Cost-efficient performance
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
Independently verified GPU comparison Updated July 15, 2025
NVIDIA H200 vs H100
Architecture, memory, workload fit and real-world rental pricing compared using NVIDIA’s official specifications and AceCloud’s current cloud GPU configurations.
High-memory performance
NVIDIA H200 NVL
Hopper · 141 GB PCIe
Memory
141 GB HBM3e
141 GB HBM3e
Bandwidth
4.8 TB/s
4.8 TB/s
FP32
60 TFLOPS
60 TFLOPS
TDP
Up to 600W
Up to 600W
The honest take: Choose H100 when your model fits comfortably within 80 GB and compute performance or acquisition cost is the main priority. Choose H200 when memory capacity, KV cache, long context lengths or memory bandwidth limit your workload. On AceCloud, H200 costs approximately 24% more per month but provides 76% more GPU memory and about 43% more memory bandwidth.
Quick answer
Which GPU fits your workload?
The clearest answer for six common AI and HPC workloads.
Training a model that fits within 80 GB
13B+ parameters, pretraining or full fine-tune
H100 For training and fine-tuning workloads that fit within 80 GB, H100 HGX provides strong Hopper-generation Tensor Core performance at a lower monthly price. Its SXM form factor also delivers higher listed compute throughput than AceCloud’s H200 NVL configuration.
Serving 70B-class LLMs
Under 13B parameters, LoRA/QLoRA
H200 H200’s 141 GB HBM3e gives teams substantially more room for model weights, runtime overhead and KV cache. Whether a specific 70B model fits on one GPU still depends on precision, context length, concurrency, batching and the inference framework.
Long-context or high-concurrency inference
Production API, many concurrent users
H200 Longer sequences and more concurrent requests increase KV-cache requirements. H200’s larger memory and 4.8 TB/s bandwidth make it better suited to memory-intensive inference, RAG and production LLM serving.
Fine-tuning with LoRA or QLoRA
CNNs, recommenders, tabular models
H100 When the model and training state fit within 80 GB, H100 is generally the more economical choice. H200 becomes useful when larger models, longer sequences or higher batch sizes cause repeated out-of-memory errors.
Memory-intensive HPC
Simulation, FP32/FP64-heavy workloads
H200 H200 is the stronger choice when scientific simulations, analytics or other HPC applications are limited by memory capacity or data movement. Compute-bound HPC workloads may see less benefit because H100 and H200 use the same Hopper-generation architecture.
PoC, experimentation or controlled budget
Dev/test, early-stage projects
H100 H100 starts at a lower monthly price and provides the same Transformer Engine, FP8 support and fourth-generation Tensor Cores. It is the practical starting point when 80 GB is sufficient.
Side by side
Full technical specifications
The table compares the specific configurations currently offered on AceCloud: NVIDIA H100 HGX/SXM 80 GB and NVIDIA H200 NVL/PCIe 141 GB.
NVIDIA H200 and NVIDIA H100 technical specification comparison
| Specification | NVIDIA H100 HGX | NVIDIA H200 NVL |
|---|---|---|
| Architecture | NVIDIA Hopper | NVIDIA Hopper |
| Form factor | SXM | PCIe, dual-slot, air-cooled |
| GPU memory | 80 GB HBM3 | 141 GB HBM3e |
| Memory bandwidth | 3.35 TB/s | 4.8 TB/s |
| FP64 | 34 TFLOPS | 30 TFLOPS |
| FP64 Tensor Core | 67 TFLOPS | 60 TFLOPS |
| FP32 | 67 TFLOPS | 60 TFLOPS |
| TF32 Tensor Core* | 989 TFLOPS | 835 TFLOPS |
| BFLOAT16 Tensor Core* | 1,979 TFLOPS | 1,671 TFLOPS |
| FP16 Tensor Core* | 1,979 TFLOPS | 1,671 TFLOPS |
| FP8 Tensor Core* | 3,958 TFLOPS | 3,341 TFLOPS |
| INT8 Tensor Core* | 3,958 TOPS | 3,341 TOPS |
| Tensor Cores | Fourth generation | Fourth generation |
| Transformer Engine | First generation | First generation |
| Max TDP | Up to 700W, configurable | Up to 600W, configurable |
| MIG support | Up to 7 instances at 10 GB each | Up to 7 instances at 16.5 GB each |
| NVLink | 900 GB/s | Up to 900 GB/s per GPU with 2-way or 4-way bridge |
| PCIe interface | PCIe Gen5, 128 GB/s | PCIe Gen5, 128 GB/s |
| Confidential computing | Supported | Supported |
| AceCloud GPU configurations | 1×, 2×, 4× and 8× | 1×, 2× and 4× |
The entry-level H200 instance costs ₹42,775 more per month, an increase of approximately 24%. In return, it provides 61 GB more GPU memory and 1.45 TB/s more memory bandwidth. Compare the full instance configuration and benchmark total workload cost rather than evaluating the GPU price alone.
Straight comparison
How much better is H200 for memory-intensive workloads?
Raw specifications translated into simple relative performance. Longer bars indicate higher output.
GPU memory capacity
GB H100
1.0×
H200
1.76×
Memory bandwidth
TB/s H100
1.0×
H200
1.43×
FP16 Tensor throughput, with sparsity
TFLOPS H100 HGX
1.0×
H200 NVL
0.84×
FP8 Tensor throughput, with sparsity
TFLOPS H100 HGX
1.0×
H200 NVL
0.84×
Monthly rental price
₹ H100 HGX
1.0×
H200 NVL
1.24×
H200 costs approximately 24% more per month in the entry configurations. The premium can be justified when its additional memory avoids multi-GPU model sharding or increases production inference throughput.
What you pay
NVIDIA H100 vs H200 rental pricing
Current Noida pricing for entry-level single-GPU Linux configurations, excluding taxes:
NVIDIA H100 HGX 80 GB
1× H100 in an HGX cloud 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
- No long-term commitment required for the standard monthly plan
NVIDIA H200 NVL 141 GB
1× H200 NVL cloud GPU instance
₹
222,775
/month
222,775
/month
- 16 vCPUs
- 128 GB RAM
- Six-month effective price: ₹211,637/month
- Twelve-month effective price: ₹200,498/month
- Hourly and monthly options available
The entry-level H200 instance costs ₹42,775 more per month, an increase of approximately 24%. In return, it provides 61 GB more GPU memory and 1.45 TB/s more memory bandwidth. Compare the full instance configuration and benchmark total workload cost rather than evaluating the GPU price alone.
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