Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on RTX 3070 Ti 8GB?

YES — With Offload

C54Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~7.7 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~92 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Balanced
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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, 92.0 tok/s, Runs with offload
7.7 GB required8.0 GB available
96% VRAM used

Fit status

Runs with offload

Decode

92.0 tok/s

TTFT

2104 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 3070 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: 92.0 tok/s decode · 2.1s TTFT (warm) · 230 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.

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 fit92.0 tok/s1148 ms22K
CodingCRuns with offload92.0 tok/s2104 ms22K
Agentic CodingCRuns with offload (needs ~0.3 GB host RAM)59.5 tok/s4736 ms22K
ReasoningCRuns with offload92.0 tok/s2487 ms22K
RAGCRuns with offload (needs ~0.3 GB host RAM)59.5 tok/s5920 ms22K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 3070 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)
C
Adds memory headroom for longer context windows and future model growth.49.9 tok/s 解码

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

~$329 MSRP

NVIDIARTX 5070 12GB最佳性价比
12 GB VRAM (+4)672 GB/s (+64)
B
Adds memory headroom for longer context windows and future model growth.89 tok/s 解码

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

~$549 MSRP

NVIDIARTX 4070 Super 12GBNVIDIA 升级
12 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.81.6 tok/s 解码

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

~$599 MSRP

Frequently asked questions

Can RTX 3070 Ti 8GB run EXAONE 3.5 7.8B Instruct?

Yes, RTX 3070 Ti 8GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs with offload). Expected decode speed: 92.0 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 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 3070 Ti 8GB?

On RTX 3070 Ti 8GB, EXAONE 3.5 7.8B Instruct achieves approximately 92.0 tokens per second decode speed with a time-to-first-token of 2104ms using Q4_K_M quantization.

Can RTX 3070 Ti 8GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on RTX 3070 Ti 8GB receives a C grade with 92.0 tok/s and 22K context.

What context window can EXAONE 3.5 7.8B Instruct use on RTX 3070 Ti 8GB?

On RTX 3070 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 RTX 3070 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 RTX 3070 Ti 8GBSee all hardware for EXAONE 3.5 7.8B Instruct
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