RTX 3080 10GBコスパ重視10 GB VRAM (+2)760 GB/s (+536)
C
Raises estimated decode speed by about 95%.72 tok/sデコード
Raises estimated decode speed by about 95%.
Adds memory headroom for longer context windows and future model growth.
〜$699 MSRP
Can Yi 1.5 6B run on RTX 3050 8GB?
YES — Runs Great
C54Usable
Estimated from fit model
Yi 1.5 6B needs ~6.3 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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, 36.9 tok/s, Runs well
6.3 GB required8.0 GB available
79% VRAM used
Fit status
Runs well
Decode
36.9 tok/s
TTFT
5247 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 RTX 3050 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: 36.9 tok/s decode · 5.2s TTFT (warm) · 92 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 36.9 tok/s | 2862 ms | 4K |
| Coding | C | Runs well | 36.9 tok/s | 5247 ms | 4K |
| Agentic Coding | C | Tight fit | 36.9 tok/s | 7632 ms | 4K |
| Reasoning | C | Runs well | 40.4 tok/s | 5664 ms | 4K |
| RAG | C | Tight fit | 36.9 tok/s | 9539 ms | 4K |
Quantization options
How Yi 1.5 6B (6B params) fits at each quantization level on RTX 3050 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 | 3.4 GB | Medium | C53 |
Q4_K_M | 4 | 3.7 GB | Medium | C53 |
Q5_K_M | 5 | 4.3 GB | High | C53 |
Q6_KBest for your GPU | 6 | 4.9 GB | High | C53 |
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 startアップグレードオプション
Yi 1.5 6Bを快適に動かすハードウェア
RTX 3080 10GBコスパ重視GTX 1080 Ti 11GBコスパ最良11 GB VRAM (+3)484 GB/s (+260)
C
Raises estimated decode speed by about 128%.84 tok/sデコード
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
〜$699 MSRP
RTX 2080 Ti 11GBNVIDIAアップグレード11 GB VRAM (+3)616 GB/s (+392)
C
Raises estimated decode speed by about 128%.84 tok/sデコード
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
〜$999 MSRP
Frequently asked questions
Can RTX 3050 8GB run Yi 1.5 6B?
Yes, RTX 3050 8GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 36.9 tok/s.
How much VRAM does Yi 1.5 6B need?
Yi 1.5 6B (6B parameters) requires approximately 6.3 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 RTX 3050 8GB?
On RTX 3050 8GB, Yi 1.5 6B achieves approximately 36.9 tokens per second decode speed with a time-to-first-token of 5247ms using Q4_K_M quantization.
Can RTX 3050 8GB run Yi 1.5 6B for coding?
For coding workloads, Yi 1.5 6B on RTX 3050 8GB receives a C grade with 36.9 tok/s and 4K context.
What context window can Yi 1.5 6B use on RTX 3050 8GB?
On RTX 3050 8GB, 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.
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