GPU comparison

B300 vs RTX 5000 Ada

Specifications and current cloud pricing, side by side.

Blackwell UltravsAda LovelaceUpdated 33 days ago

The B300 wins for the most common use case of large scale model work because its 2250 TFLOPS FP16 dense rating and 262-288 GB memory exceed the RTX 5000 Ada specifications by substantial margins in every published comparable metric.

B300 listed from $0.50/GPU/hrRTX 5000 Ada listed from $0.55/GPU/hr

Right now, from live stock

Specifications Compared

SpecB300RTX 5000 Ada
TDP1400W250W
VRAM262-288 GB32 GB
CUDA CoresNot published12,800
FP4 (dense)13,500 TFLOPSNot published
FP8 (dense)4,500 TFLOPSNot published
Memory TypeHBM3eGDDR6
ArchitectureBlackwell UltraAda Lovelace
FP16 (dense)2,250 TFLOPSNot published
Form FactorsSXM6PCIe
INT8 (dense)187.5 TOPSNot published
InterconnectNVLink, PCIe 6.0, InfiniBandPCIe 4.0
Tensor CoresNot published400
FP32 Performance75 TFLOPS65.3 TFLOPS
FP64 Performance1.25 TFLOPSNot published
Memory Bandwidth8,000 GB/s576 GB/s
FP4 (with sparsity)18,000 TFLOPSNot published
FP8 (with sparsity)9,000 TFLOPS1,044.4 TFLOPS
FP16 (with sparsity)4,500 TFLOPSNot published
INT8 (with sparsity)375 TOPSNot published

Performance Analysis

The B300 FP16 dense figure reaches 2250 TFLOPS whereas the RTX 5000 Ada provides no published FP16 value. FP32 comparison shows the B300 at 75 TFLOPS against the RTX 5000 Ada at 65.3 TFLOPS. Memory bandwidth of 8000 GB/s on the B300 versus 576 GB/s on the RTX 5000 Ada supports larger batch sizes during workloads that move substantial data volumes. The B300 FP8 dense rating stands at 4500 TFLOPS while the RTX 5000 Ada lists FP8 with sparsity at 1044.4 TFLOPS so direct ratios stay within matching sparsity categories only.

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.

B300

ProviderRegionGPUsPer GPU / hrInstance / hrDeploy
RunPodglobal1$0.50—Deploy

1 provider in stock, 2 offers (cheapest per provider shown). All B300 offers, price history and alerts

RTX 5000 Ada

RTX 5000 Ada is not offered on-demand by any provider we track right now. See the RTX 5000 Ada rental page for last-seen listed prices and a price alert.

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When to Choose the B300

The B300 suits scenarios that require 262-288 GB HBM3e memory and 8000 GB/s bandwidth together with FP16 dense performance of 2250 TFLOPS. Its 1400W TDP and SXM6 form factor align with installations that accommodate high power draw and NVLink interconnects.

When to Choose the RTX 5000 Ada

The RTX 5000 Ada fits deployments limited to 250W TDP and PCIe form factor with PCIe 4.0 interconnect. Its 32 GB GDDR6 memory and 576 GB/s bandwidth match lighter workloads that do not exceed the published FP32 rating of 65.3 TFLOPS.

Use Cases

LLM Training
B300

The B300 supplies 2250 TFLOPS FP16 dense and 262-288 GB memory that support full scale training runs.

LLM Inference
B300

The B300 FP8 dense rating of 4500 TFLOPS enables higher throughput than the RTX 5000 Ada FP8 with sparsity rating of 1044.4 TFLOPS.

Fine-tuning
B300

The B300 8000 GB/s bandwidth and 75 TFLOPS FP32 allow efficient updates on large parameter sets.

Stable Diffusion
RTX 5000 Ada

The RTX 5000 Ada 250W TDP and PCIe form factor suit graphics oriented pipelines with its 65.3 TFLOPS FP32.

Scientific Computing
B300

The B300 75 TFLOPS FP32 combined with 8000 GB/s bandwidth handles compute intensive simulations.

Frequently Asked Questions

What memory does the B300 provide?▾

The B300 provides 262-288 GB HBM3e memory with 8000 GB/s bandwidth.

How does B300 FP32 compare to RTX 5000 Ada FP32?▾

The B300 lists 75 TFLOPS FP32 while the RTX 5000 Ada lists 65.3 TFLOPS FP32.

What TDP values appear for each GPU?▾

The B300 carries a 1400W TDP and the RTX 5000 Ada carries a 250W TDP.

Which interconnects does the B300 support?▾

The B300 supports NVLink, PCIe 6.0, and InfiniBand in SXM6 form factor.

Does the RTX 5000 Ada publish an FP16 value?▾

The RTX 5000 Ada does not publish an FP16 value though it lists FP8 with sparsity at 1044.4 TFLOPS.

What architecture does each GPU use?▾

The B300 uses Blackwell Ultra architecture and the RTX 5000 Ada uses Ada Lovelace architecture.

Which is cheaper to rent, the B300 or the RTX 5000 Ada?▾

Cloud rental prices for both the B300 and RTX 5000 Ada 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 B300 have compared to the RTX 5000 Ada?▾

The B300 has 262 to 288 GB of HBM3e memory. The RTX 5000 Ada has 32 GB of GDDR6 memory.

Can I find B300 and RTX 5000 Ada 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 B300 and the RTX 5000 Ada?▾

The B300 uses the Blackwell Ultra architecture (2025) while the RTX 5000 Ada uses Ada Lovelace (2023). The B300 has 262-288 GB of HBM3e at 8,000 GB/s; the RTX 5000 Ada has 32 GB of GDDR6 at 576 GB/s, so the B300 has 13.9x the memory bandwidth of the RTX 5000 Ada.

Rent these GPUs

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Related comparisons

How this page is made

  • Specifications come from the NVIDIA, AMD and Intel datasheets for the B300 and the RTX 5000 Ada. 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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