8 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.36.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Can Yi 1.5 6B run on RTX 2060 6GB?
YES — With Offload
C51Usable
○Estimated from fit model
Yi 1.5 6B needs ~6.1 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~36 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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) — 6.1 GB, 39.5 tok/s, Runs with offload (needs ~0.1 GB host RAM)
6.1 GB required6.0 GB available
102% VRAM needed
100 MB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
39.5 tok/s
TTFT
4900 ms
Safe context
4K
Memory
6.1 GB / 6.0 GB
Memory breakdown
Weights3.7 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsYi 1.5 6B on RTX 2060 6GB
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: 39.5 tok/s decode · 4.9s TTFT (warm) · 99 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.
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
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 | Tight fit | 52.3 tok/s | 2018 ms | 4K |
| Coding | C | Runs with offload | 36.3 tok/s | 5329 ms | 4K |
| Agentic Coding | D | Very compromised | 26.3 tok/s | 10721 ms | 4K |
| Reasoning | C | Runs with offload | 36.3 tok/s | 6298 ms | 4K |
| RAG | D | Very compromised | 26.3 tok/s | 13401 ms | 4K |
Quantization options
How Yi 1.5 6B (6B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C54 |
Q3_K_S | 3 | 2.9 GB | Low | C54 |
NVFP4Best for your GPU |
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
Hardware that runs Yi 1.5 6B well
8 GB VRAM (+2)448 GB/s (+112)
B
Raises estimated decode speed by about 106%.81.2 tok/s decode
Raises estimated decode speed by about 106%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
8 GB VRAM (+2)
B
Raises estimated decode speed by about 42%.55.9 tok/s decode
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP