Specifications Compared
| Spec | QUADRO-RTX-6000 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 260W | 260W |
| VRAM | 24 GB | 48 GB |
| CUDA Cores | 4,608 | 4,608 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Turing | Turing |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | NVLink |
| Tensor Cores | 576 | 576 |
| FP16 Performance | 16.3 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 16.3 TFLOPS | 16.3 TFLOPS |
| Memory Bandwidth | 672 GB/s | 672 GB/s |
Performance Analysis
Compute performance remains identical between the Quadro RTX 6000 and Quadro RTX 8000 at 16.3 TFLOPS FP16 and 16.3 TFLOPS FP32, ensuring equivalent throughput for training and inference in mixed-precision workflows. The FP16 and FP32 parity supports efficient half-precision training without sacrificing single-precision accuracy, common in deep learning pipelines.
The standout difference is VRAM: 24 GB on the 6000 versus 48 GB on the 8000. This allows the 8000 to accommodate models requiring over 24 GB, avoiding out-of-memory errors during large-batch training or high-resolution inference. Memory bandwidth matches at 672 GB/s, so data transfer rates do not favor one over the other.
In practice, the 8000 enables larger batch sizes in memory-bound scenarios, reducing iteration overhead and accelerating convergence in training loops. For inference, it supports broader concurrent requests, enhancing throughput for deployment servers.
Live Cloud Pricing
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When to Choose the Quadro RTX 6000
The Quadro RTX 6000 suits scenarios where 24 GB GDDR6 VRAM meets requirements, such as fine-tuning mid-sized models under 20 GB or rendering scenes with moderate texture datasets. Its 260W TDP and 16.3 TFLOPS FP32 performance deliver full capability without excess capacity, ideal for cost-conscious workstations.
Select this GPU for PCIe-based single-node setups handling Stable Diffusion at 512x512 resolutions or scientific computing with datasets fitting within 24 GB, ensuring no waste on unused memory.
When to Choose the Quadro RTX 8000
Opt for the Quadro RTX 8000 when workloads demand 48 GB GDDR6 VRAM, like training large language models exceeding 24 GB or high-resolution 3D simulations. The doubled capacity supports bigger batch sizes at 672 GB/s bandwidth, minimizing padding overhead.
This model excels in NVLink-connected multi-GPU environments for distributed training, where its 16.3 TFLOPS FP16 aligns with memory-heavy inference deployments serving extensive concurrent users.
Use Cases
The 48 GB VRAM on the Quadro RTX 8000 handles larger models and batch sizes without splitting, unlike the 24 GB limit on the 6000. This reduces training time through fewer iterations.
48 GB VRAM supports higher concurrent requests and bigger batches at 672 GB/s bandwidth. The 6000's 24 GB restricts scale for production deployments.
Both offer 16.3 TFLOPS FP16/FP32 for mid-sized models under 24 GB. Choose the 8000 only if datasets exceed the 6000's VRAM capacity.
48 GB VRAM enables higher resolutions and complex prompts without swapping. The 6000 limits to lower settings due to 24 GB constraint.
24 GB VRAM suffices for most simulations fitting within that limit, matching the 8000's 16.3 TFLOPS FP32. Excess capacity on the 8000 adds unnecessary overhead.
Frequently Asked Questions
What is the VRAM difference between Quadro RTX 6000 and 8000?▾
The Quadro RTX 6000 has 24 GB GDDR6 VRAM, while the Quadro RTX 8000 doubles it to 48 GB GDDR6. This impacts handling of large models in AI workloads. Both share 672 GB/s bandwidth.
Do the Quadro RTX 6000 and 8000 have the same performance?▾
Yes, both deliver 16.3 TFLOPS FP16 and 16.3 TFLOPS FP32. Compute speeds match, but VRAM differentiates memory-bound tasks. TDP is identical at 260W.
Which GPU supports NVLink?▾
Both the Quadro RTX 6000 and 8000 feature NVLink interconnect for multi-GPU scaling. This aids distributed computing alongside PCIe form factor. VRAM remains 24 GB versus 48 GB.
Are there rental offers for these GPUs?▾
No live offers exist currently for the Quadro RTX 6000 or 8000 on gpuperhour.com. Check back for updates on Turing-era professional GPUs. Specs include 260W TDP each.
What architecture do Quadro RTX 6000 and 8000 use?▾
Both utilize NVIDIA's Turing architecture from 2018. They provide 16.3 TFLOPS FP32 performance with differences only in VRAM capacity. Memory bandwidth is 672 GB/s.
Is the Quadro RTX 8000 better for large models?▾
Yes, its 48 GB VRAM outperforms the 6000's 24 GB for models over 24 GB. Matching 16.3 TFLOPS ensures no speed loss in eligible workloads. Ideal for training and inference.
Which is cheaper to rent, the Quadro RTX 6000 or the Quadro RTX 8000?▾
Cloud rental prices for both the Quadro RTX 6000 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 Quadro RTX 6000 have compared to the Quadro RTX 8000?▾
The Quadro RTX 6000 has 24 GB of GDDR6 memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find Quadro RTX 6000 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 Quadro RTX 6000 and the Quadro RTX 8000?▾
The Quadro RTX 6000 uses the Turing architecture (2018) while the Quadro RTX 8000 uses Turing (2018). The Quadro RTX 8000 delivers 1.0x the FP16 throughput and 1.0x the memory bandwidth of the Quadro RTX 6000.