NVIDIA GB200 NVL72 GPU: 186GB Grace Blackwell Specs, Pricing & Rental.
Rent GB200 NVL72 GPU on Spheron
72x B200 · 13.4 TB HBM3e · 1.44 EFLOPS FP4 sparse. Reserved GB200 NVL72 GPU rentals, from 8 GPUs to multi-rack.
NVIDIA's Blackwell rack-scale system. 72 B200 GPUs and 36 Grace CPUs share one NVLink 5 domain with 13.4 TB of HBM3e and 1.44 EFLOPS of FP4 with sparsity. Built for trillion-parameter training and 100B+ inference where rack-scale memory and NVLink fabric decide the run. Reserve any count on Spheron, from a single 8-GPU node to multi-rack.
Tell us how many GPUs you need. We confirm availability within one business day.
Submit Your Request
Tell us how many GPUs you need. Our team confirms availability within one business day and gets you provisioned.
Reserve NVIDIA GB200 Blackwell GPUs on Spheron in any count, from a single 8-GPU node to multi-rack deployments. Each GPU is a Blackwell B200 with 192 GB HBM3e. Inside an NVL72 rack, 72 GPUs and 36 Grace CPUs share a single NVLink 5 domain at 130 TB/s, exposing 13.4 TB of unified memory for large-scale LLM training and inference. Submit the form with your GPU count and our team confirms availability within one business day. For workloads that don't need GB200 specifically, B200 per-GPU rentals and H200 are on per-minute billing today.
Where GB200 sits in the stack
GB200 is the broadly-deployed Blackwell rack-scale system. GB300 is the Blackwell Ultra upgrade with 50% more memory. Rubin R100 is the next generation, available H2 2026.
GB200 NVL72 specifications
Per-rack specifications. Smaller reservations inherit the same architecture at the slice they're allocated. All systems run in NVL72 reference configuration with liquid cooling, 2:1 InfiniBand fat-tree fabric across racks, and persistent NVMe per chassis.
GB200 NVL72 vs GB300 NVL72 vs HGX H100
| Spec (per rack) | GB200 NVL72Reserve | GB300 NVL72 | HGX H100 (8x) |
|---|---|---|---|
| Architecture | Blackwell | Blackwell Ultra | Hopper |
| GPUs / rack | 72 × B200 | 72 × B300 | 8 × H100 |
| Total HBM | 13.4 TB HBM3e | 20 TB HBM3e | 640 GB HBM3 |
| FP4 sparse | 1.44 EFLOPS | 1.44 EFLOPS | N/A |
| FP4 dense | 0.72 EFLOPS | 1.08 EFLOPS | N/A |
| FP4 dense per GPU | 10 PFLOPS | 15 PFLOPS | N/A |
| FP8 throughput | 720 PFLOPS | 720 PFLOPS | 16 PFLOPS |
| NVLink fabric | 130 TB/s | 130 TB/s | 7.2 TB/s (8 GPUs) |
| CPU | 36 × Grace | 36 × Grace | x86 host |
| Networking | ConnectX-7 · 400 Gb/s | ConnectX-8 · 800 Gb/s | ConnectX-7 · 400 Gb/s |
| Spheron access | By reservation | By reservation | Available |
Per-rack specifications for NVL72 systems. HGX H100 figures are for a standard 8-GPU node for relative scale, since H100 does not come in NVL72 configuration. GB200 specs match the NVIDIA GB200 NVL72 reference design.
Workloads built for GB200
Large-Scale LLM Pre-Training
Each B200 GPU has 192 GB HBM3e standalone, 186 GB as configured inside the NVL72 rack. A single 8-GPU node holds 1.5 TB; a full NVL72 rack holds 13.4 TB inside one NVLink domain. 1.44 EFLOPS FP4 per rack is a 4x lift over equivalent H100 capacity for 200B to 1T parameter pre-training. Reserve the count that fits your run.
High-Throughput FP4 Inference
B200 doubles FP4 throughput over H100 and delivers 8 TB/s HBM bandwidth per GPU. 130 TB/s of NVLink fabric inside an NVL72 rack supports tensor and pipeline parallelism for large models without leaving the rack. Smaller reservations fit 70B to 200B parameter inference on 8 to 32 GPUs.
Reasoning and Agentic Workloads
Grace CPUs handle orchestration, retrieval, and tool calls in the same coherent address space as the GPUs over 900 GB/s NVLink-C2C, removing PCIe round-trips that slow down agent loops on x86 nodes. Reserve from a few GPUs for prototyping up to full racks for production serving.
Fine-Tuning and Post-Training
Reserve the GPU count that matches your run. DPO and GRPO on 70B models fit on a single 8-GPU node. Large-scale RL with rollouts, value heads, and reference models benefits from full-rack co-location inside one NVLink domain. Persistent NVMe per chassis handles checkpoint streaming at any scale.
When to pick GB200
Pick GB200 if
You want Blackwell rack-scale at the best price-to-performance. Reserve any quantity, from a single 8-GPU node to multi-rack deployments. Models that need rack-scale memory and fit in 13.4 TB of HBM3e (most workloads under 1T parameters) get the same NVLink 5 fabric and Grace CPU coupling as GB300 at lower cost.
Pick GB300 instead if
Your workload needs 288 GB per GPU or is bottlenecked on attention compute. GB300 has 50% more HBM3e per GPU and 2x attention throughput over GB200, with ConnectX-8 (800 Gb/s) replacing ConnectX-7 (400 Gb/s). For 1T+ parameter training and reasoning workloads, GB300 is the upgrade.
Pick B200 (per GPU) instead if
You want per-minute billing with no commitment. B200 SXM5 is available on Spheron on a per-GPU or 8-GPU node basis with on-demand and spot pricing. For most 70B to 200B parameter inference and fine-tuning jobs that don't need a sales conversation, B200 is the simpler call.
Pick R100 if you can wait
R100 Rubin is available H2 2026 with 22 TB/s per-GPU bandwidth (2.75x B200) and 50 PFLOPS FP4 (5x B200 dense). For new training runs with flexible timelines, R100 is the higher-ceiling option. If you need to start now, GB200 is live today.
Other GPUs on Spheron
For workloads that don't need GB200 specifically, Spheron also offers B200, B300, and H200 on per-minute billing with no commitments.
GB200 Guides & Benchmarks
More GPU Deep Dives guides →GB200 NVL72 Guide
Rack specs, 1.44 exaflops per rack, and cloud rates from about $10.50/hr per GPU.
NVIDIA B200 Specs & Benchmarks
The B200 GPUs that make up every GB200 NVL72 rack: specs and MLPerf benchmarks.
NVIDIA Parabricks on GPU Cloud
50x faster genomics pipelines on rack-scale H100 and B200 clusters.
GB200 FAQ
Submit the form on this page with your expected GPU count, timeline, and intended workload. Our team confirms availability, region, and pricing within one business day. Reservations are allocated per request across Tier 3 and Tier 4 liquid-cooled data center partner regions.
Reserve any count. The smallest configuration is a single 8-GPU node. From there you can scale to 36 GPUs (half rack), 72 GPUs (full NVL72 rack), or multi-rack deployments in the hundreds. Smaller allocations still sit inside an NVL72 NVLink domain, so you keep the same interconnect at any scale.
B200 is the individual GPU: 192 GB HBM3e, 8 TB/s bandwidth, 10 PFLOPS dense FP4. It runs in standard 8-GPU SXM5 nodes connected to an x86 host. GB200 is the same B200 silicon deployed in NVL72 racks that pair 72 GPUs with 36 Grace CPUs over NVLink 5, exposing 13.4 TB of unified memory inside one NVLink domain. B200 is rented per GPU on per-minute billing. GB200 is reserved by request in any quantity and our team provisions.
Pricing depends on quantity, commitment length, region, and networking requirements. Smaller reservations run closer to per-hour rates; multi-rack reservations come with committed pricing and priority allocation. Submit the form with your GPU count and our team shares a quote within one business day.
GB300 swaps B200 GPUs (192 GB HBM3e) for B300 GPUs (288 GB HBM3e), raising total rack GPU memory from 13.4 TB to 20 TB. Per-GPU dense FP4 increases from 10 to 15 PFLOPS and B300 adds 2x attention compute. Networking upgrades from ConnectX-7 (400 Gb/s) to ConnectX-8 (800 Gb/s) per GPU. NVLink 5 fabric, Grace CPU layout, and software stack remain consistent, so workloads port directly between the two systems.
Models in the 200B to 1T parameter range that benefit from rack-scale memory and NVLink interconnect. 1.44 EFLOPS FP4 per rack supports trillion-parameter dense and MoE training, frontier inference at high concurrency, long-context serving, and large-scale RL post-training. Smaller GB200 reservations also work for fine-tuning and inference on the latest Blackwell architecture at lower cost than GB300.
Our team confirms availability within one business day. From there, smaller reservations (single 8-GPU nodes, half racks) typically come online within days. Multi-rack deployments take longer depending on region and networking requirements. We share the timeline along with the quote.