Can EXAONE 3.5 7.8B Instruct run on RTX 2070 8GB?

Q: What should I upgrade first if EXAONE 3.5 7.8B Instruct feels slow on RTX 2070 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C52Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~7.7 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 7.7 GB, 56.5 tok/s, Runs with offload

7.7 GB required8.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

56.5 tok/s

TTFT

3427 ms

Safe context

22K

Memory

7.7 GB / 8.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on RTX 2070 8GB
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: 56.5 tok/s decode · 3.4s TTFT (warm) · 141 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	56.5 tok/s	1869 ms	22K
Coding	C	Runs with offload	56.5 tok/s	3427 ms	22K
Agentic Coding	C	Runs with offload	35.3 tok/s	7982 ms	22K
Reasoning	C	Runs with offload	56.5 tok/s	4050 ms	22K
RAG	C	Runs with offload (needs ~0.3 GB host RAM)	35.3 tok/s	9977 ms	22K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C54
Q3_K_S	3	3.8 GB	Low	C53
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

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.49.9 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 58%.89 tok/s decode

Raises estimated decode speed by about 58%.

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

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 44%.81.6 tok/s decode

Raises estimated decode speed by about 44%.

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

~$599 MSRP

Frequently asked questions

Can RTX 2070 8GB run EXAONE 3.5 7.8B Instruct?

Yes, RTX 2070 8GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs with offload). Expected decode speed: 56.5 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.7 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 2070 8GB?

On RTX 2070 8GB, EXAONE 3.5 7.8B Instruct achieves approximately 56.5 tokens per second decode speed with a time-to-first-token of 3427ms using Q4_K_M quantization.

Can RTX 2070 8GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on RTX 2070 8GB receives a C grade with 56.5 tok/s and 22K context.

What context window can EXAONE 3.5 7.8B Instruct use on RTX 2070 8GB?

On RTX 2070 8GB, EXAONE 3.5 7.8B Instruct can safely use up to 22K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2070 8GB See all hardware for EXAONE 3.5 7.8B Instruct

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