Ampere Data Center Architecture
Rent NVIDIA A100 Cloud GPU
The proven data center GPU that launched the AI revolution. 40GB HBM2e, 6,912 CUDA cores, and 1,555 GB/s memory bandwidth—starting at $1.64/hr on VectorLay. Up to 55% cheaper than AWS and GCP for the same GPU.
NVIDIA A100: The Workhorse of Modern AI Infrastructure
The NVIDIA A100 is the data center GPU that powered the AI revolution. Released in 2020 as the flagship of the Ampere data center lineup, it became the default choice for AI training and inference at every major tech company, research lab, and cloud provider. From GPT-3 to Stable Diffusion, the A100 was the hardware behind the breakthroughs that brought AI into the mainstream.
What makes the A100 special is its HBM2e memory. With 40GB of high-bandwidth memory delivering 1,555 GB/s of throughput, the A100 can handle models that are too large for consumer GPUs with their 24GB GDDR6X. This makes it essential for running unquantized 13B–34B parameter models, for training workloads that require large batch sizes, and for scientific computing applications that need fast double-precision performance.
On VectorLay, the A100 is available at $1.64/hr—roughly half what hyperscalers charge. With no egress fees, per-minute billing, and built-in auto-failover, you get enterprise-grade A100 compute without the enterprise procurement process. Whether you're running production inference, training custom models, or conducting research, VectorLay delivers A100 access at startup-friendly prices.
A100 40GB Technical Specifications
| Specification | A100 40GB |
|---|---|
| GPU Architecture | Ampere (GA100) |
| VRAM | 40GB HBM2e |
| CUDA Cores | 6,912 |
| Memory Bandwidth | 1,555 GB/s |
| FP32 Performance | 19.5 TFLOPS |
| FP16 (Tensor) | 312 TFLOPS (with sparsity: 624) |
| TF32 (Tensor) | 156 TFLOPS (with sparsity: 312) |
| FP64 (Double) | 9.7 TFLOPS |
| TDP | 400W |
| Memory Bus | 5120-bit |
| Tensor Cores | 432 (3rd Gen) |
| NVLink | 3rd Gen, 600 GB/s |
| Multi-Instance GPU (MIG) | Yes (up to 7 instances) |
The A100 introduced several features that defined modern AI hardware: TF32 for automatic training speedups without code changes, structured sparsity for 2x throughput on compatible models, 3rd-gen NVLink for multi-GPU scaling, and Multi-Instance GPU (MIG) for splitting a single A100 into up to 7 isolated GPU instances. These features made the A100 the most versatile data center GPU of its generation.
A100 Cloud GPU Pricing on VectorLay
At $1.64/hr, VectorLay offers the most competitive A100 pricing in the cloud. Here's how it compares to the major providers:
| Provider | GPU | $/hour | vs VectorLay |
|---|---|---|---|
| VectorLay | A100 (40GB) | $1.64 | — |
| CoreWeave | A100 (40GB) | $2.21 | +35% |
| Azure | A100 (40GB) | $3.40 | +107% |
| AWS | A100 (40GB) | $3.67 | +124% |
| GCP | A100 (40GB) | $3.67 | +124% |
Switching from AWS to VectorLay for a single A100 saves $1,461 per month—$17,532 per year. For teams running multiple GPUs, the savings scale linearly. And with VectorLay's included storage, egress, and load balancing, the actual savings are even higher since hyperscalers charge extra for all of those.
Best Use Cases for the A100
The A100 occupies a unique position in the GPU landscape: it's the most cost-effective way to access HBM memory and data center features without stepping up to H100 pricing. Here are the workloads where it delivers the best value:
Medium-to-Large Model Inference (13B–34B)
Run Llama 3 13B, CodeLlama 34B, and Mixtral 8x7B at FP16 precision without quantization. The 40GB HBM2e provides enough memory for these models while the high bandwidth (1,555 GB/s) ensures fast token generation. For workloads where quantization would reduce output quality, the A100 lets you serve models at full precision at a reasonable cost.
Mixed-Precision Model Training
Fine-tune and train models up to 13B parameters at full precision, or up to 30B+ with gradient checkpointing and mixed precision. The A100's TF32 Tensor Cores automatically accelerate training without code changes, and BF16 support ensures numerical stability for large-scale training runs. 3rd-gen NVLink enables efficient multi-GPU training for larger models.
Large Batch Inference
Process high volumes of requests with continuous batching frameworks like vLLM and TGI. The A100's 40GB memory allows for larger batch sizes than 24GB consumer GPUs, increasing throughput per GPU. For production APIs handling thousands of concurrent requests, the A100's batch processing capabilities can serve more users per dollar than running multiple smaller GPUs.
Scientific Computing & HPC
The A100 delivers 9.7 TFLOPS of FP64 (double-precision) performance, making it one of the best GPUs for scientific computing workloads. Molecular dynamics, computational fluid dynamics, climate simulations, and genomics analysis all benefit from the A100's combination of high FP64 throughput and 40GB HBM memory. The 1,555 GB/s memory bandwidth keeps data flowing for memory-intensive simulations.
Multi-Modal AI & Computer Vision
Deploy vision-language models, large vision transformers, and multi-modal systems that require more than 24GB VRAM. Models like CLIP ViT-L, SAM (Segment Anything) with high-resolution inputs, and video understanding models benefit from the A100's generous memory and bandwidth. The 40GB capacity enables larger input resolutions and batch sizes for vision workloads.
Development & Experimentation
The A100 is the industry standard GPU for ML development. Every major framework (PyTorch, TensorFlow, JAX) is optimized for A100, every tutorial and benchmark uses A100 numbers, and every deployment guide assumes A100 compatibility. Using an A100 for development ensures your models will work seamlessly when you scale to production—no hardware-specific surprises.
How to Deploy an A100 on VectorLay
VectorLay makes A100 access as simple as renting a consumer GPU. No enterprise sales calls, no capacity negotiations, no months-long procurement. Here's how to get started:
Create your account
Sign up at vectorlay.com/get-started. No credit card required to start. You'll get immediate access to the dashboard and CLI, with the A100 available in the GPU catalog alongside consumer and other data center GPUs.
Select the A100 40GB
Choose the A100 from the GPU catalog. Configure the number of GPUs, region, and your Docker container image. VectorLay supports all major ML serving frameworks including vLLM, Text Generation Inference, Triton Inference Server, and custom containers.
Deploy with full hardware access
VectorLay provisions your A100 with VFIO GPU passthrough via Kata Containers. You get full, exclusive access to the GPU—all 40GB HBM2e, all 432 Tensor Cores, NVLink connectivity, and MIG capability. No GPU sharing, no virtualization overhead, bare-metal performance with container security.
Monitor and scale
Your A100 deployment comes with built-in auto-failover, load balancing, and real-time monitoring. View GPU utilization, memory usage, and throughput from the dashboard. Add more GPUs as demand grows, or scale down when it drops. Per-minute billing means you only pay for actual usage—no wasted compute.
A100 vs RTX 4090 vs H100: Choosing the Right GPU
Each GPU serves different needs. Here's a comprehensive comparison to help you make the right choice:
| Feature | RTX 4090 | A100 40GB | H100 SXM |
|---|---|---|---|
| VectorLay Price | $0.49/hr | $1.64/hr | $2.49/hr |
| VRAM | 24GB GDDR6X | 40GB HBM2e | 80GB HBM3 |
| Memory BW | 1,008 GB/s | 1,555 GB/s | 3,350 GB/s |
| FP32 | 82.6 TFLOPS | 19.5 TFLOPS | 67 TFLOPS |
| Best for | Fast inference ≤24GB | Training + 40GB models | Large-scale AI |
| NVLink | No | Yes (600 GB/s) | Yes (900 GB/s) |
Choose the A100 when: You need 40GB VRAM for models that don't fit in 24GB, you're doing mixed-precision training with TF32 or BF16, you need NVLink for multi-GPU workloads, you require MIG for multi-tenant GPU sharing, or you need FP64 performance for scientific computing. The A100 is the middle ground between consumer GPU value and cutting-edge H100 performance.
Choose the RTX 4090 instead when: Your models fit in 24GB VRAM and you want the lowest cost for inference. At $0.49/hr, it's 70% cheaper than the A100 and delivers higher FP32 throughput.
Choose the H100 instead when: You need 80GB VRAM, the Transformer Engine for FP8 training, or maximum throughput for large transformer models. The H100 is 3x faster per training step for transformer workloads.
A100 Performance for AI Workloads
Here are representative performance numbers for the A100 across common AI and ML workloads:
| Workload | Model | Performance |
|---|---|---|
| LLM Inference | Llama 3 13B (FP16) | ~45 tokens/sec |
| LLM Inference | Llama 3 8B (FP16) | ~70 tokens/sec |
| Training | 7B fine-tune (BF16) | ~1,200 tokens/sec |
| Image Generation | SDXL (30 steps, 1024×1024) | ~4.0 sec/image |
| Batch Throughput | Llama 3 8B (continuous batching) | ~1,500 tokens/sec |
The A100's strength lies not in single-request latency (where the RTX 4090 excels) but in throughput and memory capacity. With 40GB HBM2e, the A100 can handle larger batch sizes and larger models, making it the better choice for production inference servers that need to handle high concurrent loads with models that exceed consumer GPU memory.
Frequently Asked Questions
How much does it cost to rent an A100 on VectorLay?
VectorLay offers NVIDIA A100 40GB cloud GPUs at $1.64 per hour with per-minute billing. That works out to approximately $1,181 per month for 24/7 usage. There are no minimum commitments, no egress fees, and no hidden costs. This is 50–55% less than what AWS ($3.67/hr), GCP ($3.67/hr), and Azure ($3.40/hr) charge for A100 instances.
What is the A100 best used for?
The A100 excels at mixed-precision training, large batch inference, and workloads that need more than 24GB VRAM. Its 40GB HBM2e can run models like Llama 3 13B at full FP16 precision, and its high memory bandwidth (1555 GB/s) makes it excellent for memory-bound inference tasks. It's also widely used in HPC and scientific computing for double-precision workloads.
Should I choose the A100 or the RTX 4090?
For models that fit in 24GB VRAM, the RTX 4090 at $0.49/hr is usually the better choice—it's faster (82.6 vs 19.5 FP32 TFLOPS) and 70% cheaper. Choose the A100 when you need: more than 24GB VRAM, HBM2e bandwidth for memory-bound workloads, proven data center reliability, or NVLink for multi-GPU training. The A100's 40GB VRAM accommodates larger models at full precision.
Should I choose the A100 or the H100?
The H100 at $2.49/hr is approximately 3x faster than the A100 for transformer-based workloads thanks to its Transformer Engine and FP8 support. On a cost-per-token basis, the H100 is often more economical despite its higher hourly rate. Choose the A100 when: your budget is tighter, your workload doesn't benefit from FP8 or the Transformer Engine, or you need a well-established platform with maximum software compatibility.
Can I run Llama 3 70B on a single A100?
The A100 40GB cannot fit Llama 3 70B at FP16 (which requires ~140GB). However, you can run it with aggressive INT4 quantization (GPTQ/AWQ) which reduces the memory footprint to approximately 35–40GB. For full-precision 70B inference, consider the H100 80GB or a multi-GPU A100 setup. The A100 40GB is ideal for models up to 13B at FP16 or up to 34B with quantization.
Does VectorLay offer multi-GPU A100 deployments?
Yes. VectorLay supports multi-GPU A100 deployments with automatic load balancing and failover. You can scale horizontally across multiple A100 nodes for distributed inference or distributed training workloads. NVLink-connected multi-GPU configurations are available for workloads that require high-bandwidth GPU-to-GPU communication.
Ready to deploy on the A100?
Enterprise-grade GPU compute at startup-friendly prices. No credit card required. No egress fees. No minimum commitments. Deploy in minutes, not weeks.