Would CUDA be a better path than Gaudi 3 128GB 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.

Can EXAONE 3.5 2.4B Instruct run on Gaudi 3 128GB?

Q: What should I upgrade first if EXAONE 3.5 2.4B Instruct feels slow on Gaudi 3 128GB?

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

C41Usable

Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~15.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 15.4 GB, 33.6 tok/s, Runs well

15.4 GB required128.0 GB available

12% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5762 ms

Safe context

6.4M

Memory

15.4 GB / 128.0 GB

Memory breakdown

Weights1.5 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on Gaudi 3 128GB
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	6.4M
Coding	C	Runs well	33.6 tok/s	5762 ms	6.4M
Agentic Coding	C	Runs well	33.6 tok/s	8381 ms	6.4M
Reasoning	C	Runs well	33.6 tok/s	6810 ms	6.4M
RAG	C	Runs well	33.6 tok/s	10476 ms	6.4M

Inference speed

EXAONE 3.5 2.4B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 3.5 2.4B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~46 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	45.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	38.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	38.4

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.9 GB	Low	D38
Q3_K_S	3	1.2 GB	Low	D38
NVFP4	4

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

Upgrade options

Hardware that runs EXAONE 3.5 2.4B Instruct well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)

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

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

~$6,999 MSRP

Frequently asked questions

Can Gaudi 3 128GB run EXAONE 3.5 2.4B Instruct?

Yes, Gaudi 3 128GB 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 15.4 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 Gaudi 3 128GB?

On Gaudi 3 128GB, 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 Gaudi 3 128GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on Gaudi 3 128GB receives a C grade with 33.6 tok/s and 6.4M context.

What context window can EXAONE 3.5 2.4B Instruct use on Gaudi 3 128GB?

On Gaudi 3 128GB, EXAONE 3.5 2.4B Instruct can safely use up to 6.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Gaudi 3 128GB See all hardware for EXAONE 3.5 2.4B Instruct

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