A4000 vs V100
Explore a head to head comparison of specifications, performance, and pricing.
A4000
The NVIDIA A4000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
V100
The NVIDIA V100 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A4000 vs V100: Which Should You Choose?
Both the A4000 and V100 offer 16 GB of VRAM, putting them on equal footing for memory-bound workloads. On FP16 throughput, the V100 delivers 28.26 TFLOPS versus 19.17 TFLOPS on the A4000 — 1.5× faster for mixed-precision training and inference. Memory bandwidth favors the V100 at 0.90 TB/s compared to 0.45 TB/s on the A4000, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A4000 is built on Ampere while the V100 uses Volta, reflecting different generational capabilities and optimizations. On Shadeform, the A4000 starts from $0.15/hr versus $0.39/hr for the V100 — 160% more expensive — reflecting the performance premium. The V100 is available across 3 cloud providers on Shadeform compared to 2 for the A4000, giving more options for region and pricing flexibility.
A4000 — 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 A4000 when:
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
V100 — Best Use Cases
- •Deep learning training
- •HPC and scientific computing
- •Legacy ML infrastructure
Choose V100 when:
- ✓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 A4000 & V100 compare
Compare detailed hardware specifications and average pricing for the A4000 and V100.
Compare Hardware Specifications
| A4000 | V100 | |
|---|---|---|
| GPU Type | A4000 | V100 |
| VRAM per GPU | 16 GB | 16 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Volta |
| Interconnect | PCIe Gen4 | PCIe Gen3 |
| Memory Bandwidth | 448 GB/s | 900 GB/s |
| FP16 TFLOPS | 19.17 TFLOPS (1:1) | 28.26 TFLOPS (2:1) |
| CUDA Cores | 6144 | 5120 |
| Tensor Cores | 192 (3rd Gen) | 640 (1st Gen) |
| RT Cores | 48 (2nd Gen) | N/A |
| Base Clock | 735 MHz | 1230 MHz |
| Boost Clock | 1695 MHz | 1380 MHz |
| TDP | 140W | 250-300W |
| Process Node | TSMC 8nm | TSMC 12nm |
| Data Formats | INT8, BF16, FP16, TF32, FP32 | FP16, FP32, FP64 |
Compare Average On-Demand Pricing
| A4000 | V100 | |
|---|---|---|
| 1 GPU | $0.47 /hr | $1.36 /hr |
| 2 GPUs | $0.95 /hr | $0.78 /hr |
| 4 GPUs | $1.90 /hr | $1.56 /hr |
| 8 GPUs | $1.20 /hr | $4.72 /hr |
Frequently Asked Questions: A4000 vs V100
The main differences are FP16 throughput (19.17 vs 28.26 TFLOPS), architecture (Ampere vs Volta). The A4000 uses the Ampere architecture while the V100 is based on Volta, 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 A4000 is available from $0.15/hr. The V100 starts from $0.39/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
Based on TFLOPS per dollar, the A4000 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 V100, 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 2 for the A4000. 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 A4000 or V100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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