Is the L4 fast enough for LLM chat?

For 7B–14B models, yes. Its 221 GB/s memory bandwidth limits tokens per second compared with an RTX 4090 or A100, so it fits light concurrency and batch work better than high-traffic serving.

L4 vs RTX 4090: which should I rent?

The RTX 4090 is listed with 24 GB, 81.4 TFLOPS and 879 GB/s, so it is much faster. The L4 is the datacenter, low-power (72 W) option with 24 GB, good when throughput needs are modest.

Can the L4 run Stable Diffusion and Flux?

Yes. SD 1.5 and SDXL run well, and Flux runs in FP8 or quantized GGUF form. Expect slower images than on high-end Ada cards, since FP32 is about 24.2 TFLOPS.

How does Cloudj bill an L4 server?

Per minute from your account balance while it runs, at the price locked when you deploy, plus bandwidth per GB. A stopped instance is charged for storage only. There are no monthly GPU plans.

GPU Cloud · NVIDIA · Ada Lovelace

Rent an NVIDIA L4 GPU with 24 GB VRAM

Name: NVIDIA L4 cloud GPU
Brand: Cloudj
Availability: InStock

Rent L4 cloud GPU servers by the hour on Cloudj: 24 GB VRAM, Ada Lovelace architecture and about 24.2 TFLOPS FP32 at just a 72 W power limit. It suits developers running efficient inference, transcription, embeddings and video pipelines who want more memory than an 8–16 GB card without paying for a flagship. Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.

Rent L4 All GPUs

L4 specifications

VRAM per GPU

24 GB

Architecture

NVIDIA Ada Lovelace

Compute capability

8.9

FP32 compute

≈ 24.2 TFLOPS

Memory bandwidth

≈ 221 GB/s

Power limit

72 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Live L4 configurations and prices

From $0.42/h

incl. 32 GB disk · Live price, updated every 15 minutes

Deploy

What you can run on the L4 cloud GPU

7B–8B LLMs in 16-bit

24 GB holds an 8B model in 16-bit or a quantized 13B–14B model for chatbots, agents and internal APIs.

Speech-to-text and embeddings

Run Whisper-style transcription, rerankers and embedding models around the clock on a low-power card.

SDXL image generation

ComfyUI with SDXL and Flux in FP8 or GGUF form fits comfortably in 24 GB.

Always-on inference endpoints

A 72 W power limit makes the L4 a sensible card for small services that need to stay up rather than burst.

Is the L4 the right GPU for your workload?

Choose the L4 when you need 24 GB of VRAM and Ada Lovelace compute capability 8.9 for inference or moderate training workloads. Move to a larger datacenter GPU if your models exceed 24 GB or require higher than about 221 GB/s of memory bandwidth, or pick a smaller card for basic development tasks that need less memory.

L4 vs similar cloud GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA L4	24 GB	≈ 24.2 TFLOPS	72 W
NVIDIA Tesla P40	24 GB	≈ 9.4 TFLOPS	250 W	Details →
NVIDIA Tesla V100	16 GB	≈ 12.5 TFLOPS	300 W	Details →
NVIDIA A100 PCIE	40 GB	≈ 15.6 TFLOPS	250 W	Details →
NVIDIA A100 SXM4	40 GB	≈ 15.6 TFLOPS	400 W	Details →

Guides for L4

Low latency AI inference GPU: does location really matter?Low latency AI inference GPU choices explained: split delay into network round trips, time to first token and generation, then see when location dominates.NVIDIA L4 cloud: a low-power inference GPU close to your usersWhen NVIDIA L4 cloud instances make sense: 24 GB class inference and video AI, searching near users in SG, JP, DE or US, and how L4 compares with RTX cards.Embedding model GPU server EU: a RAG architecture guidePlan an embedding model GPU server EU setup for RAG: split embeddings, reranker and LLM, keep the vector database on a CPU VPS and filter GPUs by EU countries.TTS GPU server in Asia: latency budgets for voice appsPlan a TTS GPU server for Asian voice apps: build a streaming latency budget, size a small GPU, search SG, HK and JP, and keep the audio pipeline near users.

All articles →

L4 cloud GPU: frequently asked questions

Rent the L4 cloud GPU by the hour

Pick a live configuration, choose a template and connect over SSH with your key. Billing stops when you destroy the instance.

Rent L4 All GPUs