10 GB VRAM (+2)760 GB/s (+504)
C
Raises estimated decode speed by about 60%.72 tok/s decode
Raises estimated decode speed by about 60%.
Adds memory headroom for longer context windows and future model growth.
~$699 MSRP
Can Yi 1.5 6B run on GTX 1070 Ti 8GB?
YES — Runs Great
C54Usable
○Estimated from fit model
Yi 1.5 6B needs ~6.3 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~45 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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) — 6.3 GB, 44.9 tok/s, Runs well
6.3 GB required8.0 GB available
79% VRAM used
Fit status
Runs well
Decode
44.9 tok/s
TTFT
4314 ms
Safe context
4K
Memory
6.3 GB / 8.0 GB
Memory breakdown
Weights3.7 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsYi 1.5 6B on GTX 1070 Ti 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: 44.9 tok/s decode · 4.3s TTFT (warm) · 112 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 | C | Runs well | 44.9 tok/s | 2353 ms | 4K |
| Coding | C | Runs well | 44.9 tok/s | 4314 ms | 4K |
| Agentic Coding | C | Tight fit | 44.9 tok/s | 6275 ms | 4K |
| Reasoning | C | Runs well | 44.9 tok/s | 5098 ms | 4K |
| RAG | C | Tight fit | 44.9 tok/s | 7843 ms | 4K |
Quantization options
How Yi 1.5 6B (6B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C52 |
Q3_K_S | 3 | 2.9 GB | Low | C53 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Yi 1.5 6B on your machine.
Run
lms load Yi-1.5-6B-Chat && lms server startUpgrade options