A10 vs RTX 4000 Ada
Explore a head to head comparison of specifications, performance, and pricing.
A10
The NVIDIA A10 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
RTX 4000 Ada
The NVIDIA RTX 4000 Ada delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A10 vs RTX 4000 Ada: Which Should You Choose?
The A10 offers 24 GB of VRAM — 1.2× the 20 GB on the RTX 4000 Ada — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the A10 delivers 31.24 TFLOPS versus 26.73 TFLOPS on the RTX 4000 Ada — 1.2× faster for mixed-precision training and inference. Memory bandwidth favors the A10 at 0.60 TB/s compared to 0.36 TB/s on the RTX 4000 Ada, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A10 is built on Ampere while the RTX 4000 Ada uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the A10 starts from $0.75/hr versus $0.79/hr for the RTX 4000 Ada — 5% more expensive — reflecting the performance premium.
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:
- ✓You need 24 GB+ VRAM for large models or long context windows
- ✓Cost efficiency is your primary concern
- ✓You are training large models or running high-throughput inference
RTX 4000 Ada — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4000 Ada when:
- ✓20 GB VRAM is sufficient for your workload
- ✓Maximum performance justifies the higher cost
- ✓Your workload does not require peak FP16 throughput
See how the A10 & RTX 4000 Ada compare
Compare detailed hardware specifications and average pricing for the A10 and RTX 4000 Ada.
Compare Hardware Specifications
| A10 | RTX 4000 Ada | |
|---|---|---|
| GPU Type | A10 | RTX 4000 Ada |
| VRAM per GPU | 24 GB | 20 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Ada Lovelace |
| Interconnect | PCIe Gen4 | PCIe Gen4 |
| Memory Bandwidth | 600 GB/s | 360 GB/s |
| FP16 TFLOPS | 31.24 TFLOPS (1:1) | 26.73 TFLOPS (1:1) |
| CUDA Cores | 9216 | 6144 |
| Tensor Cores | 288 (3rd Gen) | 192 (4th Gen) |
| RT Cores | 72 (2nd Gen) | 48 (3rd Gen) |
| Base Clock | 885 MHz | 1500 MHz |
| Boost Clock | 1695 MHz | 2175 MHz |
| TDP | 150W | 130W |
| Process Node | TSMC 8nm | TSMC 4N |
| Data Formats | INT4, INT8, BF16, FP16, TF32, FP32 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| A10 | RTX 4000 Ada | |
|---|---|---|
| 1 GPU | $0.75 /hr | $0.79 /hr |
| 2 GPUs | N/A | N/A |
| 4 GPUs | N/A | N/A |
| 8 GPUs | N/A | N/A |
Frequently Asked Questions: A10 vs RTX 4000 Ada
The main differences are VRAM (24 GB vs 20 GB), FP16 throughput (31.24 vs 26.73 TFLOPS), architecture (Ampere vs Ada Lovelace). The A10 uses the Ampere architecture while the RTX 4000 Ada is based on Ada Lovelace, giving each GPU different generational capabilities.
The A10 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 A10 is available from $0.75/hr. The RTX 4000 Ada starts from $0.79/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The A10 has more VRAM at 24 GB, compared to 20 GB on the RTX 4000 Ada. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the A10 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 RTX 4000 Ada, paying the premium may be justified by faster job completion and lower total cost.
The A10 is currently available across 1 cloud providers on Shadeform's network, compared to 1 for the RTX 4000 Ada. 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 A10 or RTX 4000 Ada. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A10 & RTX 4000 Ada Instances
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