Yi 1.5 9B Chat needs ~8.2 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 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
13.7 tok/s
TTFT
14143 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 20.1 tok/s | 5267 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 13.7 tok/s | 14143 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 10.5 tok/s | 26796 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 13.7 tok/s | 16714 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 10.5 tok/s | 33494 ms | 12K |
How Yi 1.5 9B Chat (9B params) fits at each quantization level on RX 580 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 122%.
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 168%.
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 245%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
Yes, RX 580 8GB can run Yi 1.5 9B Chat with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 13.7 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 580 8GB, Yi 1.5 9B Chat achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14143ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B Chat on RX 580 8GB receives a C grade with 13.7 tok/s and 12K context.
On RX 580 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-580-8gb" 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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