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RTX 6000 vs A100

Explore a head to head comparison of specifications, performance, and pricing.

RTX 6000

The NVIDIA RTX 6000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$0.50/hr
GPU VRAM24 GB
Cloud Availability1 clouds
System Memory46 GB
CPU Cores14
Storage512 GB

A100

The NVIDIA A100 is a powerful Ampere-based GPU designed for AI training, inference, and high-performance computing workloads.

ManufacturerNVIDIA
GPU ArchitectureAmpere
Average Price$6.71/hr
GPU VRAM40 GB
Cloud Availability5 clouds
System Memory1800 GB
CPU Cores176
Storage13.6 TB

RTX 6000 vs A100: Which Should You Choose?

The A100 offers 40 GB of VRAM — 1.7× the 24 GB on the RTX 6000 — 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 32.62 TFLOPS on the RTX 6000 — 2× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 6000 at 0.67 TB/s compared to 0.00 TB/s on the A100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 6000 is built on Turing while the A100 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 6000 starts from $0.50/hr versus $1.29/hr for the A100 — 158% more expensive — reflecting the performance premium. The A100 is available across 5 cloud providers on Shadeform compared to 1 for the RTX 6000, giving more options for region and pricing flexibility.

RTX 6000 — Best Use Cases

  • Inference and model serving
  • Light training and fine-tuning
  • Graphics and rendering workloads

Choose RTX 6000 when:

  • 24 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 6000 & A100 compare

Compare detailed hardware specifications and average pricing for the RTX 6000 and A100.

Compare Hardware Specifications

RTX 6000A100
GPU Type
RTX 6000
A100
VRAM per GPU
24 GB
40 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Turing
Ampere
Interconnect
PCIe Gen3
PCIe Gen4 or SXM4
Memory Bandwidth
672 GB/s
1.55 TB/s
FP16 TFLOPS
32.62 TFLOPS (2:1)
77.97 TFLOPS (4:1)
CUDA Cores
4608
6912
Tensor Cores
576 (2nd Gen)
432 (3rd Gen)
RT Cores
72 (1st Gen)
N/A
Base Clock
1440 MHz
765 MHz
Boost Clock
1770 MHz
1410 MHz
TDP
295W
250W-400W
Process Node
TSMC 12nm
TSMC 7nm
Data Formats
INT8, INT4, FP16, FP32
INT8, BF16, FP16, TF32, FP32, FP64

Compare Average On-Demand Pricing

RTX 6000A100
1 GPU
$0.50 /hr
$1.74 /hr
2 GPUs
N/A
$3.91 /hr
4 GPUs
N/A
$7.80 /hr
8 GPUs
N/A
$13.78 /hr

Frequently Asked Questions: RTX 6000 vs A100

The main differences are VRAM (24 GB vs 40 GB), FP16 throughput (32.62 vs 77.97 TFLOPS), architecture (Turing vs Ampere). The RTX 6000 uses the Turing 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 6000 is available from $0.50/hr. The A100 starts from $1.29/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 24 GB on the RTX 6000. 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 RTX 6000 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 A100, 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 6000. 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 6000 or A100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore RTX 6000 & A100 Instances

Browse available instances with RTX 6000 and A100 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

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