Yi 1.5 9B needs ~6.5 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q2_K quantization, expect ~25 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
2.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.8 tok/s
TTFT
17912 ms
Safe context
4K
Memory
8.5 GB / 6.0 GB
Offload
30%
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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 13.1 tok/s | 8074 ms | 4K |
| Coding | F | Too heavy | 10.8 tok/s | 17912 ms | 4K |
| Agentic Coding | F | Too heavy | 7.7 tok/s | 36470 ms | 4K |
| Reasoning | F | Too heavy | 10.8 tok/s | 21169 ms | 4K |
| RAG | F | Too heavy | 7.7 tok/s | 45588 ms | 4K |
How Yi 1.5 9B (9B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.5 GB | Low | B58 |
Q3_K_S | 3 | 4.4 GB | Low | 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
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 111%.
~$249 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
RTX 3080 Ti 12GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,199 MSRP
Yes, RX 5600 XT 6GB can run Yi 1.5 9B at Q2_K quantization (Runs with offload (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 8.5 GB which exceeds available memory, but at Q2_K it needs only 6.5 GB. Expected decode speed: 25.2 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 8.5 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at Q2_K using 6.5 GB.
The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is Q2_K, which uses 6.5 GB.
On RX 5600 XT 6GB, Yi 1.5 9B achieves approximately 25.2 tokens per second decode speed with a time-to-first-token of 7683ms using Q2_K quantization.
For coding workloads, Yi 1.5 9B on RX 5600 XT 6GB receives a F grade with 10.8 tok/s and 4K context.
On RX 5600 XT 6GB, Yi 1.5 9B can safely use up to 4K tokens of context at Q2_K quantization. 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-rx-5600-xt-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
5.0 GB |
| Medium |
| F0 |
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.