V100 vs RTX 6000 Ada
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.
RTX 6000 Ada
The NVIDIA RTX 6000 Ada delivers high-performance computing capabilities for AI, machine learning, and data science applications.
V100 vs RTX 6000 Ada: Which Should You Choose?
The RTX 6000 Ada offers 48 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 RTX 6000 Ada delivers 91.06 TFLOPS versus 28.26 TFLOPS on the V100 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 6000 Ada at 0.96 TB/s compared to 0.90 TB/s on the V100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the V100 is built on Volta while the RTX 6000 Ada uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the V100 starts from $0.39/hr versus $0.97/hr for the RTX 6000 Ada — 149% more expensive — reflecting the performance premium. The RTX 6000 Ada is available across 4 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
RTX 6000 Ada — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 6000 Ada when:
- ✓You need 48 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 & RTX 6000 Ada compare
Compare detailed hardware specifications and average pricing for the V100 and RTX 6000 Ada.
Compare Hardware Specifications
| V100 | RTX 6000 Ada | |
|---|---|---|
| GPU Type | V100 | RTX 6000 Ada |
| VRAM per GPU | 16 GB | 48 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Volta | Ada Lovelace |
| Interconnect | PCIe Gen3 | PCIe Gen4 |
| Memory Bandwidth | 900 GB/s | 960 GB/s |
| FP16 TFLOPS | 28.26 TFLOPS (2:1) | 91.06 TFLOPS (1:1) |
| CUDA Cores | 5120 | 18176 |
| Tensor Cores | 640 (1st Gen) | 568 (4th Gen) |
| RT Cores | N/A | 142 (3rd Gen) |
| Base Clock | 1230 MHz | 915 MHz |
| Boost Clock | 1380 MHz | 2505 MHz |
| TDP | 250-300W | 300W |
| Process Node | TSMC 12nm | TSMC 4N |
| Data Formats | FP16, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| V100 | RTX 6000 Ada | |
|---|---|---|
| 1 GPU | $1.36 /hr | $1.20 /hr |
| 2 GPUs | $0.78 /hr | $2.04 /hr |
| 4 GPUs | N/A | $3.88 /hr |
| 8 GPUs | $3.76 /hr | $7.01 /hr |
Frequently Asked Questions: V100 vs RTX 6000 Ada
The main differences are VRAM (16 GB vs 48 GB), FP16 throughput (28.26 vs 91.06 TFLOPS), architecture (Volta vs Ada Lovelace). The V100 uses the Volta architecture while the RTX 6000 Ada is based on Ada Lovelace, giving each GPU different generational capabilities.
The RTX 6000 Ada is generally better for large language model training due to its higher throughput and 48 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 6000 Ada starts from $0.97/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The RTX 6000 Ada has more VRAM at 48 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 RTX 6000 Ada 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 RTX 6000 Ada is currently available across 4 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 RTX 6000 Ada. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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