Yi 1.5 9B needs ~8.7 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~21 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
21.4 tok/s
TTFT
9050 ms
Safe context
4K
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 | 33.7 tok/s | 3137 ms | 4K |
| Coding | C | Very compromised (needs ~0.4 GB host RAM) | 21.4 tok/s | 9050 ms | 4K |
| Agentic Coding | F | Too heavy | 15.4 tok/s | 18295 ms | 4K |
| Reasoning | C | Very compromised (needs ~0.4 GB host RAM) | 21.4 tok/s | 10696 ms | 4K |
| RAG | F | Too heavy | 15.4 tok/s | 22869 ms |
How Yi 1.5 9B (9B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B58 |
Q3_K_S | 3 | 4.4 GB | Low | B58 |
NVFP4Best for your GPU |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 55%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 86%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 140%.
~$449 MSRP
Yes, Radeon PRO W7600 8GB can run Yi 1.5 9B with a C grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 21.4 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 8.7 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 Radeon PRO W7600 8GB, Yi 1.5 9B achieves approximately 21.4 tokens per second decode speed with a time-to-first-token of 9050ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B on Radeon PRO W7600 8GB receives a C grade with 21.4 tok/s and 4K context.
On Radeon PRO W7600 8GB, 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-radeon-pro-w7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
5.0 GB |
| Medium |
| B57 |
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.