Can EXAONE 3.5 7.8B Instruct i1 run on GTX 1070 8GB?

Q: What should I upgrade first if EXAONE 3.5 7.8B Instruct i1 feels slow on GTX 1070 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

C50Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~7.7 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~32 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, 31.7 tok/s, Runs with offload

7.7 GB required8.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

31.7 tok/s

TTFT

6099 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 i1 on GTX 1070 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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 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	31.7 tok/s	3327 ms	22K
Coding	C	Runs with offload	31.7 tok/s	6099 ms	22K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	19.8 tok/s	14204 ms	22K
Reasoning	C	Runs with offload	31.7 tok/s	7208 ms	22K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	19.8 tok/s	17756 ms

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on GTX 1070 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 i1 on your machine.

Run

lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server start

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct i1 well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+104)

Raises estimated decode speed by about 57%.49.9 tok/s decode

Raises estimated decode speed by about 57%.

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+192)

Raises estimated decode speed by about 84%.58.4 tok/s decode

Raises estimated decode speed by about 84%.

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

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+32)

Raises estimated decode speed by about 39%.44.2 tok/s decode

Raises estimated decode speed by about 39%.

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

~$499 MSRP

Frequently asked questions

Can GTX 1070 8GB run EXAONE 3.5 7.8B Instruct i1?

Yes, GTX 1070 8GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs with offload). Expected decode speed: 31.7 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct i1 need?

EXAONE 3.5 7.8B Instruct i1 (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 i1?

The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct i1 run at on GTX 1070 8GB?

On GTX 1070 8GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6099ms using Q4_K_M quantization.

Can GTX 1070 8GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on GTX 1070 8GB receives a C grade with 31.7 tok/s and 22K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on GTX 1070 8GB?

On GTX 1070 8GB, EXAONE 3.5 7.8B Instruct i1 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 GTX 1070 8GB See all hardware for EXAONE 3.5 7.8B Instruct i1

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