H200 vs B200: Which NVIDIA GPU is Right for Your AI Workload?
Compare NVIDIA's Hopper H200 and Blackwell B200 architectures to determine the best fit for your LLM training and inference needs.
H200 vs B200: Which NVIDIA GPU is Right for Your AI Workload?
NVIDIA dropped two heavy hitters in 2024-2025: the H200 with 141GB HBM3e (Hopper refresh) and the B200 with 192GB HBM3e (Blackwell architecture). Both are beasts. Both cost a fortune. So which one should you buy?
Let's cut through the marketing and get to what actually matters for your AI infrastructure.
TL;DR: The Quick Decision Framework
Buy H200 if:
- You need GPUs now (H200 shipping today)
- Your models fit in 141GB HBM3e memory
- You want proven Hopper architecture with mature drivers
- Budget is $39,999/GPU and you need certainty
Buy B200 if:
- You can wait until Q2-Q3 2025
- You need 192GB memory for massive models
- You want 5x faster FP4 performance vs H100
- You're willing to bet on cutting-edge Blackwell
The Reality: Most enterprises are buying H200 today for production, and pre-ordering B200 for 2025 deployment. Hedge your bets.
Architecture Breakdown
H200: Hopper Refined
The H200 is essentially an H100 on steroids:
| Spec | H100 SXM | H200 SXM |
|---|---|---|
| Memory | 80GB HBM3 | 141GB HBM3e |
| Bandwidth | 3 TB/s | 4.8 TB/s |
| FP8 Performance | 1,979 TFLOPS | 1,979 TFLOPS |
| Process | TSMC 4N | TSMC 4N |
| TDP | 700W | 700W |
| NVLink | 900 GB/s | 900 GB/s |
What Changed: More memory, faster memory. That's it. Same Hopper silicon.
Why That Matters: LLMs like Llama 3 70B with long context windows (128K+ tokens) now fit in a single GPU. Multi-modal models with vision encoders? They fit. You're swapping less and training faster.
B200: Blackwell's Bold Bet
B200 is a complete redesign:
| Spec | B200 |
|---|---|
| Architecture | Blackwell (5th Gen Tensor Cores) |
| Memory | 192GB HBM3e |
| Bandwidth | 8 TB/s |
| FP4 Performance | 20 petaFLOPS |
| FP8 Performance | 10 petaFLOPS |
| Transistors | 208 billion (dual-die) |
| Process | TSMC N4P |
| TDP | 1000W |
| NVLink | 1.8 TB/s |
What's New:
- Dual-die chiplet design - Two GPUs in one package
- FP4 precision - 4-bit floating point for extreme throughput
- 2nd-gen Transformer Engine - Smarter mixed-precision training
- 8TB/s bandwidth - 1.67x faster than H200
The Catch: It's vaporware until mid-2025, draws 1000W (liquid cooling required), and costs... TBD (rumored $50K-$70K).
Performance Comparison
LLM Training (Llama 3 70B)
| GPU | Training Time (1 epoch) | Cost per Epoch |
|---|---|---|
| H100 80GB | 100 hours | $4,000 |
| H200 141GB | 85 hours | $3,400 |
| B200 192GB | 20 hours (est) | $1,000 (est) |
Estimates based on NVIDIA claims and early benchmarks
Why H200 wins today: Fits the model in memory without spilling. 15% faster than H100.
Why B200 wins tomorrow: FP4 Transformer Engine could deliver 4-5x speedup on compatible models.
LLM Inference (GPT-4 scale)
For inference, memory bandwidth is king:
- H200: 4.8 TB/s = ~3,400 tokens/sec/GPU
- B200: 8 TB/s = ~5,700 tokens/sec/GPU (projected)
Real Talk: If you're running high-throughput inference today, H200 is the smart buy. B200's advantage won't matter if you can't deploy it until Q3 2025.
Computer Vision & Multi-Modal
Both excel, but B200's dual-die design shines for:
- Video generation (Sora-style models)
- Multi-modal training (text + image + video)
- Real-time rendering with AI enhancement
Software Ecosystem
CUDA & Framework Support
H200: Full support today
- CUDA 12.x native
- PyTorch 2.x optimized
- TensorFlow, JAX, Triton all work
- HuggingFace Transformers tuned
B200: Coming soon™
- CUDA 12.4+ required (launching with B200)
- PyTorch 2.3+ will have FP4 kernels
- Framework updates rolling out Q2 2025
The Risk: Early B200 adopters = beta testers. Expect rough edges.
Pricing & Availability
H200
- List Price: $39,999
- Availability: In stock now
- Lead Time: 2-4 weeks
- Volume Discounts: 10% at 8+ units
B200
- List Price: TBD ($50K-$70K rumored)
- Availability: Q2-Q3 2025
- Lead Time: Pre-order now for Q3 delivery
- MOQ: Likely 8+ for early access
Pro Tip: Pre-order B200, buy H200 today. Deploy H200 for production, swap in B200 when it ships.
Power & Cooling
| GPU | TDP | Cooling | Power per 8-GPU Server |
|---|---|---|---|
| H200 | 700W | Liquid | ~6.5kW |
| B200 | 1000W | Liquid | ~9kW |
Infrastructure Impact:
- B200 requires beefier power (3x 3000W PSUs)
- More aggressive liquid cooling
- Higher datacenter density limits
Cost: Expect 30-40% higher operational costs with B200.
Decision Matrix
Choose H200 if:
- ✅ You need GPUs in 2024-Q1 2025
- ✅ Your largest model is <140GB
- ✅ You value stability over bleeding-edge performance
- ✅ Your datacenter has standard 6-7kW rack density
Choose B200 if:
- ✅ You can wait until Q3 2025
- ✅ You're training foundation models >100B parameters
- ✅ FP4 training applies to your models (Transformer-based)
- ✅ You have 9-10kW rack capacity and advanced cooling
Hybrid Strategy (Recommended):
- Buy 8x H200 today → deploy immediately
- Pre-order 8x B200 → deploy Q3 2025
- Keep H200 for inference, use B200 for training
The Honest Take
H200 is the safe bet. It's shipping, it's proven, and it'll handle 95% of workloads beautifully. The 141GB is a genuine upgrade that solves real problems today.
B200 is the future. If the FP4 Transformer Engine delivers on NVIDIA's claims, it'll be a game-changer for LLM training. But it's vaporware until it's in your rack.
Our recommendation? Buy H200 now. Pre-order B200 if you have budget. Don't wait for B200 if you need to ship models in 2025.
Ready to Order?
We have H200 141GB SXM5 in stock today. $39,999/GPU, volume discounts at 4+ units.
B200 pre-orders open for Q3 2025 delivery.
Call (850) 407-7265 for same-day quotes and custom configurations.
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