NVIDIA L4 Tensor Core Cloud GPU – high performance, energy efficient
NVIDIA L4 on HiTechCloud – a Tensor Core cloud GPU tuned for AI inference, video processing, computer vision and cloud graphics. A dedicated GPU option that balances performance, cost and scalability for business use.
NVIDIA L4 Cloud GPU for AI inference, video processing and energy-efficient workloads
NVIDIA L4 on HiTechCloud provides dedicated GPUs with 24 GB of VRAM each, suited to businesses running AI inference, video processing, computer vision and other cost-optimized cloud GPU applications.
NVIDIA L4 Tensor Core GPU
Energy-efficient cloud GPUs for AI inference, video processing, cloud graphics and enterprise workloads.
24 GB VRAM/GPU
VRAM capacity suited to inference, media pipelines, computer vision, data processing and cost-efficient GPU applications.
Scale to 8 GPUs
Configurations from 1 to 8 GPUs with up to 192 GB of total VRAM for flexible deployment.
Performance per watt
The L4 suits workloads that need a balance of GPU performance, running cost and scalability.
Choose an L4 configuration for AI inference, media and cloud GPU workloads
| Service plans | Price/month | GPU | Core | RAM | VRAM | P2P | Actions |
|---|---|---|---|---|---|---|---|
| L4 x1 | 17,496,000 VND/month | 1 GPU | 8 Core | 48 GB RAM | 24 GB VRAM | P2P: No | Sign up |
| L4 x2 | 33,436,800 VND/month | 2 GPU | 16 Core | 96 GB RAM | 48 GB VRAM | P2P: No | Sign up |
| L4 x4 | 65,512,800 VND/month | 4 GPU | 32 Core | 192 GB RAM | 96 GB VRAM | P2P: No | Sign up |
| L4 x8 | 129,664,800 VND/month | 8 GPU | 64 Core | 384 GB RAM | 192 GB VRAM | P2P: No | Sign up |
| Service plans | GPU | Core | RAM | VRAM | P2P | Disk | Type | Notes | Price/month | Actions |
|---|---|---|---|---|---|---|---|---|---|---|
| L4 x1 | 1 GPU | 8 Core | 48 GB RAM | 24 GB VRAM | P2P: No | 512 GB Up to | Dedicated | NVIDIA L4 Tensor Core Cloud GPU | 17,496,000 VND/month | Sign up |
| L4 x2 | 2 GPU | 16 Core | 96 GB RAM | 48 GB VRAM | P2P: No | 512 GB Up to | Dedicated | NVIDIA L4 Tensor Core Cloud GPU | 33,436,800 VND/month | Sign up |
| L4 x4 | 4 GPU | 32 Core | 192 GB RAM | 96 GB VRAM | P2P: No | 512 GB Up to | Dedicated | NVIDIA L4 Tensor Core Cloud GPU | 65,512,800 VND/month | Sign up |
| L4 x8 | 8 GPU | 64 Core | 384 GB RAM | 192 GB VRAM | P2P: No | 512 GB Up to | Dedicated | NVIDIA L4 Tensor Core Cloud GPU | 129,664,800 VND/month | Sign up |
Applies to all plans
- NVIDIA L4
- 512 GB Up to
- Dedicated
Flexible for AI inference, video processing and energy-efficient cloud GPU
The NVIDIA L4 lets businesses deploy Cloud GPU quickly, balancing performance, cost and scalability.
Optimized for AI inference
Suited to deploying AI inference models, computer vision, OCR, NLP and production AI services.
Energy efficient
An effective choice for businesses that need a stable, reasonably priced cloud GPU for the long term.
Video and media processing
Optimized for encode, decode, streaming, video analysis and GPU media processing pipelines.
Dedicated GPU
Dedicated resources keep workloads consistent, make performance easier to control, and suit production environments.
A range of billing cycles
Terms from 1 to 60 months are available, making cost planning straightforward.
Technical support
HiTechCloud advises on drivers, CUDA, AI frameworks, media stacks and the right configuration for your workload.
Use cases suited to NVIDIA L4
Run inference, OCR, image recognition, video analytics and other AI services that need a stable GPU.
Suited to encode/decode, media processing, video stream analysis and digital content pipeline optimization.
Use a dedicated NVIDIA L4 flexibly and cost-effectively, with no physical GPU server to buy.
High-performance GPU instances for a wide range of workloads
Powerful capacity, optimized to accelerate AI/ML and high-performance workloads at any scale.
A broad range of infrastructure options
Training, inference, or fine-tuning - HiTechCloud offers a range of GPUs to match your needs, with transparent pricing and on-demand deployment environments.
Built to NVIDIA reference architecture
HiTechCloud GPU instances combine NVLink/PCIe, InfiniBand (RDMA), and RAIL topology to optimize AI/HPC performance.
Three core connectivity layers for high-performance GPU clusters
A network and GPU fabric designed so AI workloads scale reliably, easing bandwidth bottlenecks and holding performance at scale.
NVLink / PCIe Switch
High-speed GPU-to-GPU connectivity within and between nodes, reducing bottlenecks during model training.
InfiniBand (RDMA)
Low-latency connectivity optimized for distributed training and reduced processing load on the host.
RAIL Topology
A parallel network architecture delivering higher bandwidth, redundancy, and consistent performance at any scale.
GPU auto scaling and utilization optimization
From a single GPU to large clusters, HiTechCloud provisions resources ahead of demand and gets the most out of every instance.
Auto scaling with Kubernetes
Automatically scale GPU resources from small scale to thousands of GPUs, with forecasting and Provisioning capability before you need it.
Security for every access connection
Access over SSH, TCP, and HTTP with built-in protection layers that keep enterprise data safe and access fully controlled.
Optimize GPU utilization with MIG
Partition a single GPU into multiple independent instances to run AI workloads in parallel, improving utilization and lowering infrastructure cost.
Transparent pricing, no hidden fees
From large-scale Training to real-time inference — pay only when you actually run, with a GPU Cloud platform built to serve every AI workload and high performance requirement.
Ready to run your first GPU instance?
From register to GPU instance launch in under 5 minutes — no complex setup, no resource reservation, no idle fees. Just deploy, run, and pay for what you use.
More than just Compute, you can manage, scale and develop entirely within a simple cloud ecosystem.
AgentBase
A complete management platform for deploying and operating AI agents securely at scale on enterprise-grade infrastructure.
Explore AgentBaseAI Platform
A unified platform for training, fine-tuning, and deploying AI models at any scale.
Explore AI PlatformVector Database
Supports fast search, real-time analytics, log and large-scale event data, and vector databases for RAG.
Explore Vector DatabaseKubernetes
Managed Kubernetes for container orchestration, AI services, and GPU cloud workloads.
Explore KubernetesScale with confidence across Southeast Asia
Deploy systems and applications closer to your users, reducing latency and meeting local regulatory requirements.
Bangkok
BKK-01
Ho Chi Minh
HCM-01 · HCM-02 · HCM-03
Ha Noi
HAN-01 · HAN-02
A partner for your digital transformation journey
Large enterprises and fast-growing startups choose HiTechCloud for secure, high-performance AI cloud solutions that let them innovate and scale.
Does your enterprise have a specific challenge? HiTechCloud is ready to support.
The HiTechCloud team advises on GPU architecture, networking, security, and an operating model matched to your real workloads.
FAQ
Quick facts before choosing an NVIDIA L4 configuration at HiTechCloud.
Which workloads is the NVIDIA L4 suited for?
Suited to AI inference, computer vision, video processing, streaming, media handling, and cloud GPU applications that need power efficiency.
When should you choose L4 x4 or x8?
Choose a multi-GPU configuration when you need more total VRAM, parallel inference tasks or large-scale media/video pipelines.
Is L4 suitable for AI video?
Yes. The L4 suits video analytics, transcoding, computer vision, streaming and energy-efficient inference.
Can the L4 be used for chatbots or RAG?
Can be used for embeddings, RAG, small-model inference and moderate-load AI APIs, depending on VRAM and latency requirements.
When should you choose the L4 over a larger GPU?
Choose the L4 when the priority is cost, power efficiency, and steady inference and media work rather than large-model training.
What billing cycles are available?
Plans are available on 1-month, 3-month, 6-month, 12-month, and long-term cycles.
Need configuration advice for NVIDIA L4 Workload?
HiTechCloud helps you select the GPU count, cores, RAM, storage, driver, CUDA version, AI framework and media stack for your deployment.




























































































































