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V100 vs A16

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

A16

The NVIDIA A16 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$3.37/hr
GPU VRAM64 GB
Cloud Availability1 clouds
System Memory960 GB
CPU Cores96
Storage1.7 TB

V100 vs A16: Which Should You Choose?

The A16 offers 64 GB of VRAM — 4× 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 V100 delivers 28.26 TFLOPS versus 4.493 TFLOPS on the A16 — 6× 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 A16, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the V100 is built on Volta while the A16 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the V100 starts from $0.39/hr versus $0.51/hr for the A16 — 31% more expensive — reflecting the performance premium. The V100 is available across 3 cloud providers on Shadeform compared to 1 for the A16, 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
  • You are training large models or running high-throughput inference
  • You need flexibility across multiple cloud providers or regions

A16 — 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 A16 when:

  • You need 64 GB+ VRAM for large models or long context windows
  • Maximum performance justifies the higher cost
  • Your workload does not require peak FP16 throughput
  • Your preferred provider already has availability

See how the V100 & A16 compare

Compare detailed hardware specifications and average pricing for the V100 and A16.

Compare Hardware Specifications

V100A16
GPU Type
V100
A16
VRAM per GPU
16 GB
64 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Volta
Ampere
Interconnect
PCIe Gen3
PCIe Gen4
Memory Bandwidth
900 GB/s
4x 200 GB/s
FP16 TFLOPS
28.26 TFLOPS (2:1)
4.493 TFLOPS (1:1)
CUDA Cores
5120
4x 1,280
Tensor Cores
640 (1st Gen)
4x 40 (3rd Gen)
RT Cores
N/A
4x 10 (2nd Gen)
Base Clock
1230 MHz
1312 MHz
Boost Clock
1380 MHz
1755 MHz
TDP
250-300W
250W
Process Node
TSMC 12nm
TSMC 8nm
Data Formats
FP16, FP32, FP64
INT8, BF16, FP16, TF32, FP32

Compare Average On-Demand Pricing

V100A16
1 GPU
$1.36 /hr
$0.51 /hr
2 GPUs
$0.78 /hr
$1.02 /hr
4 GPUs
N/A
$2.05 /hr
8 GPUs
$4.72 /hr
$4.09 /hr

Frequently Asked Questions: V100 vs A16

The main differences are VRAM (16 GB vs 64 GB), FP16 throughput (28.26 vs 4.493 TFLOPS), architecture (Volta vs Ampere). The V100 uses the Volta architecture while the A16 is based on Ampere, giving each GPU different generational capabilities.

The V100 is generally better for large language model training due to its higher throughput and 16 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 A16 starts from $0.51/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.

The A16 has more VRAM at 64 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 A16, paying the premium may be justified by faster job completion and lower total cost.

The V100 is currently available across 3 cloud providers on Shadeform's network, compared to 1 for the A16. 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 A16. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore V100 & A16 Instances

Browse available instances with V100 and A16 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

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