RTX 4090 vs A10
Explore a head to head comparison of specifications, performance, and pricing.
RTX 4090
The NVIDIA RTX 4090 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A10
The NVIDIA A10 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
RTX 4090 vs A10: Which Should You Choose?
Both the RTX 4090 and A10 offer 24 GB of VRAM, putting them on equal footing for memory-bound workloads. On FP16 throughput, the RTX 4090 delivers 82.58 TFLOPS versus 31.24 TFLOPS on the A10 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the A10 at 0.60 TB/s compared to 0.00 TB/s on the RTX 4090, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 4090 is built on Ada Lovelace while the A10 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 4090 starts from $0.60/hr versus $0.75/hr for the A10 — 25% more expensive — reflecting the performance premium. The RTX 4090 is available across 3 cloud providers on Shadeform compared to 1 for the A10, giving more options for region and pricing flexibility.
RTX 4090 — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4090 when:
- ✓Cost efficiency is your primary concern
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
A10 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A10 when:
- ✓Maximum performance justifies the higher cost
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
See how the RTX 4090 & A10 compare
Compare detailed hardware specifications and average pricing for the RTX 4090 and A10.
Compare Hardware Specifications
| RTX 4090 | A10 | |
|---|---|---|
| GPU Type | RTX 4090 | A10 |
| VRAM per GPU | 24 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ada Lovelace | Ampere |
| Interconnect | PCIe Gen4 | PCIe Gen4 |
| Memory Bandwidth | 1.008 TB/s | 600 GB/s |
| FP16 TFLOPS | 82.58 TFLOPS (1:1) | 31.24 TFLOPS (1:1) |
| CUDA Cores | 16384 | 9216 |
| Tensor Cores | 512 (4th Gen) | 288 (3rd Gen) |
| RT Cores | 128 (3rd Gen) | 72 (2nd Gen) |
| Base Clock | 2235 MHz | 885 MHz |
| Boost Clock | 2520 MHz | 1695 MHz |
| TDP | 450W | 150W |
| Process Node | TSMC 4N | TSMC 8nm |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32 | INT4, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| RTX 4090 | A10 | |
|---|---|---|
| 1 GPU | $0.60 /hr | $0.75 /hr |
| 2 GPUs | $1.20 /hr | N/A |
| 4 GPUs | $2.40 /hr | N/A |
| 8 GPUs | $3.97 /hr | N/A |
Frequently Asked Questions: RTX 4090 vs A10
The main differences are FP16 throughput (82.58 vs 31.24 TFLOPS), architecture (Ada Lovelace vs Ampere). The RTX 4090 uses the Ada Lovelace architecture while the A10 is based on Ampere, giving each GPU different generational capabilities.
The RTX 4090 is generally better for large language model training due to its higher throughput and 24 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the RTX 4090 is available from $0.60/hr. The A10 starts from $0.75/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
Based on TFLOPS per dollar, the RTX 4090 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A10, paying the premium may be justified by faster job completion and lower total cost.
The RTX 4090 is currently available across 3 cloud providers on Shadeform's network, compared to 1 for the A10. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either RTX 4090 or A10. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore RTX 4090 & A10 Instances
Browse available instances with RTX 4090 and A10 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
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