Will It Run AI

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

YES — With Offload

C50Usable
Estimated from fit model

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

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced
Share:

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 on GTX 1070 Ti 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit31.7 tok/s3327 ms22K
CodingCRuns with offload31.7 tok/s6099 ms22K
Agentic CodingDRuns with offload (needs ~0.3 GB host RAM)19.8 tok/s14204 ms22K
ReasoningCRuns with offload31.7 tok/s7208 ms22K
RAGDRuns with offload (needs ~0.3 GB host RAM)19.8 tok/s17756 ms22K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC54
Q3_K_S
3
3.8 GB
LowC53
NVFP4
4
4.4 GB
MediumC53
Q4_K_MBest for your GPU
4
4.8 GB
MediumC53
Q5_K_M
5
5.6 GB
HighF0
Q6_K
6
6.4 GB
HighF0
Q8_0
8
8.3 GB
Very HighF0
F16
16
16.0 GB
MaximumF0

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

升级选项

能流畅运行 EXAONE 3.5 7.8B Instruct 的硬件

NVIDIARTX 3060 12GB经济之选
12 GB VRAM (+4)360 GB/s (+104)
C
Raises estimated decode speed by about 57%.49.9 tok/s 解码

Raises estimated decode speed by about 57%.

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

~$329 MSRP

NVIDIARTX 5060 Ti 16GB最佳性价比
16 GB VRAM (+8)448 GB/s (+192)
C
Raises estimated decode speed by about 84%.58.4 tok/s 解码

Raises estimated decode speed by about 84%.

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

~$449 MSRP

NVIDIARTX 4060 Ti 16GBNVIDIA 升级
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 39%.44.2 tok/s 解码

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 Ti 8GB run EXAONE 3.5 7.8B Instruct?

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

On GTX 1070 Ti 8GB, EXAONE 3.5 7.8B Instruct 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 Ti 8GB run EXAONE 3.5 7.8B Instruct for coding?

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

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

On GTX 1070 Ti 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.

What should I upgrade first if EXAONE 3.5 7.8B Instruct feels slow on GTX 1070 Ti 8GB?

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

See all results for GTX 1070 Ti 8GBSee all hardware for EXAONE 3.5 7.8B Instruct
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