Specifications Compared
| Spec | A10 | RTX-4060 |
|---|---|---|
| TDP | 150W | 115W |
| VRAM | 24 GB | 8 GB |
| CUDA Cores | 9,216 | 3,072 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Ampere | Ada Lovelace |
| Form Factors | PCIe | PCIe |
| Interconnect | ||
| Tensor Cores | 288 | 96 |
| FP16 Performance | 31.2 TFLOPS | 15.1 TFLOPS |
| FP32 Performance | 31.2 TFLOPS | 15.1 TFLOPS |
| INT8 Performance | 250 TOPS | 242 TOPS |
| Memory Bandwidth | 600 GB/s | 272 GB/s |
Performance Analysis
The A10's 31.2 TFLOPS FP16 and FP32 throughput surpasses the RTX 4060's 15.1 TFLOPS by over 100 percent, enabling roughly twice the speed in compute-bound operations like matrix multiplications during training or inference. This delta means training epochs complete faster on the A10: for instance, a workload requiring 30 TFLOPS sustained performance favors its higher peak. Inference benefits similarly, with the A10 handling more concurrent requests before saturation.
Memory specs define real-world limits: the A10's 24 GB VRAM and 600 GB/s bandwidth support batch sizes up to three times larger than the RTX 4060's 8 GB and 272 GB/s, critical for stable training of models over 7 billion parameters. Lower bandwidth on the RTX 4060 risks bottlenecks in data-heavy phases, reducing effective throughput by 20 to 50 percent in memory-bound scenarios. Power efficiency tilts toward the RTX 4060 at 115W TDP, yielding better perf-per-watt for light loads.
Architectural advances in Ada Lovelace provide the RTX 4060 with superior ray tracing and efficiency cores, but Ampere's tensor cores on the A10 maintain parity in ML primitives. Overall, the A10 dominates heavy AI tasks, while the RTX 4060 suits optimized, smaller-scale inference.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A10
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 10×NVIDIA A10 24GB VRAM | 24GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.60/GPU/hr $6.00/hr total (10×) | Available | ||
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 2826GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 794GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | Available | ||
LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 646GB Storage | Czechia | $1.07/GPU/hr | Available |
When to Choose the A10
Opt for the A10 in scenarios demanding high VRAM capacity, such as training large language models exceeding 8 GB or fine-tuning with batch sizes over 32. Its 24 GB GDDR6 and 600 GB/s bandwidth prevent out-of-memory errors that plague the RTX 4060, ensuring stable runs at $0.60 per hour starting price. Datacenter reliability makes it ideal for production inference serving multiple users simultaneously.
When to Choose the RTX 4060
The RTX 4060 fits budget-conscious prototyping or inference on models under 7 billion parameters, leveraging its Ada Lovelace architecture for 15.1 TFLOPS at just $0.08 per hour. Lower 115W TDP suits edge deployments or long-running tasks where cost accumulates: over 100 hours, it saves over $90 versus the A10's average $1.06 per hour. Consumer optimizations excel in creative apps like lightweight Stable Diffusion.
Use Cases
The A10's 24 GB VRAM supports models over 13 billion parameters without gradient checkpointing, unlike the RTX 4060's 8 GB limit. Its 31.2 TFLOPS doubles training speed.
Higher 600 GB/s bandwidth on the A10 handles larger batch sizes for low-latency serving. 24 GB capacity fits quantized 70B models fully.
A10's 31.2 TFLOPS FP16 accelerates LoRA adapters on datasets needing 20 GB states. RTX 4060 bottlenecks at 8 GB for mid-sized models.
RTX 4060's Ada Lovelace excels in ray-traced generation at 15.1 TFLOPS with 8 GB sufficient for 512x512 images. Lower $0.08 per hour cost suits iterative art workflows.
A10's 600 GB/s bandwidth processes large simulations without paging, leveraging 24 GB for datasets over 10 GB. Higher FP32 31.2 TFLOPS speeds HPC kernels.
Frequently Asked Questions
Which GPU has more VRAM: A10 or RTX 4060?▾
The A10 provides 24 GB GDDR6 VRAM, three times the RTX 4060's 8 GB. This enables larger models on the A10 without splitting across devices.
How do their prices compare in the cloud?▾
RTX 4060 starts at $0.08 per hour with average $0.15 per hour across six offers, versus A10's $0.60 per hour average $1.06 per hour on three offers. Savings favor RTX 4060 for light use.
Is the A10 faster for AI training?▾
Yes, A10's 31.2 TFLOPS FP16 outperforms RTX 4060's 15.1 TFLOPS by 106 percent. Combined with 24 GB VRAM, it trains bigger batches faster.
What is the memory bandwidth difference?▾
A10 achieves 600 GB/s, more than double the RTX 4060's 272 GB/s. This reduces bottlenecks in data loading for training.
Which has lower power consumption?▾
RTX 4060 draws 115W TDP versus A10's 150W. It offers better efficiency for prolonged low-intensity tasks.
Can RTX 4060 handle LLM inference?▾
RTX 4060 manages inference for models up to 7B parameters in 8 GB, but A10's 24 GB supports 30B+ quantized models at higher throughput.
Which is cheaper to rent, the A10 or the RTX 4060?▾
Cloud rental prices for both the A10 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 A10 have compared to the RTX 4060?▾
The A10 has 24 GB of GDDR6 memory. The RTX 4060 has 8 GB of GDDR6 memory.
Can I find A10 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 A10 and the RTX 4060?▾
The A10 uses the Ampere architecture (2021) while the RTX 4060 uses Ada Lovelace (2023). The A10 delivers 2.1x the FP16 throughput and 2.2x the memory bandwidth of the RTX 4060.