NVIDIA B300 vs H200: GPU Specs & Performance Analysis
Publication Details
- Author: Marketing (Canopy Wave)
- Date: June 17, 2026
- Source: Canopy Wave Blog
Revolutionary Improvements in B300
Built on the Blackwell Ultra architecture, the B300 began shipping in January 2026. The article frames it as NVIDIA's most powerful single-GPU platform to date, representing "a qualitative leap across multiple key metrics" compared to the Hopper-generation H200/H100.
Key architectural improvements cited for LLM inference:
- A single B300 can host a 70B-parameter model at FP16 while still leaving over 100GB available for KV Cache
- Compared to the H100, B300 delivers 11–15× greater inference throughput
- Expanded memory allows full KV Cache retention for long-context sequences
Complete GPU Specifications Table
| GPU | Architecture | FP8 (Dense) Compute | Memory | Memory Bandwidth | NVLink |
|---|---|---|---|---|---|
| B300 | Blackwell Ultra | 7,000 TFLOPS | 288GB HBM3e | 8 TB/s | 1.8 TB/s |
| B200 | Blackwell | 4,500 TFLOPS | 192GB HBM3e | 8 TB/s | 1.8 TB/s |
| H200 | Hopper | 756 TFLOPS | 141GB HBM3e | 4.8 TB/s | 900 GB/s |
| H100 | Hopper | 756 TFLOPS | 80GB HBM3e | 3.35 TB/s | 900 GB/s |
Additional compute: B300 delivers 14 petaFLOPS of sparse FP4 compute.
Memory multipliers: B300 offers 2× the memory capacity of the H200 and 3.6× that of the H100. B200 delivers roughly 6× the FP8 inference performance compared to the H200.
Power and Cooling
- B300 TDP: 1,400W
- Cooling Requirement: Direct liquid cooling (DLC) mandatory
- 8-GPU DGX B300 peak power: ~14kW (equivalent to two H100 DGX systems)
Air cooling, sufficient for H200 and H100, is inadequate for B300. The article suggests enterprises may prefer cloud access rather than "delegating power and thermal challenges to the cloud provider."
Blackwell Ultra vs. Hopper — Performance Comparison
| Metric | B300 vs. H200 Gain |
|---|---|
| Prefill Throughput (ISL=2k) | 8× |
| Short Output Throughput (ISL=2k, OSL=128) | 20× |
Inference Performance Summary
| GPU | Memory | Bandwidth | Inference Performance | Suitable Scenarios |
|---|---|---|---|---|
| H100 | 80GB | 3.35 TB/s | Baseline | Mid-size LLMs |
| H200 | 141GB | 4.8 TB/s | 2–3× | Long-context |
| B300 | 288GB | 8 TB/s | 8–20× | Inference models |
B300 Use Cases
Optimal scenarios: 1. Large-scale inference for 70B+ models — single-GPU throughput cited as 100,000+ tokens/s 2. Inference-optimized models (DeepSeek, Kimi series) leveraging full KV Cache retention across 288GB 3. Multi-node training clusters — 6.4 Tbps of GPU interconnect bandwidth (likely aggregate NVSwitch fabric bandwidth) 4. 400B+ parameter model deployment — an 8-GPU DGX B300 provides 2.3TB of total memory for full model loading
Key challenges: - DLC requirement increases infrastructure investment - 1,400W per card necessitates careful power/cooling capacity planning - Software requires CUDA 12.x, cuDNN 9.x, and TensorRT-LLM 0.15+
Pricing / Availability
No specific pricing provided. Canopy Wave is accepting reservations for NVIDIA B300 cloud servers.
Key Takeaways
- Memory leadership: 288GB HBM3e — the defining differentiator, enabling single-GPU hosting of large models with ample KV Cache headroom
- Compute leap: 7,000 FP8 TFLOPS (dense) represents roughly 9.3× the FP8 throughput of H100/H200
- Inference dominance: 8–20× performance uplift over H200 depending on workload
- Infrastructure cost: 1,400W TDP and mandatory liquid cooling raise TCO significantly
- Ecosystem dependencies: Requires latest CUDA, cuDNN, and TensorRT-LLM versions