Can Yi 1.5 9B run on RTX 3060 12GB?

YES — Runs Great

B59Good

Estimated from fit model

Yi 1.5 9B needs ~9.4 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.4 GB, 47.1 tok/s, Runs well

9.4 GB required12.0 GB available

78% VRAM used

Fit status

Runs well

Decode

47.1 tok/s

TTFT

4113 ms

Safe context

Memory

9.4 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on RTX 3060 12GB
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: 47.1 tok/s decode · 4.1s TTFT (warm) · 118 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	B	Runs well	47.1 tok/s	2244 ms	4K
Coding	B	Runs well	43.3 tok/s	4473 ms	4K
Agentic Coding	B	Tight fit	47.1 tok/s	5983 ms	4K
Reasoning	B	Runs well	47.1 tok/s	4861 ms	4K
RAG	B	Tight fit	47.1 tok/s	7479 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C54
Q3_K_S	3	4.4 GB	Low	B56
NVFP4	4

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

Upgrade options

Hardware that runs Yi 1.5 9B well

RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)896 GB/s (+536)

Raises estimated decode speed by about 141%.113.6 tok/s decode

Raises estimated decode speed by about 141%.

Adds memory headroom for longer context windows and future model growth.

~$749 MSRP

RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 126%.106.5 tok/s decode

Raises estimated decode speed by about 126%.

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

RTX 4080 Super 16GBNVIDIA upgrade

16 GB VRAM (+4)736 GB/s (+376)

Raises estimated decode speed by about 157%.121 tok/s decode

Raises estimated decode speed by about 157%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can RTX 3060 12GB run Yi 1.5 9B?

Yes, RTX 3060 12GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 43.3 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 9.4 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 RTX 3060 12GB?

On RTX 3060 12GB, Yi 1.5 9B achieves approximately 43.3 tokens per second decode speed with a time-to-first-token of 4473ms using Q4_K_M quantization.

Can RTX 3060 12GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX 3060 12GB receives a B grade with 43.3 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX 3060 12GB?

On RTX 3060 12GB, 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.

See all results for RTX 3060 12GB See all hardware for Yi 1.5 9B

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