Can Yi 1.5 9B run on RTX 2080 Ti 11GB?

YES — Runs Great

B61Good

Estimated from fit model

Yi 1.5 9B needs ~9.0 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~79 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 9.0 GB, 79.3 tok/s, Runs well

9.0 GB required11.0 GB available

82% VRAM used

Fit status

Runs well

Decode

79.3 tok/s

TTFT

2441 ms

Safe context

Memory

9.0 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on RTX 2080 Ti 11GB
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: 79.3 tok/s decode · 2.4s TTFT (warm) · 198 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	B	Runs well	79.3 tok/s	1331 ms	4K
Coding	B	Runs well	79.3 tok/s	2441 ms	4K
Agentic Coding	B	Tight fit	79.3 tok/s	3550 ms	4K
Reasoning	B	Runs well	79.3 tok/s	2885 ms	4K
RAG	B	Tight fit	79.3 tok/s	4438 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B55
Q3_K_S	3	4.4 GB	Low	B57
NVFP4	4	5.0 GB	Medium	B57
Q4_K_M	4	5.5 GB	Medium	B57
Q5_K_M	5	6.5 GB	High	B57
Q6_KBest for your GPU	6	7.4 GB	High	B56
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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 12GBBudget pick

12 GB VRAM (+1)672 GB/s (+56)

This setup is broadly balanced for this model.83.9 tok/s decode

~$549 MSRP

RTX 3080 12GBBest value

12 GB VRAM (+1)912 GB/s (+296)

Raises estimated decode speed by about 36%.108 tok/s decode

Raises estimated decode speed by about 36%.

~$799 MSRP

RTX 3080 Ti 12GBNVIDIA upgrade

12 GB VRAM (+1)912 GB/s (+296)

Raises estimated decode speed by about 36%.108 tok/s decode

Raises estimated decode speed by about 36%.

~$1,199 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run Yi 1.5 9B?

Yes, RTX 2080 Ti 11GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 79.3 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 9.0 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 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi 1.5 9B achieves approximately 79.3 tokens per second decode speed with a time-to-first-token of 2441ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX 2080 Ti 11GB receives a B grade with 79.3 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, 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 2080 Ti 11GB See all hardware for Yi 1.5 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

79.3 tok/s

1331 ms

Coding

Runs well

79.3 tok/s

2441 ms

Agentic Coding

Tight fit

79.3 tok/s

3550 ms

Reasoning

Runs well

79.3 tok/s

2885 ms

RAG

Tight fit

79.3 tok/s

4438 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B55

Q3_K_S

4.4 GB

Low

B57

NVFP4

5.0 GB

Medium

B57

Q4_K_M

5.5 GB

Medium

B57

Q5_K_M

6.5 GB

High

B57

Q6_KBest for your GPU

7.4 GB

High

B56

Q8_0

9.6 GB

Very High

F16

18.5 GB

Maximum

Frequently asked questions

Can RTX 2080 Ti 11GB run Yi 1.5 9B?

Yes, RTX 2080 Ti 11GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 79.3 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 9.0 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 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi 1.5 9B achieves approximately 79.3 tokens per second decode speed with a time-to-first-token of 2441ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on RTX 2080 Ti 11GB receives a B grade with 79.3 tok/s and 4K context.

What context window can Yi 1.5 9B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, 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.