Can EXAONE 3.5 7.8B Instruct run on RTX 4060 Ti 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.5 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 8.5 GB, 44.2 tok/s, Runs well

8.5 GB required16.0 GB available

53% VRAM used

Fit status

Runs well

Decode

44.2 tok/s

TTFT

4381 ms

Safe context

148K

Memory

8.5 GB / 16.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on RTX 4060 Ti 16GB
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: 44.2 tok/s decode · 4.4s TTFT (warm) · 111 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	44.2 tok/s	2390 ms	148K
Coding	C	Runs well	44.2 tok/s	4381 ms	148K
Agentic Coding	C	Runs well	44.2 tok/s	6373 ms	148K
Reasoning	C	Runs well	44.2 tok/s	5178 ms	148K
RAG	C	Runs well	44.2 tok/s	7966 ms	148K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C47
Q3_K_S	3	3.8 GB	Low	C47
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 128%.100.9 tok/s decode

Raises estimated decode speed by about 128%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+352)

Raises estimated decode speed by about 137%.104.9 tok/s decode

Raises estimated decode speed by about 137%.

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

~$2,000 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run EXAONE 3.5 7.8B Instruct?

Yes, RTX 4060 Ti 16GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 44.2 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct?

The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct run at on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, EXAONE 3.5 7.8B Instruct achieves approximately 44.2 tokens per second decode speed with a time-to-first-token of 4381ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on RTX 4060 Ti 16GB receives a C grade with 44.2 tok/s and 148K context.

What context window can EXAONE 3.5 7.8B Instruct use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, EXAONE 3.5 7.8B Instruct can safely use up to 148K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GB See all hardware for EXAONE 3.5 7.8B Instruct

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