NVIDIA B200 192GB — Confidential GPU Compute (Intel TDX, Blackwell)
What makes B200 different from H100 and H200
The B200 is NVIDIA's Blackwell-generation GPU and is the first data-center GPU with native FP4 tensor cores. On Blackwell, FP4 throughput reaches roughly 5× the FP8 throughput of an H100 (Hopper) and ~2.5× an H200 — which means you train and serve frontier-scale models (400B–2T parameters) on fewer GPUs and lower wall-clock time. 192 GB of HBM3e at 8 TB/s(vs 141 GB / 4.8 TB/s on H200, 80 GB / 3.35 TB/s on H100) lets a single B200 hold a 70B model in FP16 with full KV cache headroom, or a 405B model sharded across just 4 GPUs instead of 8.
Every B200 on VoltageGPU is sealed inside an Intel TDX trust domain with Protected PCIe (encrypted CPU-to-GPU channel), AES-256 memory encryptionbacked by CPU-fused hardware keys, LUKS full-disk encryption, and on-chain remote attestation. The hypervisor and the platform operator (VoltageGPU) sit outside the trust boundary — the same silicon-level confidential computing model used by Microsoft Azure Confidential VMs and Google Cloud Confidential Computing, but at $7.50/GPU/hour with per-second billinginstead of $14+/hr on Azure.
B200 vs H200 vs H100 — quick spec comparison
| Metric | B200 | H200 | H100 |
|---|---|---|---|
| Architecture | Blackwell | Hopper refresh | Hopper |
| VRAM | 192 GB HBM3e | 141 GB HBM3e | 80 GB HBM3 |
| Memory bandwidth | 8 TB/s | 4.8 TB/s | 3.35 TB/s |
| Native FP4 tensor cores | Yes | No | No |
| NVLink generation | NVLink 5 (1.8 TB/s) | NVLink 4 (900 GB/s) | NVLink 4 (900 GB/s) |
| TDP | 1000 W | 700 W | 700 W |
| Confidential Computing | Intel TDX + Protected PCIe | Intel TDX + Protected PCIe | Intel TDX + Protected PCIe |
| VoltageGPU price | $7.50/hr | $3.60/hr | $2.77/hr |
See the dedicated pages for NVIDIA H200 141GB and NVIDIA H100 80GB, the live pricing comparison, and the full Intel TDX security architecture.
When to actually choose B200 over H200
- Frontier-model pre-training — Training 70B–405B parameter models from scratch. FP8/FP4 mixed precision on Blackwell delivers roughly 2–3× wall-clock speedup vs H200 and reduces cluster size proportionally. NVLink 5 keeps gradient all-reduce from becoming the bottleneck.
- Long-context inference (128K+) — 192 GB of HBM3e fits a 70B model plus a 256K-token KV cache without offloading. On H200 you'd start swapping past ~64K context.
- FP4 production inference — Quantizing DeepSeek-R1, Llama 3.1 405B, or GPT-OSS-120B to FP4 on B200 typically halves the per-token cost vs FP8 on H200, with negligible quality loss in evals. Native FP4 tensor cores mean no software emulation overhead.
- Multi-modal video / world models — Diffusion transformers and video-generation models are HBM-bandwidth bound; 8 TB/s is what unlocks them at production latency.
- Trillion-parameter MoE — DeepSeek-V3 (671B), Kimi-K2, GPT-OSS-120B route through experts that benefit from B200's combination of VRAM, bandwidth, and NVLink 5 expert-parallel sharding.
If you don't need any of the above, H200 at $3.60/hr is a better $/throughput pick. The B200 premium pays for itself only when you saturate FP4 or VRAM.
Pricing — single GPU, monthly equivalent, 8× cluster
- 1× B200 192GB Confidential: $7.50/hour (per-second billing, no commitment)
- Monthly equivalent (730h continuous): $5475/GPU/month
- 8× B200 NVLink 5 cluster: $60.0/hour (1.5 TB pooled HBM3e)
- Comparison: same 8× B200 hour on Azure ND-H200v5 confidential SKU lists at ~$112/hr
- $5 free credit on signup, no credit card required, Bitcoin and crypto accepted
Frequently asked — NVIDIA B200 confidential compute
Should I rent B200 or H200?
Pick B200 if you need native FP4, train 405B+ models, serve 128K+ context, or run video diffusion workloads. For everything else — including most fine-tuning, RAG, and 7B–70B inference — H200 at$3.60/hr gives a better cost-per-throughput ratio. The B200's 8 TB/s HBM3e and FP4 tensor cores only earn their premium on workloads that actually saturate them.
Does Intel TDX add overhead on B200?
Independent benchmarks (Phoronix, Intel TDX 1.5 release notes) measure 3–7% throughput overhead from TDX on CPU-bound workloads. On B200 GPU workloads the overhead is below the noise floor because compute happens inside the GPU. Protected PCIe adds a one-time ~50ms warm-up at pod start; steady-state throughput matches non-confidential B200 within margin of error.
Can I train a 70B model on a single B200?
Yes for LoRA / QLoRA fine-tuning — a 70B model fits comfortably in 192 GB with optimizer states in FP16 plus 32K context KV cache. For full fine-tuning of 70B you'd use 2–4 B200s with NVLink 5 tensor parallelism. For 405B full fine-tuning, plan on a 4×–8× B200 cluster.
Is the B200 GPU GDPR / HIPAA compatible on VoltageGPU?
Yes. B200 instances run inside Intel TDX trust domains with Protected PCIe and AES-256 memory encryption — VoltageGPU as the operator cannot read your data, satisfying GDPR Article 28 processor obligations and HIPAA technical safeguards for PHI. DPA available on request, BAA on request for healthcare workloads. EU-based legal entity (VOLTAGE EI, France).
Deploy a confidential B200 in 60 seconds
Sign up at voltagegpu.com/register with $5 free credit, pick a B200 template (vLLM, PyTorch, TensorRT-LLM, OpenClaw), and your sealed pod is reachable over SSH in under 60 seconds. Per-second billing — stop the pod and the meter stops instantly.