RTX 5070 12GBコスパ重視12 GB VRAM (+1)672 GB/s (+188)
B
Raises estimated decode speed by about 48%.83.9 tok/sデコード
Raises estimated decode speed by about 48%.
〜$549 MSRP
Can Yi 1.5 9B run on GTX 1080 Ti 11GB?
YES — Runs Great
B59Good
Estimated from fit model
Yi 1.5 9B needs ~9.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.0 GB, 56.6 tok/s, Runs well
9.0 GB required11.0 GB available
82% VRAM used
Fit status
Runs well
Decode
56.6 tok/s
TTFT
3423 ms
Safe context
4K
Memory
9.0 GB / 11.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsYi 1.5 9B on GTX 1080 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: 56.6 tok/s decode · 3.4s TTFT (warm) · 141 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 | 56.6 tok/s | 1867 ms | 4K |
| Coding | B | Runs well | 52.0 tok/s | 3722 ms | 4K |
| Agentic Coding | B | Tight fit | 56.6 tok/s | 4978 ms | 4K |
| Reasoning | B | Runs well | 56.6 tok/s | 4045 ms | 4K |
| RAG | B | Tight fit | 56.6 tok/s | 6223 ms | 4K |
Quantization options
How Yi 1.5 9B (9B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B55 |
Q3_K_S | 3 | 4.4 GB | Low | B57 |
NVFP4 | 4 | 5.0 GB | Medium | B57 |
Q4_K_M | 4 | 5.5 GB | Medium | B57 |
Q5_K_M | 5 | 6.5 GB | High | B57 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B56 |
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 on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startアップグレードオプション
Yi 1.5 9Bを快適に動かすハードウェア
Frequently asked questions
Can GTX 1080 Ti 11GB run Yi 1.5 9B?
Yes, GTX 1080 Ti 11GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 52.0 tok/s.
How much VRAM does Yi 1.5 9B need?
Yi 1.5 9B (9B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 9B?
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 9B run at on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Yi 1.5 9B achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run Yi 1.5 9B for coding?
For coding workloads, Yi 1.5 9B on GTX 1080 Ti 11GB receives a B grade with 52.0 tok/s and 4K context.
What context window can Yi 1.5 9B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, 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.
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