Can Yi 1.5 9B run on GTX 1080 Ti 11GB?

YES — Runs Great

B60Good

Estimated from fit model

Yi 1.5 9B needs ~9.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~57 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, 56.6 tok/s, Runs well

9.0 GB required11.0 GB available

82% VRAM used

Fit status

Runs well

Decode

56.6 tok/s

TTFT

3423 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 GTX 1080 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: 56.6 tok/s decode · 3.4s TTFT (warm) · 141 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	56.6 tok/s	1867 ms	4K
Coding	B	Runs well	56.6 tok/s	3423 ms	4K
Agentic Coding	B	Tight fit	56.6 tok/s	4978 ms	4K
Reasoning	B	Runs well	56.6 tok/s	4045 ms	4K
RAG	B	Tight fit	56.6 tok/s	6223 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on GTX 1080 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

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 (+188)

Raises estimated decode speed by about 48%.83.9 tok/s decode

Raises estimated decode speed by about 48%.

~$549 MSRP

RTX 4070 Super 12GBBest value

12 GB VRAM (+1)504 GB/s (+20)

This setup is broadly balanced for this model.70.7 tok/s decode

~$599 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+1)504 GB/s (+20)

This setup is broadly balanced for this model.68.9 tok/s decode

~$599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Yi 1.5 9B?

Yes, GTX 1080 Ti 11GB can run Yi 1.5 9B with a B grade (Runs well). Expected decode speed: 56.6 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Yi 1.5 9B achieves approximately 56.6 tokens per second decode speed with a time-to-first-token of 3423ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on GTX 1080 Ti 11GB receives a B grade with 56.6 tok/s and 4K context.

What context window can Yi 1.5 9B use on GTX 1080 Ti 11GB?

On GTX 1080 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 GTX 1080 Ti 11GB See all hardware for Yi 1.5 9B

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