Hopper Architecture
Rent NVIDIA H100 SXM Cloud GPU
The most powerful data center GPU ever built. 80GB HBM3, 16,896 CUDA cores, Transformer Engine, and 3,350 GB/s memory bandwidth—starting at $2.49/hr on VectorLay. Train and deploy the largest AI models at a fraction of hyperscaler prices.
NVIDIA H100: The Gold Standard for Enterprise AI
The NVIDIA H100 SXM is the flagship data center GPU built on the Hopper architecture, released in 2023. It represents a generational leap in AI compute, featuring the revolutionary Transformer Engine that automatically applies mixed FP8/FP16 precision to accelerate transformer-based models by up to 3x over the previous-generation A100.
With 80GB of HBM3 high-bandwidth memory delivering 3,350 GB/s of throughput, the H100 eliminates the memory bottleneck that limits smaller GPUs when running large language models. Models like Llama 3 70B, Mixtral 8x7B, and DBRX that can't fit on 24GB consumer GPUs run natively on the H100 at full precision—no quantization compromises required.
The H100 is the GPU that's training the next generation of foundation models. From OpenAI to Meta to Anthropic, every major AI lab relies on H100 clusters. On VectorLay, you can access this same compute at $2.49/hr—roughly half what AWS and GCP charge for comparable H100 instances. Whether you're training custom models, running high-throughput inference, or doing cutting-edge research, the H100 on VectorLay gives you enterprise-grade performance without enterprise-grade pricing.
H100 SXM Technical Specifications
| Specification | H100 SXM |
|---|---|
| GPU Architecture | Hopper (GH100) |
| VRAM | 80GB HBM3 |
| CUDA Cores | 16,896 |
| Memory Bandwidth | 3,350 GB/s |
| FP32 Performance | 67 TFLOPS |
| FP16 (Tensor) | 989 TFLOPS (with sparsity: 1,979) |
| FP8 (Tensor) | 1,979 TFLOPS (with sparsity: 3,958) |
| TDP | 700W |
| Memory Type | HBM3 (5th Gen) |
| Tensor Cores | 528 (4th Gen) |
| NVLink | 4th Gen, 900 GB/s |
| Transformer Engine | Yes (FP8 auto-mixed precision) |
The H100's standout feature is its Transformer Engine, which dynamically switches between FP8 and FP16 precision during computation. This delivers up to 3x the throughput of A100 for transformer models without any code changes—the hardware handles precision management automatically. Combined with 3,350 GB/s of HBM3 bandwidth (more than 2x the A100), the H100 is purpose-built for the large language models that define modern AI.
H100 Cloud GPU Pricing on VectorLay
VectorLay's H100 pricing is dramatically lower than hyperscalers. AWS charges $4.76/hr for p5 H100 instances, GCP charges $4.52/hr, and Azure charges $3.67/hr. Even dedicated GPU providers like CoreWeave charge $2.21/hr for A100s (not H100s). At $2.49/hr, VectorLay offers the best price-to-performance for H100 access in the cloud.
| Provider | GPU | $/hour | $/month |
|---|---|---|---|
| VectorLay | H100 SXM | $2.49 | $1,793 |
| Azure | H100 (80GB) | $3.67 | $2,642 |
| GCP | H100 (80GB) | $4.52 | $3,254 |
| AWS | H100 (80GB) | $4.76 | $3,427 |
| Lambda Labs | H100 (80GB) | $2.99 | $2,153 |
All VectorLay pricing includes storage, load balancing, and network egress. No hidden fees. With the savings over AWS alone, you could run an H100 for an extra 5+ months per year at the same budget.
Best Use Cases for the H100
The H100 is the right choice when your workload demands the absolute highest performance, the most VRAM, or features only available on the Hopper architecture. Here's where it excels:
Large Language Model Training
Train and fine-tune 70B+ parameter models with the H100's Transformer Engine, FP8 support, and 80GB HBM3. The H100 delivers up to 3x training throughput compared to A100, completing fine-tuning runs in hours instead of days. Multi-GPU training via NVLink enables scaling to even larger models. Whether you're training domain-specific LLMs or fine-tuning foundation models, the H100 is the fastest path to results.
Large Model Inference (70B+)
Run Llama 3 70B, Mixtral 8x7B, DBRX, and other models that exceed 24GB VRAM at full precision. The H100's 80GB HBM3 accommodates these massive models without quantization, preserving maximum quality. Combined with 3,350 GB/s memory bandwidth, the H100 delivers industry-leading tokens-per-second for large model inference.
Multi-Modal AI & Vision-Language Models
Deploy large vision-language models like LLaVA, CogVLM, and GPT-4V-class models that combine text and image understanding. These models often require 40–80GB VRAM and benefit enormously from the H100's memory bandwidth and Transformer Engine acceleration for both the vision encoder and language decoder components.
High-Throughput Inference Serving
Serve thousands of concurrent users with continuous batching on the H100. The massive memory allows larger batch sizes, the Transformer Engine accelerates every forward pass, and the HBM3 bandwidth keeps the GPU fed with data. For production APIs that need to handle burst traffic at scale, the H100 provides unmatched throughput per GPU.
AI Research & Experimentation
Push the boundaries of AI research with the H100's cutting-edge capabilities. FP8 precision, dynamic parallelism, and the Transformer Engine enable experiments that simply aren't possible on older hardware. Whether you're exploring new architectures, novel training techniques, or scaling laws, the H100 gives you the compute headroom to iterate quickly.
Scientific Computing & HPC
Accelerate molecular dynamics, climate modeling, genomics, and other HPC workloads. The H100's 67 TFLOPS FP32 and massive memory bandwidth make it ideal for double-precision scientific computing. NVLink enables multi-GPU communication at 900 GB/s for distributed simulations that span multiple GPUs.
How to Deploy an H100 on VectorLay
Accessing H100 compute on VectorLay is just as simple as any other GPU—no months-long waitlists, no capacity negotiations, no enterprise sales calls. Here's the process:
Create your account
Sign up at vectorlay.com/get-started. No upfront commitments or lengthy procurement processes. Get access to the dashboard and CLI immediately upon signup.
Select the H100 SXM
Choose the H100 SXM from the GPU catalog. Configure your deployment: single GPU or multi-GPU, region preferences, and container image. VectorLay supports any Docker image, including popular ML frameworks like vLLM, TGI, and Triton.
Deploy with full GPU passthrough
VectorLay provisions your H100 with VFIO passthrough via Kata Containers. Your workload gets bare-metal GPU performance with strong security isolation. The full 80GB HBM3 and all Tensor Cores are exclusively yours—no GPU sharing or virtualization overhead.
Scale and monitor
Your H100 deployment includes auto-failover, load balancing, and real-time monitoring. Track GPU utilization, memory usage, and inference throughput from the dashboard. Scale up by adding more GPUs, or scale down when demand drops—billing is per-minute, so you only pay for what you use.
H100 vs A100: A Generational Leap
The H100 represents a massive leap over the A100 across every metric that matters for AI workloads. Here's a direct comparison:
| Feature | H100 SXM | A100 40GB |
|---|---|---|
| VRAM | 80GB HBM3 | 40GB HBM2e |
| Memory BW | 3,350 GB/s | 1,555 GB/s |
| FP16 Tensor | 989 TFLOPS | 312 TFLOPS |
| FP8 Support | Yes (3,958 TFLOPS) | No |
| Transformer Engine | Yes | No |
| VectorLay Price | $2.49/hr | $1.64/hr |
The H100 costs 52% more per hour than the A100, but delivers 3x+ the throughput for transformer workloads. On a cost-per-token or cost-per-training-step basis, the H100 is actually the more economical choice for most modern AI workloads. The Transformer Engine alone can double your effective throughput without any code changes—something the A100 simply cannot match.
H100 Performance for AI Workloads
The H100 delivers unparalleled performance across the full spectrum of AI tasks. Here are representative benchmarks:
| Workload | Model | Performance |
|---|---|---|
| LLM Inference | Llama 3 70B (FP16) | ~40 tokens/sec |
| LLM Inference | Llama 3 8B (FP16) | ~180 tokens/sec |
| Training | 13B fine-tune (FP8) | ~3x faster than A100 |
| Image Generation | SDXL (30 steps, 1024×1024) | ~1.5 sec/image |
| Batch Throughput | Llama 3 8B (continuous batching) | ~3,000 tokens/sec |
These numbers showcase why the H100 is the preferred GPU for production AI at scale. The combination of massive memory, extreme bandwidth, and the Transformer Engine creates a computing platform that's purpose-built for the demands of modern large language models and generative AI.
Frequently Asked Questions
How much does it cost to rent an H100 on VectorLay?
VectorLay offers NVIDIA H100 SXM cloud GPUs at $2.49 per hour with per-minute billing. That works out to approximately $1,793 per month for 24/7 usage. There are no minimum commitments, no egress fees, and no hidden costs. This is significantly less than hyperscalers like AWS ($4.76/hr) and GCP ($4.52/hr) for comparable H100 instances.
What is the difference between H100 SXM and H100 PCIe?
The H100 SXM uses NVIDIA's SXM5 form factor, which provides higher power delivery (700W vs 350W for PCIe) and supports NVLink for high-speed multi-GPU communication at 900 GB/s. The SXM version delivers significantly higher performance for both training and inference. VectorLay offers the H100 SXM variant for maximum performance.
What AI models require an H100?
The H100's 80GB HBM3 memory is essential for running very large models at full precision: Llama 3 70B at FP16, Mixtral 8x7B, large vision-language models like LLaVA-34B, and any model over 40GB in size. It's also the preferred GPU for training large models from scratch, as the Transformer Engine and FP8 support dramatically accelerate training throughput.
How does the H100 compare to the A100 for AI workloads?
The H100 is approximately 3x faster than the A100 for transformer-based models thanks to its Transformer Engine, FP8 support, and 4th-gen Tensor Cores. Memory bandwidth is 3350 GB/s (vs 1555 GB/s on A100), which is critical for large language model inference. For new projects, the H100 offers dramatically better performance-per-dollar despite its higher hourly rate.
Can I use the H100 for model training on VectorLay?
Yes. The H100 SXM is designed for both training and inference. Its 80GB HBM3, Transformer Engine, and FP8 support make it the fastest single-GPU option for training. VectorLay supports multi-GPU deployments for distributed training workloads, with auto-failover to ensure your training runs complete even if individual nodes encounter issues.
Is the H100 worth the premium over the RTX 4090?
It depends on your workload. If your models fit in 24GB VRAM, the RTX 4090 at $0.49/hr is almost always the better choice for inference. The H100 justifies its $2.49/hr price when you need: 80GB VRAM for large models, FP8 Transformer Engine for training, HBM3 bandwidth for memory-bound workloads, or multi-GPU NVLink scaling. For models that require >24GB VRAM, the H100 is the clear winner.
Ready to deploy on the H100?
Access the most powerful GPU in the world at a fraction of hyperscaler prices. No waitlists. No minimum commitments. Deploy in minutes.