RTX 4000 Ada vs V100
Explore a head to head comparison of specifications, performance, and pricing.
RTX 4000 Ada
The NVIDIA RTX 4000 Ada 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.
RTX 4000 Ada vs V100: Which Should You Choose?
The RTX 4000 Ada offers 20 GB of VRAM — 1.3× 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 V100 delivers 28.26 TFLOPS versus 26.73 TFLOPS on the RTX 4000 Ada — 6% more faster for mixed-precision training and inference. Memory bandwidth favors the V100 at 0.90 TB/s compared to 0.36 TB/s on the RTX 4000 Ada, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 4000 Ada is built on Ada Lovelace while the V100 uses Volta, reflecting different generational capabilities and optimizations. On Shadeform, the V100 starts from $0.39/hr versus $0.79/hr for the RTX 4000 Ada — 103% more expensive — reflecting the performance premium. The V100 is available across 3 cloud providers on Shadeform compared to 1 for the RTX 4000 Ada, giving more options for region and pricing flexibility.
RTX 4000 Ada — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4000 Ada when:
- ✓You need 20 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
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
See how the RTX 4000 Ada & V100 compare
Compare detailed hardware specifications and average pricing for the RTX 4000 Ada and V100.
Compare Hardware Specifications
| RTX 4000 Ada | V100 | |
|---|---|---|
| GPU Type | RTX 4000 Ada | V100 |
| VRAM per GPU | 20 GB | 16 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ada Lovelace | Volta |
| Interconnect | PCIe Gen4 | PCIe Gen3 |
| Memory Bandwidth | 360 GB/s | 900 GB/s |
| FP16 TFLOPS | 26.73 TFLOPS (1:1) | 28.26 TFLOPS (2:1) |
| CUDA Cores | 6144 | 5120 |
| Tensor Cores | 192 (4th Gen) | 640 (1st Gen) |
| RT Cores | 48 (3rd Gen) | N/A |
| Base Clock | 1500 MHz | 1230 MHz |
| Boost Clock | 2175 MHz | 1380 MHz |
| TDP | 130W | 250-300W |
| Process Node | TSMC 4N | TSMC 12nm |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32 | FP16, FP32, FP64 |
Compare Average On-Demand Pricing
| RTX 4000 Ada | V100 | |
|---|---|---|
| 1 GPU | $0.79 /hr | $1.36 /hr |
| 2 GPUs | N/A | $0.78 /hr |
| 4 GPUs | N/A | $1.56 /hr |
| 8 GPUs | N/A | $4.72 /hr |
Frequently Asked Questions: RTX 4000 Ada vs V100
The main differences are VRAM (20 GB vs 16 GB), FP16 throughput (26.73 vs 28.26 TFLOPS), architecture (Ada Lovelace vs Volta). The RTX 4000 Ada uses the Ada Lovelace 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 V100 is available from $0.39/hr. The RTX 4000 Ada starts from $0.79/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The RTX 4000 Ada has more VRAM at 20 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 RTX 4000 Ada, 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 RTX 4000 Ada. 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 RTX 4000 Ada or V100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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