RTX 4000 Ada vs A100
Explore a head to head comparison of specifications, performance, and pricing.
RTX 4000 Ada
The NVIDIA RTX 4000 Ada delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A100
The NVIDIA A100 is a powerful Ampere-based GPU designed for AI training, inference, and high-performance computing workloads.
RTX 4000 Ada vs A100: Which Should You Choose?
The A100 offers 40 GB of VRAM — 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 A100 delivers 77.97 TFLOPS versus 26.73 TFLOPS on the RTX 4000 Ada — 3× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 4000 Ada at 0.36 TB/s compared to 0.00 TB/s on the A100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 4000 Ada is built on Ada Lovelace while the A100 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 4000 Ada starts from $0.79/hr versus $1.36/hr for the A100 — 72% more expensive — reflecting the performance premium. The A100 is available across 5 cloud providers on Shadeform compared to 1 for the RTX 4000 Ada, giving more options for region and pricing flexibility.
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
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
A100 — 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 A100 when:
- ✓You need 40 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
See how the RTX 4000 Ada & A100 compare
Compare detailed hardware specifications and average pricing for the RTX 4000 Ada and A100.
Compare Hardware Specifications
| RTX 4000 Ada | A100 | |
|---|---|---|
| GPU Type | RTX 4000 Ada | A100 |
| VRAM per GPU | 20 GB | 40 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ada Lovelace | Ampere |
| Interconnect | PCIe Gen4 | PCIe Gen4 or SXM4 |
| Memory Bandwidth | 360 GB/s | 1.55 TB/s |
| FP16 TFLOPS | 26.73 TFLOPS (1:1) | 77.97 TFLOPS (4:1) |
| CUDA Cores | 6144 | 6912 |
| Tensor Cores | 192 (4th Gen) | 432 (3rd Gen) |
| RT Cores | 48 (3rd Gen) | N/A |
| Base Clock | 1500 MHz | 765 MHz |
| Boost Clock | 2175 MHz | 1410 MHz |
| TDP | 130W | 250W-400W |
| Process Node | TSMC 4N | TSMC 7nm |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32 | INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| RTX 4000 Ada | A100 | |
|---|---|---|
| 1 GPU | $0.79 /hr | $1.88 /hr |
| 2 GPUs | N/A | $4.38 /hr |
| 4 GPUs | N/A | $8.64 /hr |
| 8 GPUs | N/A | $14.90 /hr |
Frequently Asked Questions: RTX 4000 Ada vs A100
The main differences are VRAM (20 GB vs 40 GB), FP16 throughput (26.73 vs 77.97 TFLOPS), architecture (Ada Lovelace vs Ampere). The RTX 4000 Ada uses the Ada Lovelace architecture while the A100 is based on Ampere, giving each GPU different generational capabilities.
The A100 is generally better for large language model training due to its higher throughput and 40 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 4000 Ada is available from $0.79/hr. The A100 starts from $1.36/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The A100 has more VRAM at 40 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 A100 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 A100 is currently available across 5 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 RTX 4000 Ada or A100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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