Specifications Compared
| Spec | B300 | GTX-1070 |
|---|---|---|
| TDP | 1200W | 150W |
| VRAM | 288 GB | 8 GB |
| Memory Type | HBM3e | GDDR5 |
| Architecture | Blackwell Ultra | Pascal |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 6.5 TFLOPS |
| FP32 Performance | 90 TFLOPS | 6.5 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 256 GB/s |
Performance Analysis
Compute performance defines the core disparity: B300's 2250 TFLOPS FP16 enables training large language models at speeds unattainable on GTX 1070's 6.5 TFLOPS, reducing epochs from days to hours for billion-parameter models. The FP32 gap, 90 TFLOPS versus 6.5 TFLOPS, accelerates scientific computing tasks like fluid dynamics simulations requiring precise single-precision arithmetic. FP8 capability at 4500 TFLOPS on B300 optimizes inference for quantized models, slashing latency in production deployments.
Memory specifications transform real-world usability. B300's 12000 GB/s bandwidth sustains massive batch sizes during training, processing terabytes of data per second without bottlenecks, while GTX 1070's 256 GB/s restricts batches to hundreds of samples. The 288 GB VRAM on B300 loads entire 100B+ parameter models, avoiding multi-GPU sharding; GTX 1070's 8 GB forces model parallelism or downsizing, crippling efficiency for contemporary workloads.
Power and form factor implications follow: B300's 1200W TDP suits rack-scale SXM deployments with NVLink, scaling to clusters, whereas GTX 1070's 150W PCIe fits desktops but lacks interconnects for multi-GPU coherence.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA B300 SXM6 262GB VRAM | 262GB | 0 vCPU 0GB RAM | 🌍global | $7.39/GPU/hr | |||
 VERDA | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 240 vCPU 2040GB RAM | Helsinki | $7.50/GPU/hr $60.00/hr total (8×) | Available | ||
 Scaleway | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 224 vCPU 3840GB RAM 22352GB Storage | Paris | $8.73/GPU/hr $69.84/hr total (8×) | Available |
When to Choose the B300
Choose the B300 for AI-driven workloads demanding scale: its 288 GB HBM3e VRAM accommodates full LLM training without partitioning, and 2250 TFLOPS FP16 completes iterations 346 times faster than GTX 1070's 6.5 TFLOPS. Cloud pricing from $2.45 per hour across 7 providers enables flexible scaling for inference at 4500 TFLOPS FP8, ideal for enterprise serving.
Datacenter environments benefit most, leveraging NVSwitch and 12000 GB/s bandwidth for distributed training batches exceeding millions of tokens.
When to Choose the GTX 1070
The GTX 1070 suits budget-conscious local setups for legacy tasks: its 150W TDP runs on standard desktops without infrastructure costs, handling 1080p gaming or basic CUDA scripts at 6.5 TFLOPS FP32. Absence of cloud offers favors owned hardware for non-urgent compute.
Lightweight fine-tuning of models under 1B parameters fits within 8 GB VRAM, avoiding cloud expenses for hobbyists or prototyping.
Use Cases
B300's 288 GB VRAM loads massive models intact, and 2250 TFLOPS FP16 accelerates training 346 times over GTX 1070's 6.5 TFLOPS.
B300's 4500 TFLOPS FP8 and 12000 GB/s bandwidth support high-throughput quantized serving; GTX 1070's 8 GB VRAM limits to tiny models.
B300 handles large datasets with 288 GB VRAM during fine-tuning; GTX 1070 restricts to small models under 8 GB.
B300 generates high-resolution images rapidly via 90 TFLOPS FP32; GTX 1070 manages 512x512 but stalls on larger batches due to 256 GB/s bandwidth.
B300's 90 TFLOPS FP32 outperforms GTX 1070's 6.5 TFLOPS for simulations; 12000 GB/s bandwidth processes large grids efficiently.
Frequently Asked Questions
What is the VRAM difference between B300 and GTX 1070?▾
B300 provides 288 GB HBM3e VRAM, 36 times more than GTX 1070's 8 GB GDDR5. This allows B300 to handle models over 100 GB, while GTX 1070 limits to under 8 GB datasets.
How does memory bandwidth compare?▾
B300 achieves 12000 GB/s, 47 times GTX 1070's 256 GB/s. Higher bandwidth on B300 supports larger training batches without data starvation.
What are the FP16 performance specs?▾
B300 delivers 2250 TFLOPS FP16, versus GTX 1070's 6.5 TFLOPS. This gap speeds AI training significantly on B300.
Is GTX 1070 available on cloud platforms?▾
GTX 1070 has no live cloud offers. B300 starts at $2.45 per hour across 7 providers.
What is the power consumption difference?▾
B300 requires 1200W TDP in SXM form, compared to GTX 1070's 150W PCIe. B300 suits datacenters; GTX 1070 fits consumer PCs.
Can GTX 1070 handle modern AI tasks?▾
GTX 1070's 8 GB VRAM and 6.5 TFLOPS FP16 limit it to small models. B300's 288 GB and 2250 TFLOPS FP16 excel in large-scale AI.
Which is cheaper to rent, the B300 or the GTX 1070?▾
Cloud rental prices for both the B300 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 B300 have compared to the GTX 1070?▾
The B300 has 288 GB of HBM3e memory. The GTX 1070 has 8 GB of GDDR5 memory.
Can I find B300 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 B300 and the GTX 1070?▾
The B300 uses the Blackwell Ultra architecture (2025) while the GTX 1070 uses Pascal (2016). The B300 delivers 346.2x the FP16 throughput and 46.9x the memory bandwidth of the GTX 1070.