What Is the NVIDIA L4 GPU?
The NVIDIA L4 is a single-slot, low-power data centre GPU built on the Ada Lovelace architecture, purpose-built for AI inference, generative AI, and video processing. It pairs 24GB of GDDR6 memory over a 192-bit bus (300GB/s bandwidth) with fourth-generation Tensor Cores and dedicated AV1 video encode/decode hardware, running at just 72W - enough VRAM to run most 7B–13B parameter LLMs, Stable Diffusion XL, and Whisper for inference without offloading.
It also handles lightweight fine-tuning and small-model training but for large-scale foundation model training, CloudPe's H200 instances are the right tool. On CloudPe, L4 instances deploy in under 60 seconds from data centres in India.
Full specifications:
Architecture
NVIDIA Ada Lovelace
GPU Memory
24 GB GDDR6
Memory Bandwidth
300 GB/s
CUDA Cores
7,424
Tensor Cores
240 (4th Gen)
FP8 Tensor Performance
Up to 485 TFLOPS (sparse)
FP16 Tensor Performance
Up to 242 TFLOPS (sparse)
FP32 Performance
30.3 TFLOPS
Max Power (TDP)
72W
Interconnect
PCIe Gen4 x16
Form Factor
Single-slot, low-profile PCIe
Best for
AI inference, generative AI serving, video transcoding, VDI, lightweight/small-model training — not large-scale foundation model training
Why Choose NVIDIA L4 for AI Inference
Maximum inference throughput at minimum cost per query.
Generative AI Inference
Up to 2.5x higher generative AI inference performance than the previous-generation T4, at the same power envelope.
Model Compatibility
24GB of GDDR6 comfortably runs open-source models like Llama 2 (7B/13B), Mistral 7B, Stable Diffusion XL, and Whisper V3 for inference, and small/medium models for lightweight training — 3–10x faster than CPU.
INT8 / FP8 Precision
Quantization-ready Tensor Cores deliver faster inference without a meaningful accuracy trade-off — cutting cost per query further.
Video Transcoding (AV1 Support)
Dedicated AV1 encode/decode hardware cuts video streaming bandwidth costs by up to 40% versus H.264/H.265.
Cloud Gaming & VDI
Third-gen RT Cores bring near-cinematic ray tracing to cloud gaming and virtual desktops.
Virtual Workstations
Purpose-built for architecture, design, and media teams running GPU-accelerated VDI without on-prem hardware.
How Does NVIDIA L4 Compare?
NVIDIA L4 vs T4
| Specification | T4 (Previous Gen) | L4 (Current Gen) | Improvement |
|---|---|---|---|
| Architecture | Turing | Ada Lovelace | 2 generations newer |
| GPU Memory | 16 GB GDDR6 | 24 GB GDDR6 | +50% VRAM |
| FP32 Performance | 8.1 TFLOPS | 30.3 TFLOPS | ~3.7x higher |
| Ray Tracing | 1st Gen RT Cores | 3rd Gen RT Cores | ~3x performance |
| Video Codec | H.264 / H.265 | AV1 / H.265 | Up to 40% bandwidth savings |
| DLSS Support | DLSS 2 | DLSS 3 | AI frame generation |
| Max TDP | 70W | 72W | Similar power, far higher output |
NVIDIA L4 vs A100 — Which Should You Choose?
The L4 and A100-class GPUs solve different problems — they're not direct substitutes.
| L4 | A100-class | |
|---|---|---|
| GPU Memory | 24 GB GDDR6 | 40/80 GB HBM2e |
| Best For | Inference, video AI, VDI, lightweight training | Large model training, multi-GPU clusters |
| Power (TDP) | 72W | 300–400W |
| Cost per hour | Significantly lower | Significantly higher |
| NVLink / Multi-GPU scaling | No | Yes |
| Ideal user | Teams serving models in production | Teams training or fine-tuning large models |
Is L4 better than A100? Not "better" or "worse", different jobs. If you're serving inference at scale on a budget, L4 wins on cost-per-query. If you're training or fine-tuning large models (70B+ parameters, 100K+ context), CloudPe's H200 instances are built for exactly that — with the memory bandwidth and headroom L4 doesn't have.
NVIDIA L4 vs RTX Pro 6000 — Two Different Jobs on CloudPe
| L4 | RTX PRO 6000 | |
|---|---|---|
| Memory | 24 GB GDDR6 | Higher VRAM, workstation-class |
| Best for | Video analytics, edge inference, cost-sensitive AI workloads | Professional 3D rendering, AI content creation, digital twin simulation |
| Starting price | ₹35.86/hr | ₹215.31/hr |
If your workload is inference or video at scale, L4 is the cost-efficient choice. If you're rendering, doing AI-assisted content creation, or running simulation workloads, RTX Pro 6000 is the better fit.
NVIDIA L4 Price in India
CloudPe's NVIDIA L4 instances start at ₹35.86/hour (₹26,180/month, or ₹23,038/month equivalent on an annual plan — a 12% saving), with no hidden bandwidth or egress fees.
| Config | vCPU | RAM | Hourly | Monthly | Annual (equiv./mo) |
|---|---|---|---|---|---|
| 1× L4 | 8 | 32 GB | ₹35.86 | ₹26,180 | ₹23,038 |
| 1× L4 | 16 | 64 GB | ₹41.21 | ₹30,080 | ₹26,470 |
| 1× L4 | 24 | 128 GB | ₹46.55 | ₹33,980 | ₹29,902 |
| 2× L4 | 16 | 64 GB | ₹71.20 | ₹51,974 | ₹45,737 |
| 2× L4 | 32 | 128 GB | ₹81.88 | ₹59,774 | ₹52,601 |
| 2× L4 | 64 | 256 GB | ₹92.57 | ₹67,574 | ₹59,465 |
| 4× L4 | 32 | 128 GB | ₹141.87 | ₹1,03,562 | ₹91,135 |
| 4× L4 | 64 | 256 GB | ₹163.23 | ₹1,19,162 | ₹1,04,863 |
| 4× L4 | 96 | 512 GB | ₹184.60 | ₹1,34,762 | ₹1,18,591 |
What's Included in the Price
1x, 2x, and 4x L4 Configurations
Need more than one GPU for parallel inference or higher throughput? CloudPe offers 1×, 2×, and 4× L4 configurations on a single instance — from ₹35.86/hr for a single L4 up to ₹184.60/hr for a 4× L4 / 96 vCPU / 512GB RAM instance. Ideal for high-volume chatbot serving, batch video transcoding, or running multiple models side by side.
Pricing Calculator
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Mumbai Tier-4 Datacenter · Monthly & yearly commitments available · Custom configurations on request
Why Run NVIDIA L4 on CloudPe
Data hosted in India (Mumbai Tier-3/4 data centres, IN-WEST2 region) — no cross-border transfer, simpler DPDP compliance
INR billing — no dollar-denominated surprises, no egress fees
Deploy in under 60 seconds via OpenStack-compatible APIs — existing tooling works unchanged
120+ L2/L3 engineers, 24/7, sub-2-hour resolution SLA
99.9% uptime SLA — enforceable, not a marketing number
20–60% lower cost than equivalent hyperscaler GPU instances
What Can You Build on NVIDIA L4?
AI Chatbots
Deploy customer support bots on Llama or Mistral models. L4 delivers low latency and high concurrency per query.
Visual Search
Power “search by image” across millions of product photos with fast, low-cost inference.
Video Transcoding & Streaming
AV1 hardware encode cuts bandwidth costs while scaling live and VOD transcoding pipelines.
Smart Cities
Process real-time video feeds from traffic and surveillance cameras — L4's decode engines handle high camera counts per GPU.
Frequently Asked Questions
AI inference, generative AI serving, video transcoding, and virtual desktop/graphics workloads. It also handles lightweight fine-tuning and small-model training well — but for large-scale foundation model training, an H200 instance is the better fit.
No — they're built for different jobs. A100-class GPUs are for training and fine-tuning large models; the L4 is for running (inference) already-trained models at a much lower cost per hour. Most production inference workloads are cheaper and just as fast on L4.
On CloudPe, L4 instances start at ₹35.86/hour or ₹26,180/month, with no egress fees. Multi-GPU (2× and 4×) configurations are also available.
CloudPe hosts NVIDIA L4 GPUs in Mumbai data centres with INR billing starting at ₹35.86/hour — roughly 20–60% lower than equivalent hyperscaler GPU pricing.
24GB of GDDR6 memory with 300GB/s bandwidth — enough to run most 7B–13B parameter LLMs and diffusion models for inference.
Yes — it's purpose-built for it. L4 delivers up to 2.5x higher generative AI inference performance than the T4 at just 72W, making it one of the most cost-efficient inference GPUs available.
Yes, for inference. Models like Llama 2 7B/13B and Mistral 7B run comfortably within 24GB VRAM. Larger models (30B+) or large-scale training/fine-tuning need an H200 instance instead.
Yes. Dedicated AV1 and H.265 encode/decode hardware handles high-throughput video transcoding at lower bandwidth cost.
Under 60 seconds, through CloudPe's OpenStack-compatible dashboard or API.
72W maximum — low enough to run without auxiliary power connectors, which is part of why it's cheaper to operate at scale than higher-TDP GPUs.