NVIDIA B200 Blackwell Cloud Pricing & Rental Rates
The B200 Blackwell delivers 192 GB HBM3e at 8 TB/s with NVLink 5.0 at 1.8 TB/s. Its 4,500 FP4 TFLOPS enable next-generation MoE training and extreme-throughput inference for trillion-parameter models.
Target Workload: Next-gen MoE training & extreme-throughput FP4 serving
Live Cloud Pricing for NVIDIA B200 Blackwell
Spot and reserved rates refreshed from provider APIs. Click a provider to deploy.
| Provider | GPU & VRAM | Interconnect | Spot Rate | On-Demand | Monthly | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Community | NVLink 5.0 (1.8 TB/s) | $3.99 / hr | $9.98 / hr | $2,442 / mo | Instant | |||
Bare Metal | NVLink 5.0 (1.8 TB/s) | $4.49 / hr | $11.23 / hr | $2,748 / mo | Instant | |||
Dedicated | NVLink 5.0 (1.8 TB/s) | $5.49 / hr | $13.73 / hr | $3,360 / mo | Instant | |||
Cloud | NVLink 5.0 (1.8 TB/s) | $5.99 / hr | $14.98 / hr | $3,666 / mo | Instant |
Models That Fit NVIDIA B200 Blackwell (192GB HBM3e)
Deterministic VRAM calculation (FP8) via entity graph. Only editorial and enriched models shown.
| MODEL | PARAMS | CONTEXT | FP8 VRAM | FIT? | CALCULATOR |
|---|---|---|---|---|---|
| DeepSeek R1 Distill 70B | 70B | 128K | 93.9 GB | ✓ fits | Pre-filled → |
| DeepSeek R1 Distill Qwen 32B | 32B | 128K | 42.9 GB | ✓ fits | Pre-filled → |
| Llama 3.3 70B Instruct | 70.6B | 128K | 94.7 GB | ✓ fits | Pre-filled → |
| Llama 3.1 8B Instruct | 8.03B | 128K | 10.8 GB | ✓ fits | Pre-filled → |
| Llama 3.2 3B Instruct | 3.2B | 128K | 4.3 GB | ✓ fits | Pre-filled → |
| Llama 3.2 1B Instruct | 1B | 128K | 1.3 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 32B | 32.5B | 128K | 43.6 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 14B | 14.7B | 128K | 19.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 7B | 7.61B | 128K | 10.2 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 72B Instruct | 72.7B | 128K | 97.5 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 14B Instruct | 14.7B | 128K | 19.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 7B Instruct | 7.61B | 128K | 10.2 GB | ✓ fits | Pre-filled → |
Inference & Serving Capacity
Practical model feasibility, max batch sizes, and KV-cache retention limits for NVIDIA B200 Blackwell.
Llama 3.3 70B
FEASIBLEFP4 native, single GPU, extreme throughput
DeepSeek 671B
FEASIBLEFP4 cuts memory 50%, NVLink 5.0 mesh
Qwen 2.5 32B
FEASIBLEFP4 at 4,500 TFLOPS, production serving
vLLM Throughput (FP8)
~180 tok/s (vLLM, Llama 70B FP8, batch=1)
Estimated tokens/second, single GPU, Llama-class model
Max Context Window (Llama 70B)
128k+ tokens (FP8) — single-GPU, full context, high batch
Maximum context length before KV-cache eviction
Hardware Bottleneck Analysis
Whether NVIDIA B200 Blackwell is compute-bound (TFLOPS) or memory-bandwidth bound (GB/s) across workloads.
Bottleneck Classification
Balanced — 8 TB/s bandwidth matches 2,250 TFLOPS at FP8
Recommended Quantization
FP4 / FP8 native — FP4 cuts memory 50% with <5% quality loss
Best Cluster Topology
8-way NVLink 5.0 Full Mesh (1.8 TB/s per GPU)
Deep Analysis
B200 Blackwell FP4 Precision Scaling & Liquid Cooling Analysis: The B200's second-generation Transformer Engine adds native FP4 precision — 4,500 TFLOPS at one-quarter the precision of FP16. FP4 quantization cuts memory requirements in half: Llama 70B at FP4 occupies ~35 GB (vs 70 GB FP16), fitting entirely on a single B200 with 157 GB remaining for KV-cache. The quality tradeoff is <5% perplexity degradation on standard benchmarks, acceptable for inference workloads. NVLink 5.0 at 1.8 TB/s per GPU enables 8-way tensor parallelism with near-zero communication overhead — the doubled bandwidth vs NVLink 4.0 absorbs all-reduce latency even at batch=256. The critical infrastructure dependency: B200 at 1000W TDP requires liquid-cooled racks. Air-cooled datacenters cannot sustain the thermal envelope. Providers offering B200 must have direct-to-chip liquid cooling infrastructure, which limits availability to purpose-built AI datacenters.
Architecture & Die Breakdown
Architecture
Blackwell GB200 — 4NP TSMC
TDP
1000W
Memory Subsystem
192GB HBM3e at 8.0 TB/s bandwidth. High Bandwidth Memory provides the throughput needed to keep tensor cores fed during large batch inference.
Interconnect
NVLink 5.0 (1.8 TB/s). Enables multi-GPU tensor parallelism with high-bandwidth, low-latency GPU-to-GPU communication.
Precision Performance
| Precision | TFLOPS | Use Case |
|---|---|---|
| FP4 | 4,500 | Extreme-throughput inference, quantized serving |
| FP8 | 2,250 | Training & inference with mixed-precision |
| FP16 | 1,125 | Full-precision training, fine-tuning, evaluation |
Break-Even ROI Calculator
Monthly hours where reserved pricing beats spot for NVIDIA B200 Blackwell. Above the break-even point, reserve commits save money.
Spheron
RunPod
Lambda Labs
Break-even at 280 hours/month: if you run NVIDIA B200 Blackwell more than 280 hours per month, reserved pricing on all three providers saves money. At 720 hours/month (24/7), reserved saves $1,078+/mo vs on-demand.
Related GPUs
Frequently Asked Questions
How much does it cost to rent an NVIDIA B200 Blackwell per hour?▾
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