V100 vs A4000
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.
A4000
The NVIDIA A4000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
V100 vs A4000: Which Should You Choose?
Both the V100 and A4000 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 V100 is built on Volta while the A4000 uses Ampere, 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.
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
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
See how the V100 & A4000 compare
Compare detailed hardware specifications and average pricing for the V100 and A4000.
Compare Hardware Specifications
| V100 | A4000 | |
|---|---|---|
| GPU Type | V100 | A4000 |
| VRAM per GPU | 16 GB | 16 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Volta | Ampere |
| Interconnect | PCIe Gen3 | PCIe Gen4 |
| Memory Bandwidth | 900 GB/s | 448 GB/s |
| FP16 TFLOPS | 28.26 TFLOPS (2:1) | 19.17 TFLOPS (1:1) |
| CUDA Cores | 5120 | 6144 |
| Tensor Cores | 640 (1st Gen) | 192 (3rd Gen) |
| RT Cores | N/A | 48 (2nd Gen) |
| Base Clock | 1230 MHz | 735 MHz |
| Boost Clock | 1380 MHz | 1695 MHz |
| TDP | 250-300W | 140W |
| Process Node | TSMC 12nm | TSMC 8nm |
| Data Formats | FP16, FP32, FP64 | INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| V100 | A4000 | |
|---|---|---|
| 1 GPU | $1.36 /hr | $0.47 /hr |
| 2 GPUs | $0.78 /hr | $0.95 /hr |
| 4 GPUs | N/A | $1.90 /hr |
| 8 GPUs | $3.76 /hr | $1.20 /hr |
Frequently Asked Questions: V100 vs A4000
The main differences are FP16 throughput (28.26 vs 19.17 TFLOPS), architecture (Volta vs Ampere). The V100 uses the Volta architecture while the A4000 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 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 V100 or A4000. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore V100 & A4000 Instances
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