Can EXAONE 3.5 7.8B Instruct run on NVIDIA L4 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~9.3 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 9.3 GB, 41.0 tok/s, Runs well

9.3 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

41.0 tok/s

TTFT

4724 ms

Safe context

274K

Memory

9.3 GB / 24.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on NVIDIA L4 24GB
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: 41.0 tok/s decode · 4.7s TTFT (warm) · 103 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	41.0 tok/s	2577 ms	274K
Coding	C	Runs well	41.0 tok/s	4724 ms	274K
Agentic Coding	C	Runs well	41.0 tok/s	6871 ms	274K
Reasoning	C	Runs well	41.0 tok/s	5583 ms	274K
RAG	C	Runs well	41.0 tok/s	8589 ms	274K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C44
Q3_K_S	3	3.8 GB	Low	C45
NVFP4	4

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

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 166%.109.2 tok/s decode

Raises estimated decode speed by about 166%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 166%.109.2 tok/s decode

Raises estimated decode speed by about 166%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 136%.96.8 tok/s decode

Raises estimated decode speed by about 136%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run EXAONE 3.5 7.8B Instruct?

Yes, NVIDIA L4 24GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 41.0 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 9.3 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, EXAONE 3.5 7.8B Instruct achieves approximately 41.0 tokens per second decode speed with a time-to-first-token of 4724ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on NVIDIA L4 24GB receives a C grade with 41.0 tok/s and 274K context.

What context window can EXAONE 3.5 7.8B Instruct use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, EXAONE 3.5 7.8B Instruct can safely use up to 274K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for EXAONE 3.5 7.8B Instruct

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