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URL: https://willitrunai.com/can-run/hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf-on-gtx-1660-ti-6gb

⇱ EXAONE 3.5 2.4B Instruct on GTX 1660 Ti 6GB? YES

Can EXAONE 3.5 2.4B Instruct run on GTX 1660 Ti 6GB?

YES — Runs Great

C51Usable
○Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~3.5 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Share:

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) — 3.5 GB, 33.6 tok/s, Runs well
3.5 GB required6.0 GB available
58% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5762 ms

Safe context

156K

Memory

3.5 GB / 6.0 GB

Memory breakdown

Weights1.5 GB
KV Cache0.3 GB
Runtime1.2 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on GTX 1660 Ti 6GB
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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well33.6 tok/s3143 ms156K
CodingCRuns well33.6 tok/s5762 ms156K
Agentic CodingCRuns well33.6 tok/s8381 ms156K
ReasoningCRuns well33.6 tok/s6810 ms156K
RAGCRuns well33.6 tok/s10476 ms156K

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.9 GB
LowC52
Q3_K_S
3
1.2 GB
LowC53
NVFP4
4
1.3 GB
MediumC53
Q4_K_M
4
1.5 GB
MediumC54
Q5_K_M
5
1.7 GB
HighC54
Q6_K
6
2.0 GB
HighC54
Q8_0Best for your GPU
8
2.6 GB
Very HighC54
F16
16
4.9 GB
MaximumF0

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

Frequently asked questions

See all results for GTX 1660 Ti 6GBSee all hardware for EXAONE 3.5 2.4B Instruct