Yi 1.5 9B Chat needs ~8.2 GB VRAM. RX 7600 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
21.4 tok/s
TTFT
9039 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 30.4 tok/s | 3471 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 21.4 tok/s | 9039 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 16.6 tok/s | 16940 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 21.4 tok/s | 10683 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 16.6 tok/s | 21175 ms | 12K |
How Yi 1.5 9B Chat (9B params) fits at each quantization level on RX 7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | C53 |
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 |
Copy-paste commands to run Yi 1.5 9B Chat on your machine.
Run
lms load hf-bartowski--yi-1-5-9b-chat-gguf && lms server startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 42%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
Sube la velocidad estimada de decodificación alrededor de un 71%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$349 MSRP
Sube la velocidad estimada de decodificación alrededor de un 121%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
Yes, RX 7600 8GB can run Yi 1.5 9B Chat with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 21.4 tok/s.
Yi 1.5 9B Chat (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 9B Chat is Q4_K_M, which balances quality and memory efficiency.
On RX 7600 8GB, Yi 1.5 9B Chat achieves approximately 21.4 tokens per second decode speed with a time-to-first-token of 9039ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B Chat on RX 7600 8GB receives a C grade with 21.4 tok/s and 12K context.
On RX 7600 8GB, Yi 1.5 9B Chat can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--yi-1-5-9b-chat-gguf-on-rx-7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: