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NVIDIA B300 Blackwell Ultra vs NVIDIA H100 SXM5

Side-by-side comparison of NVIDIA B300 Blackwell Ultra (288 GB VRAM, 9.0 TB/s) and NVIDIA H100 SXM5 (80 GB VRAM, 3.35 TB/s). Compare specs, compute throughput, and workload sizing for LLM inference.

Decision Summary

SpecNVIDIA B300 Blackwell UltraNVIDIA H100 SXM5
VRAM288GB HBM3e80GB HBM3
Memory Bandwidth9.0 TB/s3.35 TB/s
FP8 TFLOPS2,5001,979
FP16 TFLOPS1,250989
InterconnectNVLink 5.0 (1.8 TB/s)NVLink 4.0 (900 GB/s)
TDP1200W700W
Recommended QuantizationFP4 / FP8 / BF16 — all precisions native, no quality tradeoffFP8 / FP4 native

Compare for a Workload

Configure a workload to see how each selected GPU performs. Calculations are deterministic estimates based on architectural specifications.

Configure workload parameters above and click "Calculate VRAM Requirement" to see results.

Cloud Provider Pricing

Current spot and on-demand rates across providers for NVIDIA B300 Blackwell Ultra and NVIDIA H100 SXM5.

Featured GPU Pricing Pages

NVIDIA H100 SXM5

80GB HBM3 • 3.35 TB/s • NVLink 4.0

NVIDIA A100 40GB

40GB HBM2 • 1.55 TB/s • NVLink 3.0

NVIDIA A100 80GB

Verified spot rates & reserved pricing

NVIDIA A10G

24GB GDDR6 • 0.62 TB/s • PCIe 4.0

NVIDIA RTX 3090

24GB GDDR6X • 0.94 TB/s • PCIe 4.0

NVIDIA T4

16GB GDDR6 • 0.32 TB/s • PCIe 3.0

ProviderGPU & VRAMInterconnectSpot Price ($/hr)On-Demand ($/hr)Monthly ($/720h)StatusAction
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On-demand and monthly figures are the providers' listed rates (monthly = listed rate, else hourly × 720h). Rows without a tracked rate show —. All rates subject to preemption and provider availability.
Data Freshness: Public Cloud APIs & Market Scraping | Refreshed Daily (UTC)Benchmark Baseline: Ubuntu 24.04, CUDA 12.4, vLLM v0.6.x, PagedAttention v2, FlashAttention-3

Prices verified daily from Spheron, RunPod, Vast.ai, and Lambda Labs APIs.

Microarchitecture & Interconnect

NVIDIA B300 Blackwell Ultra
ArchitectureBlackwell Ultra GB300
Process Node4NP TSMC
FP8 TFLOPS2,500
FP16 TFLOPS1,250
Compute BoundCompute-bound at small batch with FP8/BF16; memory-bound for frontier parameter counts
Recommended Topology8-way NVLink 5.0 Full Mesh, multi-node via NVLink-C2C
Recommended Quantization: FP4 / FP8 / BF16 — all precisions native, no quality tradeoff
NVIDIA H100 SXM5
ArchitectureHopper GH100
Process Node4nm TSMC
FP8 TFLOPS1,979
FP16 TFLOPS989
Compute BoundMemory-bandwidth bound at large batch; compute-bound at small batch with FP8/BF16
Recommended Topology8-way HGX Baseboard with NVLink 4.0 Mesh
Recommended Quantization: FP8 / FP4 native

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NVIDIA H100 SXM5 vs NVIDIA H200 SXM5→
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NVIDIA B200 Blackwell vs NVIDIA H100 SXM5→
192GB HBM3e vs 80GB HBM3
Deploy NVIDIA B300 Blackwell UltraDeploy NVIDIA H100 SXM5
Data Freshness: Verified via Public Cloud APIs & Market Scraping | Refreshed Daily (UTC)Benchmark Baseline: Ubuntu 24.04, CUDA 12.4, vLLM v0.6.x (PagedAttention v2, FlashAttention-3), BF16/FP8 weights.
Methodology →