RTX 5070 12GBOpción económica12 GB VRAM (+1)672 GB/s (+56)
B
Este setup está bastante equilibrado para este modelo.77.1 tok/s decodificación
~$549 MSRP
Can Yi 1.5 9B Chat run on RTX 2080 Ti 11GB?
YES — Runs Great
B56Good
Estimated from fit model
Yi 1.5 9B Chat needs ~8.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~73 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 8.8 GB, 72.9 tok/s, Runs well
8.8 GB required11.0 GB available
80% VRAM used
Fit status
Runs well
Decode
72.9 tok/s
TTFT
2655 ms
Safe context
49K
Memory
8.8 GB / 11.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsYi 1.5 9B Chat on RTX 2080 Ti 11GB
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: 72.9 tok/s decode · 2.7s TTFT (warm) · 182 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 72.9 tok/s | 1448 ms | 49K |
| Coding | B | Runs well | 72.9 tok/s | 2655 ms | 49K |
| Agentic Coding | C | Tight fit | 72.9 tok/s | 3861 ms | 49K |
| Reasoning | B | Runs well | 72.9 tok/s | 3137 ms | 49K |
| RAG | C | Tight fit | 72.9 tok/s | 4826 ms | 49K |
Quantization options
How Yi 1.5 9B Chat (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C51 |
Q3_K_S | 3 | 4.4 GB | Low | C52 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C52 |
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 startOpciones de mejora
Hardware que ejecuta bien Yi 1.5 9B Chat
RTX 5070 12GBOpción económicaRTX 3080 12GBMejor relación calidad-precio12 GB VRAM (+1)912 GB/s (+296)
B
Sube la velocidad estimada de decodificación alrededor de un 73%.126 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 73%.
~$799 MSRP
RTX 3080 Ti 12GBMejora NVIDIA12 GB VRAM (+1)912 GB/s (+296)
B
Sube la velocidad estimada de decodificación alrededor de un 69%.122.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 69%.
~$1,199 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run Yi 1.5 9B Chat?
Yes, RTX 2080 Ti 11GB can run Yi 1.5 9B Chat with a B grade (Runs well). Expected decode speed: 72.9 tok/s.
How much VRAM does Yi 1.5 9B Chat need?
Yi 1.5 9B Chat (9B parameters) requires approximately 8.8 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 RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Yi 1.5 9B Chat achieves approximately 72.9 tokens per second decode speed with a time-to-first-token of 2655ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run Yi 1.5 9B Chat for coding?
For coding workloads, Yi 1.5 9B Chat on RTX 2080 Ti 11GB receives a B grade with 72.9 tok/s and 49K context.
What context window can Yi 1.5 9B Chat use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Yi 1.5 9B Chat can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--yi-1-5-9b-chat-gguf-on-rtx-2080-ti-11gb" 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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