Will It Run AI

Can EXAONE 3.5 2.4B Instruct run on AMD Instinct MI250 128GB?

YES — Runs Great

C41Usable
Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~15.4 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 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 AMD Instinct MI250 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

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well33.6 tok/s3143 ms6.4M
CodingCRuns well33.6 tok/s5762 ms6.4M
Agentic CodingCRuns well33.6 tok/s8381 ms6.4M
ReasoningCRuns well33.6 tok/s6810 ms6.4M
RAGCRuns well33.6 tok/s10476 ms6.4M

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.9 GB
LowD38
Q3_K_S
3
1.2 GB
LowD38
NVFP4
4
1.3 GB
MediumD38
Q4_K_M
4
1.5 GB
MediumD38
Q5_K_M
5
1.7 GB
HighD38
Q6_K
6
2.0 GB
HighD38
Q8_0
8
2.6 GB
Very HighD38
F16Best for your GPU
16
4.9 GB
MaximumD38

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

Opciones de mejora

Hardware que ejecuta bien EXAONE 3.5 2.4B Instruct

Mac Studio M3 Ultra 256GBOpción económica
256 GB Unified (+128)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.33.6 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

Frequently asked questions

Can AMD Instinct MI250 128GB run EXAONE 3.5 2.4B Instruct?

Yes, AMD Instinct MI250 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 AMD Instinct MI250 128GB?

On AMD Instinct MI250 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 AMD Instinct MI250 128GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on AMD Instinct MI250 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 AMD Instinct MI250 128GB?

On AMD Instinct MI250 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 AMD Instinct MI250 128GBSee all hardware for EXAONE 3.5 2.4B Instruct
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