Can EXAONE 3.5 7.8B Instruct run on GTX 1080 Ti 11GB?

YES — Runs Great

B55Good

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~60 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) — 8.0 GB, 60.0 tok/s, Runs well

8.0 GB required11.0 GB available

73% VRAM used

Fit status

Runs well

Decode

60.0 tok/s

TTFT

3226 ms

Safe context

69K

Memory

8.0 GB / 11.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B 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: 60.0 tok/s decode · 3.2s TTFT (warm) · 150 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	60.0 tok/s	1760 ms	69K
Coding	B	Runs well	60.0 tok/s	3226 ms	69K
Agentic Coding	B	Runs well	60.0 tok/s	4692 ms	69K
Reasoning	B	Runs well	60.0 tok/s	3812 ms	69K
RAG	B	Runs well	60.0 tok/s	5865 ms	69K

Inference speed

EXAONE 3.5 7.8B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 3.5 7.8B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~109 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	109.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	109.2	Fits
RTX 3090 24GB	24 GB	Q4_K_M	109.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	109.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	97.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	92.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	79.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	78.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	78.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	50.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	49.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	46.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	41.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.6	Fits

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 7.8B Instruct (7.800000190734863B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C50
Q3_K_S	3	3.8 GB	Low	C51
NVFP4	4	4.4 GB	Medium	C52
Q4_K_M	4	4.8 GB	Medium	C53
Q5_K_M	5	5.6 GB	High	C52
Q6_KBest for your GPU	6	6.4 GB	High	C52
Q8_0	8	8.3 GB	Very High	F0
F16	16	16.0 GB	Maximum	F0

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

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct well

RTX 5070 12GBBudget pick

12 GB VRAM (+1)672 GB/s (+188)

Raises estimated decode speed by about 48%.89 tok/s decode

Raises estimated decode speed by about 48%.

~$549 MSRP

RTX 4070 Super 12GBBest value

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 36%.81.6 tok/s decode

Raises estimated decode speed by about 36%.

~$599 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 32%.79.4 tok/s decode

Raises estimated decode speed by about 32%.

~$599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run EXAONE 3.5 7.8B Instruct?

Yes, GTX 1080 Ti 11GB can run EXAONE 3.5 7.8B Instruct with a B grade (Runs well). Expected decode speed: 60.0 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.0 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, EXAONE 3.5 7.8B Instruct achieves approximately 60.0 tokens per second decode speed with a time-to-first-token of 3226ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on GTX 1080 Ti 11GB receives a B grade with 60.0 tok/s and 69K context.

What context window can EXAONE 3.5 7.8B Instruct use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, EXAONE 3.5 7.8B Instruct can safely use up to 69K 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 7.8B Instruct

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--exaone-3-5-7-8b-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

60.0 tok/s

1760 ms

69K

Coding

Runs well

60.0 tok/s

3226 ms

69K

Agentic Coding

Runs well

60.0 tok/s

4692 ms

69K

Reasoning

Runs well

60.0 tok/s

3812 ms

69K

RAG

Runs well

60.0 tok/s

5865 ms

69K

EXAONE 3.5 7.8B Instruct inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

109.2

Fits

RTX 4090 24GB

24 GB

Q4_K_M

109.2

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

109.2

Fits

RTX 3090 24GB

24 GB

Q4_K_M

109.2

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

109.2

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

109.2

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

97.5

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

92.5

Fits

RTX 4070 12GB

12 GB

Q4_K_M

79.4

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

78.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

78.8

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

50.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

49.9

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

46.2

Fits

RTX 4060 8GB

8 GB

Q4_K_M

41.7

Offloads

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

40.6

Fits

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C50
Q3_K_S	3	3.8 GB	Low	C51
NVFP4	4	4.4 GB	Medium	C52
Q4_K_M	4	4.8 GB	Medium	C53
Q5_K_M	5	5.6 GB	High	C52
Q6_KBest for your GPU	6	6.4 GB	High	C52
Q8_0	8	8.3 GB	Very High	F0
F16	16	16.0 GB	Maximum	F0

Frequently asked questions

Can GTX 1080 Ti 11GB run EXAONE 3.5 7.8B Instruct?

Yes, GTX 1080 Ti 11GB can run EXAONE 3.5 7.8B Instruct with a B grade (Runs well). Expected decode speed: 60.0 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.0 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, EXAONE 3.5 7.8B Instruct achieves approximately 60.0 tokens per second decode speed with a time-to-first-token of 3226ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on GTX 1080 Ti 11GB receives a B grade with 60.0 tok/s and 69K context.

What context window can EXAONE 3.5 7.8B Instruct use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, EXAONE 3.5 7.8B Instruct can safely use up to 69K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.