Would CUDA be a better path than Intel Arc Pro B60 24GB for Yi 1.5 9B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Yi 1.5 9B run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if Yi 1.5 9B feels slow on Intel Arc Pro B60 24GB?

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.

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 9B needs ~10.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 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) — 10.3 GB, 48.8 tok/s, Runs well

10.3 GB required24.0 GB available

43% VRAM used

Fit status

Runs well

Decode

48.8 tok/s

TTFT

3969 ms

Safe context

Memory

10.3 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi 1.5 9B on Intel Arc Pro B60 24GB
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: 48.8 tok/s decode · 4.0s TTFT (warm) · 122 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	C	Runs well	48.8 tok/s	2165 ms	4K
Coding	C	Runs well	48.8 tok/s	3969 ms	4K
Agentic Coding	C	Runs well	48.8 tok/s	5773 ms	4K
Reasoning	C	Runs well	48.8 tok/s	4691 ms	4K
RAG	C	Runs well	48.8 tok/s	7217 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C49
Q3_K_S	3	4.4 GB	Low	C50
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 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 250%.171 tok/s decode

Raises estimated decode speed by about 250%.

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.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+12)

This setup is broadly balanced for this model.55.8 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Yi 1.5 9B?

Yes, Intel Arc Pro B60 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 48.8 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 10.3 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 Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Yi 1.5 9B achieves approximately 48.8 tokens per second decode speed with a time-to-first-token of 3969ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on Intel Arc Pro B60 24GB receives a C grade with 48.8 tok/s and 4K context.

What context window can Yi 1.5 9B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, 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.

What should I upgrade first if Yi 1.5 9B feels slow on Intel Arc Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for Yi 1.5 9B

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