Specifications Compared
| Spec | B200 | GTX-1070 |
|---|---|---|
| TDP | 1000W | 150W |
| VRAM | 192 GB | 8 GB |
| CUDA Cores | 18,432 | 1,920 |
| Memory Type | HBM3e | GDDR5 |
| Architecture | Blackwell | Pascal |
| Form Factors | SXM, NVL | PCIe |
| Interconnect | NVLink, PCIe 6.0, InfiniBand | |
| Tensor Cores | 576 | |
| FP8 Performance | 9,000 TFLOPS | |
| FP16 Performance | 4,500 TFLOPS | 6.5 TFLOPS |
| FP32 Performance | 90 TFLOPS | 6.5 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 9,000 TOPS | |
| Memory Bandwidth | 8,000 GB/s | 256 GB/s |
Performance Analysis
Compute peaks reveal overwhelming B200 dominance: 4500 TFLOPS FP16 enables training large language models 399 times faster than the GTX 1070 Ti's 11.3 TFLOPS, critical for deep learning where half-precision dominates. FP32 at 90 TFLOPS provides eightfold speedup over 11.3 TFLOPS for simulation tasks, while B200's FP8 at 9000 TFLOPS accelerates inference on quantized models unavailable to Pascal hardware.
Memory specs dictate real-world scalability: 192 GB HBM3e versus 8 GB GDDR5X restricts 1070 Ti to tiny batch sizes, often under 1 for modern LLMs, whereas 8000 GB/s bandwidth on B200 sustains massive throughput without stalling. This gap manifests in training as hours-per-epoch on 1070 Ti scaling to minutes on B200 for equivalent work. TDP disparity, 1000W to 180W, suits B200 for rack-scale deployments but limits 1070 Ti to desktops.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B200 SXM
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Nebius | NVIDIA B200 SXM 192GB VRAM | 192GB | 20 vCPU 224GB RAM | 🌍Europe | $3.95/GPU/hr | |||
 Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $4.79/GPU/hr $38.32/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.39/GPU/hr $43.12/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.69/GPU/hr $45.52/hr total (8×) | |||
 RunPod | NVIDIA B200 SXM 192GB VRAM | 192GB | 28 vCPU 283GB RAM | California | $5.89/GPU/hr |
When to Choose the B200 SXM
Select the B200 SXM for professional AI pipelines: its 4500 TFLOPS FP16 and 192 GB VRAM handle full LLM training or fine-tuning of models exceeding 70B parameters, impossible on 8 GB cards. Multi-GPU inference thrives via NVLink and 9000 TFLOPS FP8, with cloud pricing from $1.71/hr enabling scalable clusters.
Enterprise scientific computing demands B200's 8000 GB/s bandwidth for large simulations, far beyond GTX 1070 Ti capabilities.
When to Choose the GTX 1070 Ti
The GTX 1070 Ti suits budget desktop gaming or hobbyist tinkering: 11.3 TFLOPS FP32 powers light Stable Diffusion generations at 512x512 resolution, and 180W TDP fits standard PCs without high power bills. Local ownership avoids cloud costs, ideal for non-time-critical tasks.
Legacy software or casual ML prototyping favors its PCIe simplicity over B200's datacenter requirements.
Use Cases
B200's 4500 TFLOPS FP16 and 192 GB VRAM support massive batch sizes for billion-parameter models. GTX 1070 Ti's 11.3 TFLOPS and 8 GB VRAM cannot handle such scales.
9000 TFLOPS FP8 on B200 accelerates high-throughput serving. 1070 Ti lacks FP8 and sufficient VRAM for production loads.
90 TFLOPS FP32 and 8000 GB/s bandwidth enable efficient LoRA on large models. 1070 Ti limits to small adapters due to 8 GB constraint.
GTX 1070 Ti's 11.3 TFLOPS FP32 generates images at low resolutions adequately for hobby use. B200 overkill for single-user creative tasks.
B200's 90 TFLOPS FP32 and NVLink scale complex simulations across nodes. 1070 Ti's 11.3 TFLOPS suits trivial serial jobs only.
Frequently Asked Questions
What is the VRAM difference between B200 SXM and GTX 1070 Ti?▾
B200 SXM provides 192 GB HBM3e VRAM, 24 times more than the GTX 1070 Ti's 8 GB GDDR5X. This enables B200 to load enormous models intact. GTX 1070 Ti requires heavy quantization or offloading for mid-size AI tasks.
How does memory bandwidth compare?▾
B200 SXM offers 8000 GB/s, over 22 times the GTX 1070 Ti's 352 GB/s. Higher bandwidth reduces data starvation in training. 1070 Ti bottlenecks on large batches.
Which has better FP16 performance for AI?▾
B200 SXM reaches 4500 TFLOPS FP16, 399 times the GTX 1070 Ti's 11.3 TFLOPS. This translates to drastically faster neural net training. 1070 Ti manages basic prototypes at best.
What are the power requirements?▾
B200 SXM demands 1000W TDP for datacenter use. GTX 1070 Ti uses 180W, suitable for consumer PCs. B200 requires robust cooling infrastructure.
Is GTX 1070 Ti available on cloud like B200?▾
No live cloud offers exist for GTX 1070 Ti. B200 SXM starts at $1.71/hr, averaging $4.60/hr across 13 providers. Use 1070 Ti locally if owned.
Can GTX 1070 Ti handle modern AI tasks?▾
GTX 1070 Ti's 8 GB VRAM and 11.3 TFLOPS FP32 limit it to small models or low-res inference. B200 SXM excels in production AI with 192 GB and 4500 TFLOPS FP16.
Which is cheaper to rent, the B200 or the GTX 1070?▾
Cloud rental prices for both the B200 and GTX 1070 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 B200 have compared to the GTX 1070?▾
The B200 has 192 GB of HBM3e memory. The GTX 1070 has 8 GB of GDDR5 memory.
Can I find B200 and GTX 1070 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 B200 and the GTX 1070?▾
The B200 uses the Blackwell architecture (2024) while the GTX 1070 uses Pascal (2016). The B200 delivers 692.3x the FP16 throughput and 31.3x the memory bandwidth of the GTX 1070.