deploybase

GB200 vs H200: Specs, Benchmarks & Cloud Pricing Compared

Deploybase · October 7, 2025 · GPU Comparison

Contents

GB200 vs H200 Specs: Unified Memory Changes Everything

Gb200 vs H200 is the focus of this guide. GB200 = 1x Grace CPU + 2x Blackwell GPUs. 480GB unified memory (CPU) + 384GB HBM3e (2x B200 GPUs), cache-coherent.

H200 = 1x Hopper GPU. 141GB HBM. Separate memory space from the host.

This matters. With H200, developers need multiple GPUs for 405B models. With GB200, one unit. Unified memory = no round-tripping data between CPU and GPU.

GPU performance is similar (GB200's Blackwell ≈ H100's Hopper). The memory is the story.

GB200 is production hardware as of March 2026 but scarce. Cloud availability is coming Q2-Q3 2026.

Memory and Bandwidth: The Real Story

GB200: 480GB unified. H200: 141GB. That's the difference.

405B model: 3 H200s needed. 1 GB200 fits it. Unified memory also means GPU can read CPU data directly-no copying embeddings back and forth. Latency drops 40% for inference that touches both CPU and GPU.

Bandwidth is identical (960 GB/s both). Power is identical (~700W). Per-token cost winds up similar, but GB200 handles way bigger models.

Inference Benchmarks: GB200 Dominates at Scale

405B model (4-bit):

  • 1x H200: ~18 tokens/sec (need 3 for real throughput)
  • 1x GB200: ~24 tokens/sec (fits entirely, handles batching alone)

First-token latency matters more. GB200 is 40% faster due to unified memory (no inter-GPU communication overhead). Smaller models (13B-70B) show little advantage.

Training: H200 and GB200 differ less

For training, H200 and GB200 are similar performers. Training benefits less from unified memory because training already communicates heavily between components.

Distributed training across multiple GPUs: both use NVIDIA H200 pricing strategy - multiple units working in parallel, not unified memory.

GB200 offers advantage during model serving after training. Train on H200 or H100 clusters. Serve on GB200.

Cloud pricing for GB200

CoreWeave offers the GB200 NVL72 (4x GB200) at $42/hour as of March 2026. Azure offers 4x GB200 at $108.16/hour. Other providers are expected to launch later in 2026.

  • CoreWeave GB200 NVL72 (4x): $42/hr ($10.50 per GPU)
  • Azure GB200 (4x): $108.16/hr

Compare to H200 pricing at $3.59-4.50/hour per GPU. GB200 costs more per unit but handles 3x the model size, so per-token cost improves.

CoreWeave is the primary GB200 source as of March 2026. H200 remains widely available at lower cost for most workloads.

When to use each chip

Choose H200 via Lambda when:

  • Production hardware is needed today
  • Model fits within 141GB HBM limits
  • Cost optimization ranks below availability concerns

Choose GB200 when:

  • Serving massive models (405B+) is the priority
  • Unified memory APIs are available
  • Cost per inference token improves at scale

FAQ

Q: Is GB200 really twice as fast as H200? Not twice as fast. Same tensor throughput roughly. Advantage comes from unified memory reducing communication overhead. 15-25% speedup for large model inference.

Q: Can I run H200 optimized code on GB200 unmodified? Yes, most code. CUDA code that assumes separate memory spaces needs refactoring to use unified memory. VRAM limiting code becomes less common.

Q: What's a Grace CPU doing in there anyway? Grace handles host-side compute, CPU workloads. If your inference uses CPU components (feature engineering, embedding lookups), Grace reduces data movement.

Q: Should I buy GB200 or rent it? Rent it. Availability is scarce. By the time you get hardware, better chips exist. Cloud pricing will settle in Q3 2026.

Q: Does GB200 have hardware issues like early H100s? Blackwell had good launches. No major issues reported. Grace CPU integration adds complexity. Early versions may have quirks but production units ship stable.

Q: What about power consumption differences? GB200 system: 700-800W. H200: 700W. Nearly identical. Your facility power costs don't change much between them.

Sources