RTX 3050 8GBOpción económica8 GB VRAM (+2)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.36.9 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$249 MSRP
Can Yi 1.5 6B run on GTX 1660 Super 6GB?
YES — With Offload
C51Usable
Estimated from fit model
Yi 1.5 6B needs ~6.1 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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, 38.1 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
38.1 tok/s
TTFT
5079 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 GTX 1660 Super 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: 38.1 tok/s decode · 5.1s TTFT (warm) · 95 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 | 50.5 tok/s | 2091 ms | 4K |
| Coding | C | Runs with offload | 35.1 tok/s | 5523 ms | 4K |
| Agentic Coding | D | Very compromised | 25.3 tok/s | 11111 ms | 4K |
| Reasoning | C | Runs with offload | 35.1 tok/s | 6527 ms | 4K |
| RAG | D | Very compromised | 25.3 tok/s | 13888 ms | 4K |
Quantization options
How Yi 1.5 6B (6B params) fits at each quantization level on GTX 1660 Super 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 | 4 | 3.4 GB | Medium | C54 |
Q4_K_M | 4 | 3.7 GB | Medium | F0 |
Q5_K_M | 5 | 4.3 GB | High | F0 |
Q6_K | 6 | 4.9 GB | High | F0 |
Q8_0 | 8 | 6.4 GB | Very High | F0 |
F16 | 16 | 12.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 1.5 6B on your machine.
Run
lms load Yi-1.5-6B-Chat && lms server startOpciones de mejora
Hardware que ejecuta bien Yi 1.5 6B
RTX 3050 8GBOpción económicaRTX 5060 8GBMejor relación calidad-precio8 GB VRAM (+2)448 GB/s (+112)
B
Sube la velocidad estimada de decodificación alrededor de un 113%.81.2 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 113%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$299 MSRP
RTX 5050 8GBMejora NVIDIA8 GB VRAM (+2)
B
Sube la velocidad estimada de decodificación alrededor de un 47%.55.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 47%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$299 MSRP
Frequently asked questions
Can GTX 1660 Super 6GB run Yi 1.5 6B?
Yes, GTX 1660 Super 6GB can run Yi 1.5 6B with a C grade (Runs with offload). Expected decode speed: 35.1 tok/s.
How much VRAM does Yi 1.5 6B need?
Yi 1.5 6B (6B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 6B?
The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 6B run at on GTX 1660 Super 6GB?
On GTX 1660 Super 6GB, Yi 1.5 6B achieves approximately 35.1 tokens per second decode speed with a time-to-first-token of 5523ms using Q4_K_M quantization.
Can GTX 1660 Super 6GB run Yi 1.5 6B for coding?
For coding workloads, Yi 1.5 6B on GTX 1660 Super 6GB receives a C grade with 35.1 tok/s and 4K context.
What context window can Yi 1.5 6B use on GTX 1660 Super 6GB?
On GTX 1660 Super 6GB, Yi 1.5 6B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
What should I upgrade first if Yi 1.5 6B feels slow on GTX 1660 Super 6GB?
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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