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V100 vs A100

Explore a head to head comparison of specifications, performance, and pricing.

V100

The NVIDIA V100 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU ArchitectureVolta
Average Price$2.59/hr
GPU VRAM16 GB
Cloud Availability3 clouds
System Memory448 GB
CPU Cores92
Storage6.0 TB

A100

The NVIDIA A100 is a powerful Ampere-based GPU designed for AI training, inference, and high-performance computing workloads.

ManufacturerNVIDIA
GPU ArchitectureAmpere
Average Price$7.35/hr
GPU VRAM40 GB
Cloud Availability5 clouds
System Memory1800 GB
CPU Cores176
Storage13.6 TB

V100 vs A100: Which Should You Choose?

The A100 offers 40 GB of VRAM — 3× the 16 GB on the V100 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the A100 delivers 77.97 TFLOPS versus 28.26 TFLOPS on the V100 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the V100 at 0.90 TB/s compared to 0.00 TB/s on the A100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the V100 is built on Volta while the A100 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the V100 starts from $0.39/hr versus $1.36/hr for the A100 — 249% more expensive — reflecting the performance premium. The A100 is available across 5 cloud providers on Shadeform compared to 3 for the V100, giving more options for region and pricing flexibility.

V100 — Best Use Cases

  • Deep learning training
  • HPC and scientific computing
  • Legacy ML infrastructure

Choose V100 when:

  • 16 GB VRAM is sufficient for your workload
  • Cost efficiency is your primary concern
  • Your workload does not require peak FP16 throughput
  • Your preferred provider already has availability

A100 — Best Use Cases

  • General-purpose deep learning training
  • Fine-tuning models up to 13B parameters
  • AI inference at moderate throughput
  • Computer vision and NLP workloads

Choose A100 when:

  • You need 40 GB+ VRAM for large models or long context windows
  • Maximum performance justifies the higher cost
  • You are training large models or running high-throughput inference
  • You need flexibility across multiple cloud providers or regions

See how the V100 & A100 compare

Compare detailed hardware specifications and average pricing for the V100 and A100.

Compare Hardware Specifications

V100A100
GPU Type
V100
A100
VRAM per GPU
16 GB
40 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Volta
Ampere
Interconnect
PCIe Gen3
PCIe Gen4 or SXM4
Memory Bandwidth
900 GB/s
1.55 TB/s
FP16 TFLOPS
28.26 TFLOPS (2:1)
77.97 TFLOPS (4:1)
CUDA Cores
5120
6912
Tensor Cores
640 (1st Gen)
432 (3rd Gen)
Base Clock
1230 MHz
765 MHz
Boost Clock
1380 MHz
1410 MHz
TDP
250-300W
250W-400W
Process Node
TSMC 12nm
TSMC 7nm
Data Formats
FP16, FP32, FP64
INT8, BF16, FP16, TF32, FP32, FP64

Compare Average On-Demand Pricing

V100A100
1 GPU
$1.36 /hr
$1.88 /hr
2 GPUs
$0.78 /hr
$4.38 /hr
4 GPUs
N/A
$8.64 /hr
8 GPUs
$4.72 /hr
$14.90 /hr

Frequently Asked Questions: V100 vs A100

The main differences are VRAM (16 GB vs 40 GB), FP16 throughput (28.26 vs 77.97 TFLOPS), architecture (Volta vs Ampere). The V100 uses the Volta architecture while the A100 is based on Ampere, giving each GPU different generational capabilities.

The A100 is generally better for large language model training due to its higher throughput and 40 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.

On Shadeform, the V100 is available from $0.39/hr. The A100 starts from $1.36/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.

The A100 has more VRAM at 40 GB, compared to 16 GB on the V100. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.

Based on TFLOPS per dollar, the V100 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A100, paying the premium may be justified by faster job completion and lower total cost.

The A100 is currently available across 5 cloud providers on Shadeform's network, compared to 3 for the V100. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.

Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either V100 or A100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore V100 & A100 Instances

Browse available instances with V100 and A100 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

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