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RTX 6000 vs A4000

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

A4000

The NVIDIA A4000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$1.17/hr
GPU VRAM16 GB
Cloud Availability2 clouds
System Memory215 GB
CPU Cores56
Storage1.3 TB

RTX 6000 vs A4000: Which Should You Choose?

The RTX 6000 offers 24 GB of VRAM — 1.5× the 16 GB on the A4000 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the RTX 6000 delivers 32.62 TFLOPS versus 19.17 TFLOPS on the A4000 — 1.7× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 6000 at 0.67 TB/s compared to 0.45 TB/s on the A4000, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX 6000 is built on Turing while the A4000 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the A4000 starts from $0.15/hr versus $0.50/hr for the RTX 6000 — 233% more expensive — reflecting the performance premium. The A4000 is available across 2 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:

  • You need 24 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
  • Your preferred provider already has availability

A4000 — 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 A4000 when:

  • 16 GB VRAM is sufficient for your workload
  • Cost efficiency is your primary concern
  • Your workload does not require peak FP16 throughput
  • You need flexibility across multiple cloud providers or regions

See how the RTX 6000 & A4000 compare

Compare detailed hardware specifications and average pricing for the RTX 6000 and A4000.

Compare Hardware Specifications

RTX 6000A4000
GPU Type
RTX 6000
A4000
VRAM per GPU
24 GB
16 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Turing
Ampere
Interconnect
PCIe Gen3
PCIe Gen4
Memory Bandwidth
672 GB/s
448 GB/s
FP16 TFLOPS
32.62 TFLOPS (2:1)
19.17 TFLOPS (1:1)
CUDA Cores
4608
6144
Tensor Cores
576 (2nd Gen)
192 (3rd Gen)
RT Cores
72 (1st Gen)
48 (2nd Gen)
Base Clock
1440 MHz
735 MHz
Boost Clock
1770 MHz
1695 MHz
TDP
295W
140W
Process Node
TSMC 12nm
TSMC 8nm
Data Formats
INT8, INT4, FP16, FP32
INT8, BF16, FP16, TF32, FP32

Compare Average On-Demand Pricing

RTX 6000A4000
1 GPU
$0.50 /hr
$0.47 /hr
2 GPUs
N/A
$0.95 /hr
4 GPUs
N/A
$1.90 /hr
8 GPUs
N/A
$1.20 /hr

Frequently Asked Questions: RTX 6000 vs A4000

The main differences are VRAM (24 GB vs 16 GB), FP16 throughput (32.62 vs 19.17 TFLOPS), architecture (Turing vs Ampere). The RTX 6000 uses the Turing architecture while the A4000 is based on Ampere, giving each GPU different generational capabilities.

The RTX 6000 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 A4000 is available from $0.15/hr. The RTX 6000 starts from $0.50/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.

The RTX 6000 has more VRAM at 24 GB, compared to 16 GB on the A4000. 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 A4000 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 6000, paying the premium may be justified by faster job completion and lower total cost.

The A4000 is currently available across 2 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 A4000. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore RTX 6000 & A4000 Instances

Browse available instances with RTX 6000 and A4000 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

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