Specifications Compared
| Spec | GB300 | GTX-1070 |
|---|---|---|
| TDP | 1400W | 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
Raw compute reveals stark disparities: the GB300 SXM6 delivers 2250 TFLOPS in FP16 for accelerated AI training and inference, dwarfing the GTX 1070 Ti's 11.2 TFLOPS. This FP16 to FP32 ratio on the GB300 SXM6, 2250 TFLOPS versus 90 TFLOPS, optimizes half-precision workflows common in deep learning, enabling faster model convergence on massive datasets. The GTX 1070 Ti maintains parity at 11.2 TFLOPS for both precisions, suitable only for legacy single-precision tasks. Memory bandwidth defines practical limits: the GB300 SXM6's 12000 GB/s supports enormous batch sizes in LLM training, preventing out-of-memory errors for models exceeding 8 GB, the GTX 1070 Ti's limit. In inference, the GB300 SXM6's 4500 TFLOPS FP8 throughput handles high-volume queries at scale, while the 1070 Ti struggles with even modest loads due to 256 GB/s bandwidth. Power efficiency diverges too: the GB300 SXM6's 1400W TDP suits rack-scale deployments, contrasting the 1070 Ti's 180W for desktop use.
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When to Choose the GB300 SXM6
The NVIDIA GB300 SXM6 excels in large-scale AI workloads. Its 288 GB HBM3e VRAM and 12000 GB/s bandwidth enable training of trillion-parameter LLMs without model sharding. Datacenter environments leverage its NVLink interconnect and 2250 TFLOPS FP16 for distributed computing at hyperscale.
When to Choose the GTX 1070 Ti
The NVIDIA GeForce GTX 1070 Ti suits budget-conscious legacy applications. Its 180W TDP and PCIe form factor fit consumer desktops for light gaming or basic ML prototyping within 8 GB VRAM constraints. Enthusiasts prefer it for cost-effective tasks avoiding the GB300 SXM6's 1400W power demands.
Use Cases
The GB300 SXM6's 288 GB HBM3e VRAM and 2250 TFLOPS FP16 support massive batch sizes and rapid convergence for trillion-parameter models. The GTX 1070 Ti's 8 GB limit prevents such scales.
4500 TFLOPS FP8 on the GB300 SXM6 enables high-throughput serving of large models. The GTX 1070 Ti's 11.2 TFLOPS FP16 cannot handle production inference volumes.
12000 GB/s bandwidth on the GB300 SXM6 accelerates gradient computations for billion-parameter fine-tunes. The 1070 Ti's 256 GB/s bottlenecks even smaller adapters.
The GB300 SXM6's 288 GB VRAM fits full diffusion models without quantization. GTX 1070 Ti requires heavy optimizations due to 8 GB constraints.
90 TFLOPS FP32 and NVSwitch on the GB300 SXM6 scale simulations across nodes. The 1070 Ti's 11.2 TFLOPS suits only single-node serial tasks.
Frequently Asked Questions
How much more VRAM does the GB300 SXM6 have than the GTX 1070 Ti?▾
The GB300 SXM6 provides 288 GB HBM3e VRAM compared to the GTX 1070 Ti's 8 GB GDDR5. This 36 times greater capacity allows loading enormous models without splitting. Datacenter tasks benefit immensely from this difference.
What is the FP16 performance gap between these GPUs?▾
The GB300 SXM6 achieves 2250 TFLOPS FP16, while the GTX 1070 Ti reaches 11.2 TFLOPS. This over 200-fold advantage speeds AI training dramatically. Inference latencies drop proportionally on the newer architecture.
Which GPU has higher memory bandwidth?▾
The GB300 SXM6 offers 12000 GB/s, vastly exceeding the GTX 1070 Ti's 256 GB/s. Larger batch sizes become feasible, reducing training epochs. Data movement no longer bottlenecks modern workflows.
What are the TDP ratings for GB300 SXM6 and GTX 1070 Ti?▾
The GB300 SXM6 consumes 1400W, suited for enterprise cooling, versus the GTX 1070 Ti's 180W for standard desktops. Power scales with performance density. Efficiency per watt favors the GB300 SXM6 in datacenters.
Can the GTX 1070 Ti handle LLM inference?▾
The GTX 1070 Ti's 8 GB VRAM limits it to tiny distilled models at 11.2 TFLOPS FP16. The GB300 SXM6's 288 GB and 4500 TFLOPS FP8 serve production-scale LLMs. Legacy use requires quantization tradeoffs.
What architectures power these GPUs?▾
Blackwell Ultra from 2025 drives the GB300 SXM6 with NVLink support. Pascal from 2017 powers the GTX 1070 Ti via PCIe. The nine-year gap explains compute disparities like 90 TFLOPS FP32 versus 11.2 TFLOPS.
Which is cheaper to rent, the GB300 or the GTX 1070?▾
Cloud rental prices for both the GB300 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 GB300 have compared to the GTX 1070?▾
The GB300 has 288 GB of HBM3e memory. The GTX 1070 has 8 GB of GDDR5 memory.
Can I find GB300 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 GB300 and the GTX 1070?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the GTX 1070 uses Pascal (2016). The GB300 delivers 346.2x the FP16 throughput and 46.9x the memory bandwidth of the GTX 1070.