RTX 6000 vs RTX 6000 Ada
Explore a head to head comparison of specifications, performance, and pricing.
RTX 6000
The NVIDIA RTX 6000 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.
RTX 6000 vs RTX 6000 Ada: Which Should You Choose?
The RTX 6000 Ada offers 48 GB of VRAM — 2× the 24 GB on the RTX 6000 — 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 32.62 TFLOPS on the RTX 6000 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 6000 Ada at 0.96 TB/s compared to 0.67 TB/s on the RTX 6000, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 6000 is built on Turing while the RTX 6000 Ada uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 6000 starts from $0.50/hr versus $0.97/hr for the RTX 6000 Ada — 94% more expensive — reflecting the performance premium. The RTX 6000 Ada is available across 4 cloud providers on Shadeform compared to 1 for the RTX 6000, giving more options for region and pricing flexibility.
RTX 6000 — Best Use Cases
- •Inference and model serving
- •Light training and fine-tuning
- •Graphics and rendering workloads
Choose RTX 6000 when:
- ✓24 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 RTX 6000 & RTX 6000 Ada compare
Compare detailed hardware specifications and average pricing for the RTX 6000 and RTX 6000 Ada.
Compare Hardware Specifications
| RTX 6000 | RTX 6000 Ada | |
|---|---|---|
| GPU Type | RTX 6000 | RTX 6000 Ada |
| VRAM per GPU | 24 GB | 48 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Turing | Ada Lovelace |
| Interconnect | PCIe Gen3 | PCIe Gen4 |
| Memory Bandwidth | 672 GB/s | 960 GB/s |
| FP16 TFLOPS | 32.62 TFLOPS (2:1) | 91.06 TFLOPS (1:1) |
| CUDA Cores | 4608 | 18176 |
| Tensor Cores | 576 (2nd Gen) | 568 (4th Gen) |
| RT Cores | 72 (1st Gen) | 142 (3rd Gen) |
| Base Clock | 1440 MHz | 915 MHz |
| Boost Clock | 1770 MHz | 2505 MHz |
| TDP | 295W | 300W |
| Process Node | TSMC 12nm | TSMC 4N |
| Data Formats | INT8, INT4, FP16, FP32 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| RTX 6000 | RTX 6000 Ada | |
|---|---|---|
| 1 GPU | $0.50 /hr | $1.20 /hr |
| 2 GPUs | N/A | $2.04 /hr |
| 4 GPUs | N/A | $3.88 /hr |
| 8 GPUs | N/A | $7.01 /hr |
Frequently Asked Questions: RTX 6000 vs RTX 6000 Ada
The main differences are VRAM (24 GB vs 48 GB), FP16 throughput (32.62 vs 91.06 TFLOPS), architecture (Turing vs Ada Lovelace). The RTX 6000 uses the Turing 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 RTX 6000 is available from $0.50/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 24 GB on the RTX 6000. 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 RTX 6000, 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 1 for the RTX 6000. 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 6000 or RTX 6000 Ada. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore RTX 6000 & RTX 6000 Ada Instances
Browse available instances with RTX 6000 and RTX 6000 Ada GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
Explore more GPU comparisons
Select any two GPUs to compare their specifications and explore pricing across providers.
