GPU comparison

Quadro P6000 vs RTX 5070

Specifications and current cloud pricing, side by side.

PascalvsBlackwellUpdated 32 days ago

The RTX 5070 wins for the most common use case of LLM inference and training because its 30.9 TFLOPS FP32 exceeds the Quadro P6000 figure of 12.6 TFLOPS by 2.45 times while also providing dense FP16 at 61.7 TFLOPS.

Quadro P6000 listed from $1.10/GPU/hrRTX 5070 listed from $0.17/GPU/hr

Right now, from live stock

  • Cheapest right now: Quadro P6000 at $1.10/hr on Paperspace

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  • Most providers in stock: Quadro P6000 (1)

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Specifications Compared

SpecQuadro P6000RTX 5070
TDP250W250W
VRAM24 GB12-16 GB
CUDA Cores3,8406,144
FP4 (dense)Not published493.9 TFLOPS
FP8 (dense)Not published123.5 TFLOPS
Memory TypeGDDR5XGDDR7
ArchitecturePascalBlackwell
FP16 (dense)Not published61.7 TFLOPS
Form FactorsPCIePCIe
INT8 (dense)Not published246.9 TOPS
InterconnectPCIe 3.0PCIe 5.0
Tensor CoresNot published192
FP32 Performance12.6 TFLOPS30.9 TFLOPS
Memory Bandwidth432 GB/s672 GB/s
FP4 (with sparsity)Not published987.8 TFLOPS
FP8 (with sparsity)Not published246.9 TFLOPS
FP16 (with sparsity)Not published123.5 TFLOPS
INT8 (with sparsity)Not published493.9 TOPS

Performance Analysis

The RTX 5070 delivers 30.9 TFLOPS FP32 compared with 12.6 TFLOPS FP32 on the Quadro P6000 which produces a 2.45 times increase in single precision throughput. The RTX 5070 further supplies dense FP16 at 61.7 TFLOPS and dense FP8 at 123.5 TFLOPS where the Quadro P6000 provides no published figures for these precisions. The 1.55 times bandwidth advantage combined with the 2.45 times FP32 advantage indicates the RTX 5070 sustains higher throughput for both training and inference when dense tensor figures are compared directly against dense tensor figures.

Current On-Demand Offers

Cheapest secure on-demand offer per provider that is reported in stock right now, per GPU per hour; multi-GPU instances show the whole-instance price alongside. Deploy opens the DeployGPU console for providers on the platform, otherwise the provider's own site. Stock is rechecked every minute.

Quadro P6000

ProviderRegionGPUsPer GPU / hrInstance / hrDeploy
Paperspaceams11$1.10—Deploy

1 provider in stock, 6 offers (cheapest per provider shown). All Quadro P6000 offers, price history and alerts

RTX 5070

RTX 5070 is not offered on-demand by any provider we track right now. See the RTX 5070 rental page for last-seen listed prices and a price alert.

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When to Choose the Quadro P6000

The Quadro P6000 suits workloads that require the full 24 GB of GDDR5X memory for large model states that exceed the 12 to 16 GB range of the RTX 5070. Its 12.6 TFLOPS FP32 rating remains adequate for scientific computing tasks where memory capacity outweighs the need for dense FP16 throughput.

When to Choose the RTX 5070

Its 672 GB per second bandwidth further enables larger batches during dense FP8 operations at 123.5 TFLOPS compared with the older Quadro P6000.

Use Cases

LLM Training
RTX 5070

The RTX 5070 provides 30.9 TFLOPS FP32 and dense FP16 at 61.7 TFLOPS which exceed the Quadro P6000 FP32 rating of 12.6 TFLOPS.

LLM Inference
RTX 5070

The RTX 5070 supplies dense FP16 at 61.7 TFLOPS and 672 GB per second bandwidth for higher throughput than the Quadro P6000.

Fine-tuning
RTX 5070

The RTX 5070 delivers dense FP8 at 123.5 TFLOPS and 30.9 TFLOPS FP32 which support faster adaptation than the Quadro P6000 12.6 TFLOPS FP32.

Stable Diffusion
RTX 5070

Scientific Computing
Quadro P6000

The Quadro P6000 supplies 24 GB of memory which accommodates larger datasets than the 12 to 16 GB on the RTX 5070.

Frequently Asked Questions

What is the FP32 rating of the RTX 5070?▾

The RTX 5070 provides 30.9 TFLOPS FP32. This figure is 2.45 times higher than the 12.6 TFLOPS FP32 of the Quadro P6000.

Does the Quadro P6000 publish FP16 performance?▾

The Quadro P6000 does not publish an FP16 figure. The RTX 5070 supplies dense FP16 at 61.7 TFLOPS and sparse FP16 at 123.5 TFLOPS.

What interconnect does the RTX 5070 use?▾

The RTX 5070 uses PCIe 5.0 interconnect. The Quadro P6000 uses PCIe 3.0 interconnect.

How does memory bandwidth compare between the two GPUs?▾

The RTX 5070 provides 672 GB per second bandwidth. The Quadro P6000 provides 432 GB per second bandwidth.

What TDP do both GPUs share?▾

Both the Quadro P6000 and the RTX 5070 list a 250 watt TDP.

Which is cheaper to rent, the Quadro P6000 or the RTX 5070?▾

Cloud rental prices for both the Quadro P6000 and RTX 5070 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.

How much VRAM does the Quadro P6000 have compared to the RTX 5070?▾

The Quadro P6000 has 24 GB of GDDR5X memory. The RTX 5070 has 12 to 16 GB of GDDR7 memory.

Can I find Quadro P6000 and RTX 5070 GPUs available to rent right now?▾

Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.

What is the main difference between the Quadro P6000 and the RTX 5070?▾

The Quadro P6000 uses the Pascal architecture (2016) while the RTX 5070 uses Blackwell (2025). The Quadro P6000 has 24 GB of GDDR5X at 432 GB/s; the RTX 5070 has 12-16 GB of GDDR7 at 672 GB/s, so the RTX 5070 has 1.6x the memory bandwidth of the Quadro P6000.

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Related comparisons

How this page is made

  • Specifications come from the NVIDIA, AMD and Intel datasheets for the Quadro P6000 and the RTX 5070. Dense and sparse throughput are listed separately.
  • Prices and availability are live: every offer shown is an in-stock, on-demand listing from a provider we track, rechecked every minute.
  • The written comparison (overview, performance notes, when to choose each card, verdict, use cases and FAQ) was drafted with an AI model from the specification table above and passed an automated check that rejects any figure not in that table. It was last generated on . It contains no prices; those are always read live.
  • Read how we collect the data, or report an error on this page.

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