Specifications Compared
| Spec | RTX 3070 | RTX 4060 |
|---|---|---|
| TDP | 220W | 115W |
| VRAM | 8 GB | 8-16 GB |
| CUDA Cores | 5,888 | 3,072 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Ampere | Ada Lovelace |
| FP16 (dense) | 40.6 TFLOPS | Not published |
| Form Factors | PCIe | PCIe |
| INT8 (dense) | 162.6 TOPS | Not published |
| Interconnect | PCIe 4.0 | PCIe 4.0 |
| Tensor Cores | 184 | 96 |
| FP32 Performance | 20.3 TFLOPS | 15.1 TFLOPS |
| Memory Bandwidth | 448 GB/s | 272 GB/s |
| FP8 (with sparsity) | Not published | 242 TFLOPS |
| FP16 (with sparsity) | 81.3 TFLOPS | Not published |
| INT8 (with sparsity) | 325.2 TOPS | Not published |
Performance Analysis
The RTX 3070 supplies dense FP16 performance at 40.6 TFLOPS whereas the RTX 4060 does not publish an FP16 figure. FP32 performance favors the RTX 3070 at 20.3 TFLOPS over the RTX 4060 at 15.1 TFLOPS. Memory bandwidth of 448 GB/s on the RTX 3070 exceeds the 272 GB/s on the RTX 4060 and this difference influences maximum batch sizes during training or inference passes. Higher bandwidth permits larger batches before memory capacity constraints appear. The RTX 3070 also lists dense INT8 at 162.6 TOPS while the RTX 4060 provides no corresponding INT8 value.
Current On-Demand Offers
Cheapest secure on-demand offer per provider that is reported in stock right now, per GPU per hour; multi-GPU instances show the whole-instance price alongside. Deploy opens the DeployGPU console for providers on the platform, otherwise the provider's own site. Stock is rechecked every minute.
RTX 3070
RTX 3070 is not offered on-demand by any provider we track right now. See the RTX 3070 rental page for last-seen listed prices and a price alert.
RTX 4060
RTX 4060 is not offered on-demand by any provider we track right now. See the RTX 4060 rental page for last-seen listed prices and a price alert.
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When to Choose the RTX 3070
Choose the RTX 3070 when your model fits in 8 GB and you care about speed. Its 448 GB/s bandwidth against 272 GB/s makes it noticeably quicker at LLM decode and at image generation, and it publishes 40.6 TFLOPS dense FP16 for mixed precision fine tuning of small models. Do not stretch it: 8 GB is a hard ceiling with no larger configuration to ask for.
When to Choose the RTX 4060
Choose the RTX 4060 when the listing is the 16 GB configuration and you need more than 8 GB, which is the case for larger quantized models, longer contexts, and fine tuning with any real batch size. It is a newer Ada part with 96 tensor cores and FP8 support, listed at 242 TFLOPS with sparsity, so it is not slow, just bandwidth limited at 272 GB/s. An 8 GB RTX 4060 has no advantage over the RTX 3070 and should not be chosen over it.
Use Cases
The RTX 3070 provides dense FP16 at 40.6 TFLOPS and 448 GB/s bandwidth that support larger training batches than the RTX 4060.
FP32 performance at 20.3 TFLOPS on the RTX 3070 exceeds 15.1 TFLOPS on the RTX 4060 for inference throughput.
Memory bandwidth of 448 GB/s on the RTX 3070 allows larger fine-tuning batches compared with 272 GB/s on the RTX 4060.
The RTX 4060 TDP of 115W reduces power requirements relative to 220W on the RTX 3070 for sustained image generation.
The RTX 3070 FP32 rating of 20.3 TFLOPS exceeds the 15.1 TFLOPS rating of the RTX 4060 for compute kernels.
Frequently Asked Questions
What is the FP32 performance difference between RTX 3070 and RTX 4060?▾
The RTX 3070 reaches 20.3 TFLOPS FP32 while the RTX 4060 reaches 15.1 TFLOPS FP32. This difference affects workloads that rely on single precision matrix operations.
How does memory bandwidth compare on these GPUs?▾
The RTX 3070 provides 448 GB/s memory bandwidth and the RTX 4060 provides 272 GB/s memory bandwidth. Bandwidth limits batch size in memory intensive training or inference.
Which GPU has lower power consumption?▾
The RTX 4060 TDP measures 115W while the RTX 3070 TDP measures 220W. Lower TDP reduces cooling and electricity demands in multi GPU setups.
Does the RTX 4060 publish an FP16 value?▾
The table lists no FP16 figure for the RTX 4060, but that is a data gap rather than a missing feature: it has 96 Ada tensor cores and publishes 242 TFLOPS FP8 with sparsity, so it runs FP16 and mixed precision normally. The RTX 3070 lists 40.6 TFLOPS dense FP16 and 81.3 TFLOPS with sparsity.
Which is cheaper to rent, the RTX 3070 or the RTX 4060?▾
Cloud rental prices for both the RTX 3070 and RTX 4060 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 RTX 3070 have compared to the RTX 4060?▾
The RTX 3070 has 8 GB of GDDR6 memory. The RTX 4060 has 8 to 16 GB of GDDR6 memory.
Can I find RTX 3070 and RTX 4060 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 RTX 3070 and the RTX 4060?▾
The RTX 3070 uses the Ampere architecture (2020) while the RTX 4060 uses Ada Lovelace (2023). The RTX 3070 has 8 GB of GDDR6 at 448 GB/s; the RTX 4060 has 8-16 GB of GDDR6 at 272 GB/s, so the RTX 3070 has 1.6x the memory bandwidth of the RTX 4060.
Rent these GPUs
Each GPU page lists every current on-demand offer by provider, per GPU-hour, updated every minute.
Related comparisons
How this page is made
- Specifications come from the NVIDIA, AMD and Intel datasheets for the RTX 3070 and the RTX 4060. Dense and sparse throughput are listed separately.
- Prices and availability are live: every offer shown is an in-stock, on-demand listing from a provider we track, rechecked every minute.
- The written comparison (overview, performance notes, when to choose each card, verdict, use cases and FAQ) was drafted with an AI model from the specification table above and passed an automated check that rejects any figure not in that table. It was last generated on . It contains no prices; those are always read live.
- Read how we collect the data, or report an error on this page.
Next steps
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