Can EXAONE 3.5 2.4B Instruct run on GTX 1080 Ti 11GB?

YES — Runs Great

C46Usable

Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~4.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 4.0 GB, 33.6 tok/s, Runs well

4.0 GB required11.0 GB available

36% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5762 ms

Safe context

412K

Memory

4.0 GB / 11.0 GB

Memory breakdown

Weights1.5 GB

KV Cache0.3 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on GTX 1080 Ti 11GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	33.6 tok/s	3143 ms	412K
Coding	C	Runs well	33.6 tok/s	5762 ms	412K
Agentic Coding	C	Runs well	33.6 tok/s	8381 ms	412K
Reasoning	C	Runs well	33.6 tok/s	6810 ms	412K
RAG	C	Runs well	33.6 tok/s	10476 ms	412K

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.9 GB	Low	C47
Q3_K_S	3	1.2 GB	Low	C48
NVFP4	4	1.3 GB	Medium	C48
Q4_K_M	4	1.5 GB	Medium	C48
Q5_K_M	5	1.7 GB	High	C48
Q6_K	6	2.0 GB	High	C49
Q8_0	8	2.6 GB	Very High	C49
F16Best for your GPU	16	4.9 GB	Maximum	C53

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

Upgrade options

Hardware that runs EXAONE 3.5 2.4B Instruct well

MacBook Pro M4 16GBBudget pick

16 GB Unified (+5)

This setup is broadly balanced for this model.33.6 tok/s decode

~$599 MSRP

MacBook Air M1 16GBBest value

16 GB Unified (+5)

This setup is broadly balanced for this model.27.9 tok/s decode

~$999 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run EXAONE 3.5 2.4B Instruct?

Yes, GTX 1080 Ti 11GB 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 4.0 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, 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 GTX 1080 Ti 11GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on GTX 1080 Ti 11GB receives a C grade with 33.6 tok/s and 412K context.

What context window can EXAONE 3.5 2.4B Instruct use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, EXAONE 3.5 2.4B Instruct can safely use up to 412K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for EXAONE 3.5 2.4B Instruct

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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

33.6 tok/s

3143 ms

412K

Coding

Runs well

33.6 tok/s

5762 ms

412K

Agentic Coding

Runs well

33.6 tok/s

8381 ms

412K

Reasoning

Runs well

33.6 tok/s

6810 ms

412K

RAG

Runs well

33.6 tok/s

10476 ms

412K

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.9 GB	Low	C47
Q3_K_S	3	1.2 GB	Low	C48
NVFP4	4	1.3 GB	Medium	C48
Q4_K_M	4	1.5 GB	Medium	C48
Q5_K_M	5	1.7 GB	High	C48
Q6_K	6	2.0 GB	High	C49
Q8_0	8	2.6 GB	Very High	C49
F16Best for your GPU	16	4.9 GB	Maximum	C53

Frequently asked questions

Can GTX 1080 Ti 11GB run EXAONE 3.5 2.4B Instruct?

Yes, GTX 1080 Ti 11GB 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 4.0 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, 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 GTX 1080 Ti 11GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on GTX 1080 Ti 11GB receives a C grade with 33.6 tok/s and 412K context.

What context window can EXAONE 3.5 2.4B Instruct use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, EXAONE 3.5 2.4B Instruct can safely use up to 412K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.