Would CUDA be a better path than Intel Arc Pro B60 24GB for exaone 3.0 7.8b it?

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 exaone 3.0 7.8b it run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if exaone 3.0 7.8b it 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

C48Usable

Estimated from fit model

exaone 3.0 7.8b it needs ~9.0 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~52 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, 51.8 tok/s, Runs well

9.0 GB required24.0 GB available

38% VRAM used

Fit status

Runs well

Decode

51.8 tok/s

TTFT

3741 ms

Safe context

279K

Memory

9.0 GB / 24.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsexaone 3.0 7.8b it 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: 51.8 tok/s decode · 3.7s TTFT (warm) · 129 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	51.8 tok/s	2040 ms	279K
Coding	C	Runs well	51.8 tok/s	3741 ms	279K
Agentic Coding	C	Runs well	51.8 tok/s	5441 ms	279K
Reasoning	C	Runs well	51.8 tok/s	4421 ms	279K
RAG	C	Runs well	51.8 tok/s	6802 ms	279K

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C44
Q3_K_S	3	3.8 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run exaone 3.0 7.8b it on your machine.

Run

lms load hf-bingsu--exaone-3-0-7-8b-it && lms server start

Upgrade options

Hardware that runs exaone 3.0 7.8b it well

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 186%.148.2 tok/s decode

Raises estimated decode speed by about 186%.

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.59.2 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run exaone 3.0 7.8b it?

Yes, Intel Arc Pro B60 24GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 51.8 tok/s.

How much VRAM does exaone 3.0 7.8b it need?

exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for exaone 3.0 7.8b it?

The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.

What speed will exaone 3.0 7.8b it run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, exaone 3.0 7.8b it achieves approximately 51.8 tokens per second decode speed with a time-to-first-token of 3741ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run exaone 3.0 7.8b it for coding?

For coding workloads, exaone 3.0 7.8b it on Intel Arc Pro B60 24GB receives a C grade with 51.8 tok/s and 279K context.

What context window can exaone 3.0 7.8b it use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, exaone 3.0 7.8b it can safely use up to 279K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B60 24GB See all hardware for exaone 3.0 7.8b it

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