Will It Run AI

Can EXAONE 3.5 7.8B Instruct i1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C48Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 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
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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 3.5 7.8B Instruct i1 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well51.8 tok/s2040 ms279K
CodingCRuns well51.8 tok/s3741 ms279K
Agentic CodingCRuns well51.8 tok/s5441 ms279K
ReasoningCRuns well51.8 tok/s4421 ms279K
RAGCRuns well51.8 tok/s6802 ms279K

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC44
Q3_K_S
3
3.8 GB
LowC45
NVFP4
4
4.4 GB
MediumC45
Q4_K_M
4
4.8 GB
MediumC45
Q5_K_M
5
5.6 GB
HighC46
Q6_K
6
6.4 GB
HighC46
Q8_0
8
8.3 GB
Very HighC47
F16Best for your GPU
16
16.0 GB
MaximumC50

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.

Run

lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server start

Upgrade-Optionen

Hardware, die EXAONE 3.5 7.8B Instruct i1 gut ausführt

NVIDIARTX 5090 32GBGünstige Wahl
32 GB VRAM (+8)1792 GB/s (+1336)
C
Raises estimated decode speed by about 186%.148.2 tok/s Dekodierung

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.

ca. $1,999 MSRP

MacBook Pro M4 Max 36GBBestes Preis-Leistungs-Verhältnis
36 GB Unified (+12)
C
This setup is broadly balanced for this model.59.2 tok/s Dekodierung

ca. $2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run EXAONE 3.5 7.8B Instruct i1?

Yes, Intel Arc Pro B60 24GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 51.8 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct i1 need?

EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct i1?

The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct i1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, EXAONE 3.5 7.8B Instruct i1 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 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on Intel Arc Pro B60 24GB receives a C grade with 51.8 tok/s and 279K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 279K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if EXAONE 3.5 7.8B Instruct i1 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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for EXAONE 3.5 7.8B Instruct i1?

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.

See all results for Intel Arc Pro B60 24GBSee all hardware for EXAONE 3.5 7.8B Instruct i1
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