Specifications Compared
| Spec | GTX-1070 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 150W | 260W |
| VRAM | 8 GB | 48 GB |
| CUDA Cores | 1,920 | 4,608 |
| Memory Type | GDDR5 | GDDR6 |
| Architecture | Pascal | Turing |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | |
| FP16 Performance | 6.5 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 6.5 TFLOPS | 16.3 TFLOPS |
| Memory Bandwidth | 256 GB/s | 672 GB/s |
Performance Analysis
The Quadro RTX 8000 outperforms the GTX 1070 significantly in compute capabilities: 16.3 TFLOPS FP32 versus 6.5 TFLOPS translates to approximately 2.5 times faster processing for machine learning training and inference tasks. This FP16 and FP32 parity in both GPUs supports mixed-precision workflows, but the Quadro's higher throughput accelerates model convergence and reduces training times for deep learning models.
Memory specifications create the largest real-world disparity. The 48 GB GDDR6 VRAM on the Quadro RTX 8000 enables handling of large language models or datasets that exceed the GTX 1070's 8 GB GDDR5 limit, preventing out-of-memory errors during training. Coupled with 672 GB/s bandwidth versus 256 GB/s, the Quadro supports larger batch sizes without bottlenecks, improving utilization in inference pipelines and allowing 2.6 times faster data movement.
Power and interconnect differences further influence deployment. The Quadro's 260 W TDP demands robust cooling compared to 150 W, while NVLink enables multi-GPU scaling unavailable on the GTX 1070, enhancing distributed training efficiency.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
No live offers available at this time.
When to Choose the GTX 1070
The GTX 1070 suits budget-limited scenarios with light computational demands. Its 8 GB GDDR5 VRAM and 6.5 TFLOPS FP32 performance handle small-scale inference or gaming at 1080p resolutions effectively, while the 150 W TDP minimizes power costs in home setups or older servers.
Choose it for legacy applications where 256 GB/s bandwidth suffices and upgrades are unnecessary, such as basic Stable Diffusion runs with low-resolution outputs.
When to Choose the Quadro RTX 8000
The Quadro RTX 8000 excels in professional environments requiring extensive memory and performance. Its 48 GB GDDR6 VRAM accommodates large models in AI training, and 16.3 TFLOPS FP32 with 672 GB/s bandwidth supports high-batch workloads without constraints.
Opt for it in multi-GPU configurations via NVLink, ideal for scientific simulations or enterprise visualization demanding the 260 W TDP's sustained output.
Use Cases
LLM training requires substantial VRAM for large models: the Quadro RTX 8000's 48 GB GDDR6 far exceeds the GTX 1070's 8 GB GDDR5. Its 16.3 TFLOPS FP32 performance doubles training speed over 6.5 TFLOPS.
High memory bandwidth of 672 GB/s on the Quadro RTX 8000 supports larger batch sizes for efficient inference, unlike the GTX 1070's 256 GB/s limit. The 48 GB VRAM handles full model loading without swapping.
Fine-tuning benefits from the Quadro RTX 8000's 16.3 TFLOPS FP16 for faster iterations compared to 6.5 TFLOPS on GTX 1070. Ample 48 GB VRAM prevents memory constraints on mid-sized models.
Stable Diffusion image generation scales with VRAM: 48 GB on Quadro RTX 8000 enables high-resolution outputs and batch processing, surpassing GTX 1070's 8 GB capacity.
Scientific simulations demand high throughput: Quadro RTX 8000's 672 GB/s bandwidth and NVLink interconnect outperform GTX 1070's PCIe-only 256 GB/s for data-intensive computations.
Frequently Asked Questions
What is the VRAM difference between GTX 1070 and Quadro RTX 8000?▾
The GTX 1070 has 8 GB GDDR5 VRAM, while the Quadro RTX 8000 offers 48 GB GDDR6. This sixfold increase allows the Quadro to manage much larger datasets and models without memory errors. It directly impacts tasks like training deep neural networks.
Which GPU has higher compute performance?▾
The Quadro RTX 8000 delivers 16.3 TFLOPS in FP32, compared to the GTX 1070's 6.5 TFLOPS. This 2.5 times advantage accelerates machine learning workloads significantly. FP16 performance matches at the same rates for both.
How do power requirements compare?▾
The GTX 1070 requires 150 W TDP, lower than the Quadro RTX 8000's 260 W. Lower power suits energy-sensitive deployments, but the Quadro provides superior performance for demanding tasks. Cooling needs scale accordingly.
What architectures do they use?▾
GTX 1070 uses Pascal from 2016, while Quadro RTX 8000 employs Turing from 2018. Turing introduces tensor cores implied in the FP16 specs, enhancing AI efficiency over Pascal. This generational gap affects modern software compatibility.
Is NVLink supported?▾
The Quadro RTX 8000 includes NVLink for multi-GPU connectivity, absent on the GTX 1070. This enables faster inter-GPU communication at high bandwidths for scaled training. PCIe alone limits GTX 1070 in clusters.
Which is better for AI workloads?▾
Quadro RTX 8000 outperforms with 672 GB/s bandwidth versus 256 GB/s and 48 GB VRAM versus 8 GB. It handles larger batches and models critical for AI. GTX 1070 fits only lightweight inference.
Which is cheaper to rent, the GTX 1070 or the Quadro RTX 8000?▾
Cloud rental prices for both the GTX 1070 and Quadro RTX 8000 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the GTX 1070 have compared to the Quadro RTX 8000?▾
The GTX 1070 has 8 GB of GDDR5 memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find GTX 1070 and Quadro RTX 8000 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the GTX 1070 and the Quadro RTX 8000?▾
The GTX 1070 uses the Pascal architecture (2016) while the Quadro RTX 8000 uses Turing (2018). The Quadro RTX 8000 delivers 2.5x the FP16 throughput and 2.6x the memory bandwidth of the GTX 1070.