ms | 9K |
| Coding | C | Very compromised (needs ~0.4 GB host RAM) | 35.8 tok/s | 5402 ms | 9K |
| Agentic Coding | F | Too heavy | 26.0 tok/s | 10811 ms | 9K |
| Reasoning | C | Very compromised (needs ~0.4 GB host RAM) | 35.8 tok/s | 6385 ms | 9K |
| RAG | F | Too heavy | 26.0 tok/s | 13513 ms |
How Yi Coder 9B (9B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B66 |
Q3_K_S | 3 | 4.4 GB | Low | B65 |
NVFP4Best for your GPU |
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 54%.
~$449 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, RTX 5060 Ti 8GB can run Yi Coder 9B with a C grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 35.8 tok/s.
Yi Coder 9B (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5060 Ti 8GB, Yi Coder 9B achieves approximately 35.8 tokens per second decode speed with a time-to-first-token of 5402ms using Q4_K_M quantization.
For coding workloads, Yi Coder 9B on RTX 5060 Ti 8GB receives a C grade with 35.8 tok/s and 9K context.
On RTX 5060 Ti 8GB, Yi Coder 9B can safely use up to 9K tokens of context. The model's official context limit is 131K, 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-coder-9b-on-rtx-5060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 9K |
| 4 |
5.0 GB |
| Medium |
| B65 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.