Can EXAONE 3.5 2.4B Instruct run on Intel Arc A770 16GB?

YES — Runs Great

C44Usable

Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~4.2 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~34 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) — 4.2 GB, 33.6 tok/s, Runs well

4.2 GB required16.0 GB available

26% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5762 ms

Safe context

685K

Memory

4.2 GB / 16.0 GB

Memory breakdown

Weights1.5 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on Intel Arc A770 16GB
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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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	33.6 tok/s	3143 ms	685K
Coding	C	Runs well	33.6 tok/s	5762 ms	685K
Agentic Coding	C	Runs well	33.6 tok/s	8381 ms	685K
Reasoning	C	Runs well	33.6 tok/s	6810 ms	685K
RAG	C	Runs well	33.6 tok/s	10476 ms	685K

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.9 GB	Low	C45
Q3_K_S	3	1.2 GB	Low	C45
NVFP4	4	1.3 GB	Medium	C45
Q4_K_M	4	1.5 GB	Medium	C46
Q5_K_M	5	1.7 GB	High	C46
Q6_K	6	2.0 GB	High	C46
Q8_0	8	2.6 GB	Very High	C46
F16Best for your GPU	16	4.9 GB	Maximum	C48

Get started

Copy-paste commands to run EXAONE 3.5 2.4B Instruct on your machine.

Run

lms load hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf && lms server start

アップグレードオプション

EXAONE 3.5 2.4B Instructを快適に動かすハードウェア

MacBook Pro M4 32GBコスパ重視

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.33.6 tok/sデコード

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

〜$799 MSRP

MacBook Pro M3 24GBコスパ最良

24 GB Unified (+8)

This setup is broadly balanced for this model.33.6 tok/sデコード

〜$1,099 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run EXAONE 3.5 2.4B Instruct?

Yes, Intel Arc A770 16GB can run EXAONE 3.5 2.4B Instruct with a C grade (Runs well). Expected decode speed: 33.6 tok/s.

How much VRAM does EXAONE 3.5 2.4B Instruct need?

EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 4.2 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 2.4B Instruct?

The recommended quantization for EXAONE 3.5 2.4B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 2.4B Instruct run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, EXAONE 3.5 2.4B Instruct achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5762ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on Intel Arc A770 16GB receives a C grade with 33.6 tok/s and 685K context.

What context window can EXAONE 3.5 2.4B Instruct use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, EXAONE 3.5 2.4B Instruct can safely use up to 685K 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 2.4B Instruct feels slow on Intel Arc A770 16GB?

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 A770 16GB for EXAONE 3.5 2.4B Instruct?

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 A770 16GB See all hardware for EXAONE 3.5 2.4B Instruct

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