Can Yi 1.5 9B run on Intel Arc B580 12GB?

YES — Runs Great

B59Good

○Estimated from fit model

Yi 1.5 9B needs ~9.1 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~43 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) — 9.1 GB, 43.4 tok/s, Runs well

9.1 GB required12.0 GB available

76% VRAM used

Fit status

Runs well

Decode

43.4 tok/s

TTFT

4465 ms

Safe context

Memory

9.1 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on Intel Arc B580 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: 43.4 tok/s decode · 4.5s TTFT (warm) · 108 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	43.4 tok/s	2436 ms	4K
Coding	B	Runs well	43.4 tok/s	4465 ms	4K
Agentic Coding	B	Tight fit	43.4 tok/s	6495 ms	4K
Reasoning	B	Runs well	43.4 tok/s	5277 ms	4K
RAG	B	Tight fit	43.4 tok/s	8119 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on Intel Arc B580 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	5.0 GB	Medium	B56
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

👁 NVIDIA
RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)896 GB/s (+440)

Raises estimated decode speed by about 162%.113.6 tok/s decode

Raises estimated decode speed by about 162%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$749 MSRP

👁 NVIDIA
RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)672 GB/s (+216)

Raises estimated decode speed by about 126%.98 tok/s decode

Raises estimated decode speed by about 126%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$799 MSRP

Frequently asked questions

See all results for Intel Arc B580 12GB See all hardware for Yi 1.5 9B

URL: https://willitrunai.com/can-run/yi-1.5-9b-on-arc-b580-12gb