Specifications Compared
| Spec | GTX-1070 | MI250X |
|---|---|---|
| TDP | 150W | 560W |
| VRAM | 8 GB | 128 GB |
| CUDA Cores | 1,920 | |
| Memory Type | GDDR5 | HBM2e |
| Architecture | Pascal | CDNA 2 |
| Form Factors | PCIe | OAM |
| Interconnect | Infinity Fabric | |
| FP16 Performance | 6.5 TFLOPS | 383 TFLOPS |
| FP32 Performance | 6.5 TFLOPS | 383 TFLOPS |
| Memory Bandwidth | 256 GB/s | 3,277 GB/s |
Performance Analysis
Compute throughput defines workload viability: the MI250X's 383 TFLOPS in FP16 and FP32 enables rapid training and inference for large models, dwarfing the GTX 1070's 6.5 TFLOPS by a factor of nearly 59. This delta means training sessions that take days on the GTX 1070 complete in minutes on the MI250X, while inference latency drops dramatically for real-time applications.
Memory specifications dictate model scale: 128 GB HBM2e on the MI250X supports massive batch sizes and full-precision large language models, unlike the GTX 1070's 8 GB GDDR5 which limits users to small models or heavy quantization. Bandwidth amplifies this, as 3277 GB/s on the MI250X minimizes data transfer bottlenecks during training, allowing sustained high utilization, compared to 256 GB/s on the GTX 1070 which constrains throughput for memory-intensive tasks like fine-tuning.
Power and form factor influence deployment: the GTX 1070's 150W TDP suits desktop environments, but the MI250X's 560W and Infinity Fabric excel in clustered datacenter inference and scientific simulations requiring interconnect efficiency.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
MI250X
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Cirrascale | 4×AMD Instinct MI250X 128GB VRAM | 128GB | 256 vCPU 1024GB RAM 11882GB Storage | United States | $1.28/GPU/hr $5.12/hr total (4×) | |||
Cirrascale | 4×AMD Instinct MI250X 128GB VRAM | 128GB | 256 vCPU 1024GB RAM 11882GB Storage | United States | $1.44/GPU/hr $5.76/hr total (4×) | |||
Cirrascale | 4×AMD Instinct MI250X 128GB VRAM | 128GB | 256 vCPU 1024GB RAM 11882GB Storage | United States | $1.52/GPU/hr $6.08/hr total (4×) | |||
Cirrascale | 4×AMD Instinct MI250X 128GB VRAM | 128GB | 256 vCPU 1024GB RAM 11882GB Storage | United States | $1.60/GPU/hr $6.40/hr total (4×) |
When to Choose the GTX 1070
The GTX 1070 suits legacy gaming rigs or entry-level local compute where cloud access is unavailable, given its absence of live offers. Its 150W TDP and PCIe form factor enable easy integration into consumer desktops for tasks like basic Stable Diffusion image generation with models fitting in 8 GB GDDR5, avoiding the MI250X's $1.28 per hour cloud costs.
When to Choose the MI250X
The MI250X excels in professional AI and HPC environments demanding high scale, with 128 GB HBM2e VRAM handling large LLMs and 383 TFLOPS FP16/FP32 accelerating training. Cloud pricing from $1.28 per hour across four offers makes it viable for bursty workloads, leveraging 3277 GB/s bandwidth for efficient large-batch inference unavailable on the GTX 1070.
Use Cases
The MI250X's 128 GB HBM2e VRAM and 383 TFLOPS FP16 support full-scale training of large models, while the GTX 1070's 8 GB GDDR5 cannot accommodate model parameters exceeding that limit.
MI250X delivers 383 TFLOPS FP32 with 3277 GB/s bandwidth for low-latency serving of large batches, far surpassing the GTX 1070's 6.5 TFLOPS and 256 GB/s constrained by 8 GB VRAM.
High memory bandwidth of 3277 GB/s and 128 GB VRAM on MI250X enable efficient fine-tuning of billion-parameter models; GTX 1070's 256 GB/s and 8 GB limit it to tiny datasets.
GTX 1070 handles basic image generation in 8 GB VRAM at 6.5 TFLOPS; MI250X accelerates complex pipelines with 383 TFLOPS but may be overkill for single-user tasks.
MI250X's Infinity Fabric interconnect and 383 TFLOPS FP32 optimize simulations; GTX 1070's PCIe and 6.5 TFLOPS lack scalability for large-scale computations.
Frequently Asked Questions
Can the GTX 1070 handle modern AI training?▾
The GTX 1070's 8 GB GDDR5 VRAM and 6.5 TFLOPS FP16 limit it to small models only. Larger training requires more than its 256 GB/s bandwidth can sustain efficiently.
How much faster is MI250X for machine learning?▾
MI250X provides 383 TFLOPS FP16/FP32, approximately 59 times the GTX 1070's 6.5 TFLOPS. This translates to drastically reduced training times for compute-bound tasks.
What is the VRAM difference between GTX 1070 and MI250X?▾
GTX 1070 has 8 GB GDDR5, while MI250X offers 128 GB HBM2e. The MI250X supports models 16 times larger without offloading.
Is MI250X available on cloud platforms?▾
MI250X has live offers from $1.28 per hour, averaging $1.46 per hour across four providers. GTX 1070 currently has no live cloud offers.
Which has higher power consumption?▾
MI250X draws 560W TDP compared to GTX 1070's 150W. This reflects its datacenter design versus consumer orientation.
What interconnect does MI250X use?▾
MI250X employs Infinity Fabric for multi-GPU scaling. GTX 1070 relies on standard PCIe without specialized links.
Which is cheaper to rent, the GTX 1070 or the MI250X?▾
Cloud rental prices for both the GTX 1070 and MI250X 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 MI250X?▾
The GTX 1070 has 8 GB of GDDR5 memory. The MI250X has 128 GB of HBM2e memory.
Can I find GTX 1070 and MI250X 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 MI250X?▾
The GTX 1070 uses the Pascal architecture (2016) while the MI250X uses CDNA 2 (2021). The MI250X delivers 58.9x the FP16 throughput and 12.8x the memory bandwidth of the GTX 1070.