Rent NVIDIA L4 GPUs for High-Efficiency AI Inference

L4 is optimized for high-throughput, low-latency inference and media tasks. Typical workloads include:

• AI inference for recommenders, chatbots and search
• AI-generated and AI-enhanced video pipelines
• Video transcoding and streaming with AV1 support
• Smart city and vision analytics (CCTV, retail, logistics)
• Edge AI deployments where power and space are limited
• Virtual workstations and graphics-intensive applications

These use cases take advantage of L4’s Tensor Cores, media engines and efficient power profile.

Choose L4 when your priority is efficient inference, media, and edge workloads, not heavy training:

• Pick L4 for production inference, video streaming, smart city analytics, vector DB queries and cost-sensitive GenAI workloads.
• Pick A100 / H100 / H200 when you need large-scale model training, multi-GPU data parallelism or very large LLMs.
• Pick L40S when you want a “do-it-all” GPU for heavy GenAI, high-end graphics and mixed workloads.

On AceCloud you can also mix L4 with other GPUs in the same environment as workloads evolve.

Each NVIDIA L4 GPU comes with 24 GB GDDR6 memory and about 300 GB/s of memory bandwidth. That is enough to:

• Serve mid-sized and quantized LLMs comfortably
• Run many concurrent inference requests per GPU
• Handle high-resolution video streams and multi-stream encoding

It is not meant for very large model training runs, but it’s excellent for serving and media at scale.

Yes, L4 is well-suited for LLM and GenAI inference, especially with FP8/FP16 and quantized models. It can power chatbots, assistants, retrieval-augmented generation (RAG) and image / video generation services efficiently. For large-scale training or very large models, AceCloud typically recommends A100, H100 or H200 instead.

L4 is designed for high performance per watt: it delivers strong AI and video throughput while drawing only around 72 W, so you can serve more requests or streams per server compared to CPU-only setups or heavier GPUs. That usually means:

• Lower power and cooling costs
• Fewer servers for the same throughput
• Lower GPU hourly rates than large training GPUs

On AceCloud, this makes L4 a cost-efficient option for always-on inference and media services.

Yes. L4’s low-profile, low-power design and media/AI acceleration make it ideal for edge servers in retail, factories, smart cities and telco environments. You get high throughput with tight power and space budgets, and when you deploy in regions close to your users, you can keep end-to-end inference latency low.

NVIDIA L4 includes dedicated NVENC/NVDEC engines and an optimized AV1 stack. It supports hardware-accelerated AV1, H.264 and H.265 encode/decode, which lets you run dense video streaming, live transcoding, conferencing and media processing pipelines efficiently without overloading CPUs.

You can use all mainstream AI and media tools, including:

• PyTorch, TensorFlow, JAX and ONNX Runtime
• NVIDIA CUDA, cuDNN, TensorRT and Triton Inference Server
• CV-CUDA and other vision/video SDKs
• FFmpeg with NVENC/NVDEC for media pipelines
• Docker, Kubernetes and AceCloud GPU clusters for orchestration

AceCloud provides L4 images that come pre-configured with common GPU stacks, or you can bring your own containers.

Yes. You can start with a single L4 instance and:

• Scale vertically by choosing larger vCPU/RAM flavors with one or more L4 GPUs
• Scale horizontally by adding more L4 nodes and using Kubernetes or other orchestrators to distribute traffic

AceCloud lets you spin instances up or down on demand, so you can match GPU capacity to traffic without long-term lock-in.

L4 pricing on AceCloud depends on your chosen configuration (vCPU, RAM, storage, region and billing term). You can view exact rates on the L4 pricing section of the page. New customers usually receive ₹20,000 (India) or $200 (US) in free credits to test L4 performance before committing.

Yes. L4 is widely used in production for finance, healthcare, media, surveillance and SaaS products that require consistent, low-latency inference. On AceCloud, L4 runs in secure, enterprise-grade data centers with network isolation, access controls, encrypted storage options and 24/7 support, so you can deploy business-critical services with confidence.

AceCloud’s L4 GPU instances deliver sub-millisecond latency, enabling real-time inference, video analytics, and interactive workloads without delay.

Yes, while not designed for large-scale training like H100 or A100, the L4 performs excellently for LLM inference with support for FP8/FP16, making it perfect for deploying quantized Transformer models.

Definitely. L4 instances are container-ready and fully compatible with Kubernetes, allowing scalable deployment with orchestration tools.