Can exaone 3.0 7.8b it run on RTX 3050 8GB?

Q: What should I upgrade first if exaone 3.0 7.8b it feels slow on RTX 3050 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 3.0 7.8b it needs ~7.7 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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, 31.1 tok/s, Runs with offload

7.7 GB required8.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

31.1 tok/s

TTFT

6231 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 3.0 7.8b it on RTX 3050 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: 31.1 tok/s decode · 6.2s TTFT (warm) · 78 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	31.1 tok/s	3399 ms	22K
Coding	C	Runs with offload	31.1 tok/s	6231 ms	22K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	20.1 tok/s	14023 ms	22K
Reasoning	C	Runs with offload	31.1 tok/s	7364 ms	22K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	20.1 tok/s	17529 ms

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on RTX 3050 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 3.0 7.8b it on your machine.

Run

lms load hf-bingsu--exaone-3-0-7-8b-it && lms server start

Upgrade options

Hardware that runs exaone 3.0 7.8b it well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+136)

Raises estimated decode speed by about 60%.49.9 tok/s decode

Raises estimated decode speed by about 60%.

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 (+224)

Raises estimated decode speed by about 88%.58.4 tok/s decode

Raises estimated decode speed by about 88%.

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 (+64)

Raises estimated decode speed by about 42%.44.2 tok/s decode

Raises estimated decode speed by about 42%.

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

~$499 MSRP

Frequently asked questions

Can RTX 3050 8GB run exaone 3.0 7.8b it?

Yes, RTX 3050 8GB can run exaone 3.0 7.8b it with a C grade (Runs with offload). Expected decode speed: 31.1 tok/s.

How much VRAM does exaone 3.0 7.8b it need?

exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.

What is the best quantization for exaone 3.0 7.8b it?

The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.

What speed will exaone 3.0 7.8b it run at on RTX 3050 8GB?

On RTX 3050 8GB, exaone 3.0 7.8b it achieves approximately 31.1 tokens per second decode speed with a time-to-first-token of 6231ms using Q4_K_M quantization.

Can RTX 3050 8GB run exaone 3.0 7.8b it for coding?

For coding workloads, exaone 3.0 7.8b it on RTX 3050 8GB receives a C grade with 31.1 tok/s and 22K context.

What context window can exaone 3.0 7.8b it use on RTX 3050 8GB?

On RTX 3050 8GB, exaone 3.0 7.8b it 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 3.0 7.8b it feels slow on RTX 3050 8GB?

See all results for RTX 3050 8GB See all hardware for exaone 3.0 7.8b it

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