RX 7600 XT 16GB经济之选
16 GB VRAM (+8)288 GB/s (+8)
C
Raises estimated decode speed by about 66%.30.4 tok/s 解码
Raises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Yi 1.5 9B Chat run on RX 6650 XT 8GB?
YES — With Offload
C49Usable
Estimated from fit model
Yi 1.5 9B Chat needs ~8.2 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.2 GB, 18.3 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
18.3 tok/s
TTFT
10574 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsYi 1.5 9B Chat on RX 6650 XT 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 18.3 tok/s decode · 10.6s TTFT (warm) · 46 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 26.0 tok/s | 4060 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 18.3 tok/s | 10574 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 14.2 tok/s | 19816 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 18.3 tok/s | 12496 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 14.2 tok/s | 24769 ms | 12K |
Quantization options
How Yi 1.5 9B Chat (9B params) fits at each quantization level on RX 6650 XT 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 |
Get started
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 start升级选项
能流畅运行 Yi 1.5 9B Chat 的硬件
RX 9060 XT 16GB最佳性价比
16 GB VRAM (+8)320 GB/s (+40)
C
Raises estimated decode speed by about 101%.36.7 tok/s 解码
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
RX 7700 XT 12GBAMD 升级
12 GB VRAM (+4)432 GB/s (+152)
C
Raises estimated decode speed by about 158%.47.2 tok/s 解码
Raises estimated decode speed by about 158%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Frequently asked questions
Can RX 6650 XT 8GB run Yi 1.5 9B Chat?
Yes, RX 6650 XT 8GB can run Yi 1.5 9B Chat with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 18.3 tok/s.
How much VRAM does Yi 1.5 9B Chat need?
Yi 1.5 9B Chat (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 9B Chat?
The recommended quantization for Yi 1.5 9B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 9B Chat run at on RX 6650 XT 8GB?
On RX 6650 XT 8GB, Yi 1.5 9B Chat achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10574ms using Q4_K_M quantization.
Can RX 6650 XT 8GB run Yi 1.5 9B Chat for coding?
For coding workloads, Yi 1.5 9B Chat on RX 6650 XT 8GB receives a C grade with 18.3 tok/s and 12K context.
What context window can Yi 1.5 9B Chat use on RX 6650 XT 8GB?
On RX 6650 XT 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.
What should I upgrade first if Yi 1.5 9B Chat feels slow on RX 6650 XT 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--yi-1-5-9b-chat-gguf-on-rx-6650-xt-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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