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URL: https://willitrunai.com/can-run/hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf-on-rtx-3060-12gb

⇱ EXAONE 3.5 7.8B Instruct on RTX 3060 12GB? YES

Can EXAONE 3.5 7.8B Instruct run on RTX 3060 12GB?

YES — Runs Great

C54Usable
○Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.1 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~50 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) — 8.1 GB, 49.9 tok/s, Runs well
8.1 GB required12.0 GB available
68% VRAM used

Fit status

Runs well

Decode

49.9 tok/s

TTFT

3877 ms

Safe context

85K

Memory

8.1 GB / 12.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on RTX 3060 12GB
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: 49.9 tok/s decode · 3.9s TTFT (warm) · 125 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 well49.9 tok/s2115 ms85K
CodingCRuns well49.9 tok/s3877 ms85K
Agentic CodingCRuns well49.9 tok/s5639 ms85K
ReasoningCRuns well49.9 tok/s4582 ms85K
RAGCRuns well49.9 tok/s7049 ms85K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC49
Q3_K_S
3
3.8 GB
LowC50
NVFP4
4
4.4 GB
MediumC51
Q4_K_M
4
4.8 GB
MediumC51
Q5_K_M
5
5.6 GB
HighC52
Q6_K
6
6.4 GB
HighC52
Q8_0Best for your GPU
8
8.3 GB
Very HighC51
F16
16
16.0 GB
MaximumF0

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.

Run

lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server start

Frequently asked questions

See all results for RTX 3060 12GBSee all hardware for EXAONE 3.5 7.8B Instruct