Can EXAONE 4.0 1.2B run on RTX 5050 8GB?

YES — Runs Great

C44Usable

Estimated from fit model

EXAONE 4.0 1.2B needs ~2.6 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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) — 2.6 GB, 22.8 tok/s, Runs well

2.6 GB required8.0 GB available

33% VRAM used

Fit status

Runs well

Decode

22.8 tok/s

TTFT

8491 ms

Safe context

634K

Memory

2.6 GB / 8.0 GB

Memory breakdown

Weights0.7 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 1.2B on RTX 5050 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: 22.8 tok/s decode · 8.5s TTFT (warm) · 57 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	22.8 tok/s	4632 ms	445K
Coding	C	Runs well	22.8 tok/s	8491 ms	634K
Agentic Coding	C	Runs well	22.8 tok/s	12351 ms	634K
Reasoning	C	Runs well	22.8 tok/s	10035 ms	634K
RAG	C	Runs well	22.8 tok/s	15439 ms	634K

Quantization options

How EXAONE 4.0 1.2B (1.2000000476837158B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.5 GB	Low	C49
Q3_K_S	3	0.6 GB	Low	C49
NVFP4	4	0.7 GB	Medium	C49
Q4_K_M	4	0.7 GB	Medium	C49
Q5_K_M	5	0.9 GB	High	C50
Q6_K	6	1.0 GB	High	C50
Q8_0	8	1.3 GB	Very High	C50
F16Best for your GPU	16	2.5 GB	Maximum	C53

Get started

Copy-paste commands to run EXAONE 4.0 1.2B on your machine.

Run

lms load hf-lgai-exaone--exaone-4-0-1-2b-gguf && lms server start

Frequently asked questions

Can RTX 5050 8GB run EXAONE 4.0 1.2B?

Yes, RTX 5050 8GB can run EXAONE 4.0 1.2B with a C grade (Runs well). Expected decode speed: 22.8 tok/s.

How much VRAM does EXAONE 4.0 1.2B need?

EXAONE 4.0 1.2B (1.2000000476837158B parameters) requires approximately 2.6 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 4.0 1.2B?

The recommended quantization for EXAONE 4.0 1.2B is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 4.0 1.2B run at on RTX 5050 8GB?

On RTX 5050 8GB, EXAONE 4.0 1.2B achieves approximately 22.8 tokens per second decode speed with a time-to-first-token of 8491ms using Q4_K_M quantization.

Can RTX 5050 8GB run EXAONE 4.0 1.2B for coding?

For coding workloads, EXAONE 4.0 1.2B on RTX 5050 8GB receives a C grade with 22.8 tok/s and 634K context.

What context window can EXAONE 4.0 1.2B use on RTX 5050 8GB?

On RTX 5050 8GB, EXAONE 4.0 1.2B can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5050 8GB See all hardware for EXAONE 4.0 1.2B

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<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-4-0-1-2b-gguf-on-rtx-5050-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

22.8 tok/s

4632 ms

445K

Coding

Runs well

22.8 tok/s

8491 ms

634K

Agentic Coding

Runs well

22.8 tok/s

12351 ms

634K

Reasoning

Runs well

22.8 tok/s

10035 ms

634K

RAG

Runs well

22.8 tok/s

15439 ms

634K

Quantization options

How EXAONE 4.0 1.2B (1.2000000476837158B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.5 GB	Low	C49
Q3_K_S	3	0.6 GB	Low	C49
NVFP4	4	0.7 GB	Medium	C49
Q4_K_M	4	0.7 GB	Medium	C49
Q5_K_M	5	0.9 GB	High	C50
Q6_K	6	1.0 GB	High	C50
Q8_0	8	1.3 GB	Very High	C50
F16Best for your GPU	16	2.5 GB	Maximum	C53

Frequently asked questions

Can RTX 5050 8GB run EXAONE 4.0 1.2B?

Yes, RTX 5050 8GB can run EXAONE 4.0 1.2B with a C grade (Runs well). Expected decode speed: 22.8 tok/s.

How much VRAM does EXAONE 4.0 1.2B need?

EXAONE 4.0 1.2B (1.2000000476837158B parameters) requires approximately 2.6 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 4.0 1.2B?

The recommended quantization for EXAONE 4.0 1.2B is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 4.0 1.2B run at on RTX 5050 8GB?

On RTX 5050 8GB, EXAONE 4.0 1.2B achieves approximately 22.8 tokens per second decode speed with a time-to-first-token of 8491ms using Q4_K_M quantization.

Can RTX 5050 8GB run EXAONE 4.0 1.2B for coding?

For coding workloads, EXAONE 4.0 1.2B on RTX 5050 8GB receives a C grade with 22.8 tok/s and 634K context.

What context window can EXAONE 4.0 1.2B use on RTX 5050 8GB?

On RTX 5050 8GB, EXAONE 4.0 1.2B can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.