willitrun·ai

Can EXAONE 3.5 2.4B Instruct run on RTX 3090 24GB?

YES — Runs Great

C43Usable
Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~5.3 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 5.3 GB, 33.6 tok/s, Runs well
5.3 GB required24.0 GB available
22% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5762 ms

Safe context

1.1M

Memory

5.3 GB / 24.0 GB

Memory breakdown

Weights1.5 GB
KV Cache0.3 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on RTX 3090 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: 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 ms1.1M
CodingCRuns well33.6 tok/s5762 ms1.1M
Agentic CodingCRuns well33.6 tok/s8381 ms1.1M
ReasoningCRuns well33.6 tok/s6810 ms1.1M
RAGCRuns well33.6 tok/s10476 ms1.1M

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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M45.6Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M38.4Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M38.4Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M33.6Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M33.6Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M33.6Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M33.6Fits
RX 7900 XTX 24GB
24 GBQ4_K_M33.6Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M33.6Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M33.6Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M33.6Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M33.6Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M33.6Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M33.6Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M33.6Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M33.6Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.9 GB
LowC43
Q3_K_S
3
1.2 GB
LowC44
NVFP4
4
1.3 GB
MediumC44
Q4_K_M
4
1.5 GB
MediumC44
Q5_K_M
5
1.7 GB
HighC44
Q6_K
6
2.0 GB
HighC44
Q8_0
8
2.6 GB
Very HighC44
F16Best for your GPU
16
4.9 GB
MaximumC45

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

升级选项

能流畅运行 EXAONE 3.5 2.4B Instruct 的硬件

MacBook Pro M3 Pro 36GB经济之选
36 GB Unified (+12)
C
This setup is broadly balanced for this model.33.6 tok/s 解码

~$1,999 MSRP

MacBook Pro M4 Max 36GB最佳性价比
36 GB Unified (+12)
C
This setup is broadly balanced for this model.33.6 tok/s 解码

~$2,499 MSRP

Frequently asked questions

Can RTX 3090 24GB run EXAONE 3.5 2.4B Instruct?

Yes, RTX 3090 24GB 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 5.3 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 RTX 3090 24GB?

On RTX 3090 24GB, 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 RTX 3090 24GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on RTX 3090 24GB receives a C grade with 33.6 tok/s and 1.1M context.

What context window can EXAONE 3.5 2.4B Instruct use on RTX 3090 24GB?

On RTX 3090 24GB, EXAONE 3.5 2.4B Instruct can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3090 24GBSee all hardware for EXAONE 3.5 2.4B Instruct
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