Efficiency profile
NVIDIA L4
Ada Lovelace · 24 GB PCIe
Memory
24 GB GDDR6
24 GB GDDR6
Bandwidth
300 GB/s
300 GB/s
FP32
30.3 TFLOPS
30.3 TFLOPS
TDP
72W
72W
Independently verified GPU comparison Updated July 15, 2025
NVIDIA L4 vs H100
Architecture, memory, AI performance, media capabilities and real-world rental pricing compared using NVIDIA’s official specifications and AceCloud’s current cloud GPU configurations.
Performance profile
NVIDIA H100 HGX
Hopper · 80 GB SXM
Memory
80 GB HBM3
80 GB HBM3
Bandwidth
3.35 TB/s
3.35 TB/s
FP32
67 TFLOPS
67 TFLOPS
TDP
Up to 700W
Up to 700W
The honest take: L4 is the economical choice for inference, video processing, graphics and virtualized applications that fit within 24 GB. H100 costs about 4.9× more in AceCloud’s starting configurations but provides 3.3× the memory, 11.2× the bandwidth and approximately 8.2× the FP8 and FP16 Tensor throughput. Choose H100 when training scale, model size or time to result justifies the additional cost.
Quick answer
Which GPU fits your workload?
A workload-first view of where H100 delivers scale and L4 delivers efficiency.
Training large AI models
Pretraining, full fine-tuning and distributed AI workloads
H100 H100’s 80 GB HBM3, Transformer Engine, high Tensor throughput and NVLink connectivity make it significantly better for large-model training, full fine-tuning and distributed AI workloads.
Serving smaller and mid-sized models
Production inference when models and KV cache fit within 24 GB
L4 L4 is a cost-effective inference option when the model, runtime overhead and KV cache fit within 24 GB. It supports FP8, INT8 and INT4 for optimized production inference.
Generative AI at high concurrency
Large models, long contexts and sustained request volumes
H100 For larger models, longer contexts or high request concurrency, H100’s 80 GB memory and 3.35 TB/s bandwidth provide substantially more room for model weights, batching and KV cache.
AI video and media processing
High-density streaming, transcoding and video inference
L4 L4 includes two NVENC encoders, four NVDEC decoders, four JPEG decoders and AV1 encoding support. It is purpose-built for video inference, streaming, transcoding and computer vision pipelines.
HPC and scientific computing
FP64-intensive simulations, scientific modeling and GPU analytics
H100 H100 provides FP64 and FP64 Tensor Core performance, high-bandwidth HBM3 and NVLink. It is the appropriate choice for simulations, scientific computing and large-scale GPU analytics.
PoC or controlled budget
Inference testing, development and early-stage deployment
L4 L4 starts at a much lower monthly price and provides 24 GB of memory, FP8 support and fourth-generation Tensor Cores. It is a practical starting point for inference testing, development and GPU-accelerated applications.
Side by side
Full technical specifications
The table compares the exact configurations currently offered on AceCloud: NVIDIA L4 24 GB PCIe and NVIDIA H100 HGX/SXM 80 GB.
NVIDIA L4 and NVIDIA H100 technical specification comparison
| Specification | NVIDIA L4 | NVIDIA H100 HGX |
|---|---|---|
| Architecture | Ada Lovelace | Hopper |
| GPU memory | 24 GB GDDR6 | 80 GB HBM3 |
| Memory bandwidth | 300 GB/s | 3.35 TB/s |
| FP64 | Not listed | 34 TFLOPS |
| FP64 Tensor Core | Not listed | 67 TFLOPS |
| FP32 | 30.3 TFLOPS | 67 TFLOPS |
| TF32 Tensor Core* | 120 TFLOPS | 989 TFLOPS |
| BFLOAT16 Tensor Core* | 242 TFLOPS | 1,979 TFLOPS |
| FP16 Tensor Core* | 242 TFLOPS | 1,979 TFLOPS |
| FP8 Tensor Core* | 485 TFLOPS | 3,958 TFLOPS |
| INT8 Tensor Core* | 485 TOPS | 3,958 TOPS |
| Tensor Cores | Fourth generation | Fourth generation |
| Transformer Engine | Not listed by NVIDIA | First generation |
| NVENC encoders | 2 | Not listed |
| NVDEC decoders | 4 | 7 |
| JPEG decoders | 4 | 7 |
| AV1 encoding | Supported | Not listed |
| MIG support | No | Up to 7 instances at 10 GB each |
| NVLink | No | Fourth-generation NVLink, 900 GB/s |
| PCIe interface | PCIe Gen4 x16, 64 GB/s | PCIe Gen5, 128 GB/s |
| Form factor | Single-slot, low-profile PCIe | SXM |
| Maximum TDP | 72W | Up to 700W, configurable |
| AceCloud configurations | 1×, 2× and 4× GPUs | 1×, 2×, 4× and 8× GPUs |
This is not a GPU-only price comparison. The H100 instance also includes significantly more vCPU and system RAM. Buyers should compare the complete configuration, workload completion time and cost per useful output.
Straight comparison
How much faster is H100, really?
Raw specifications translated into simple relative performance. Longer bars indicate higher output.
FP16 Tensor performance, with sparsity
TFLOPS L4
1.0×
H100
8.2×
FP8 Tensor performance, with sparsity
TFLOPS L4
1.0×
H100
8.2×
Memory bandwidth
GB/s L4
1.0×
H100
11.2×
GPU memory capacity
GB L4
1.0×
H100
3.3×
FP32 performance
TFLOPS L4
1.0×
H100
2.2×
Maximum TDP
Watts L4
H100
Monthly rental price
₹ L4
1.0×
H100
4.9×
Disclaimer: These are theoretical peak specifications. Real-world performance depends on model architecture, precision, batch size, sequence length, framework, optimization, CPU and RAM allocation and multi-GPU topology.
What you pay
NVIDIA L4 vs H100 rental pricing
Current entry-level Noida pricing for Linux instances, excluding taxes:
NVIDIA L4 24 GB
1× L4 cloud GPU instance
₹
36,730
/month
36,730
/month
- 8 vCPUs
- 32 GB RAM
- Six-month effective price: ₹34,894/month
- Twelve-month effective price: ₹33,057/month
- 1×, 2× and 4× configurations available
₹20,000 free credits · Spin up in <15 min
NVIDIA H100 HGX 80 GB
1× H100 HGX cloud GPU instance
₹
180,000
/month
180,000
/month
- 26 vCPUs
- 250 GB RAM
- Six-month effective price: ₹171,000/month
- Twelve-month effective price: ₹162,000/month
- 1×, 2×, 4× and 8× configurations available
₹20,000 free credits · Help with setup
This is not a GPU-only price comparison. The H100 instance also includes significantly more vCPU and system RAM. Buyers should compare the complete configuration, workload completion time and cost per useful output.
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