NVIDIA GB300 NVL72 GPU: 288GB Blackwell Ultra Specs, Pricing & Rental.
Rent GB300 NVL72 GPU on Spheron
72x B300 · 20 TB HBM3e · 1.44 EFLOPS FP4 sparse. Reserved GB300 NVL72 GPU rentals, from 8 GPUs to multi-rack.
NVIDIA's Blackwell Ultra rack-scale system. 72 B300 GPUs and 36 Grace CPUs share one NVLink 5 domain with 20.7 TB of HBM3e, the most memory in any rack-scale GPU NVIDIA ships. Built for trillion-parameter training, 200B+ inference, and frontier MoE workloads. Rent 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.
Rent NVIDIA GB300 GPUs on Spheron. 1024+ GPUs are live across our data center partner regions and ready to provision. Rent any quantity, from a single 8-GPU node to multi-rack deployments. Each GPU is a Blackwell Ultra B300 with 288 GB HBM3e. Inside an NVL72 rack, 72 GPUs and 36 Grace CPUs share a single NVLink 5 domain at 130 TB/s, exposing 20 TB of unified memory for trillion-parameter training and frontier inference. Submit the form with your GPU count and our team confirms availability within one business day. For workloads that don't need GB300 specifically, B300 per-GPU rentals and B200 are on per-minute billing today.
Where GB300 sits in the stack
GB300 is NVIDIA's latest Blackwell Ultra GPU. Spheron has 1024+ available to rent at any quantity. Rubin R100 is the next generation, available H2 2026.
GB300 NVL72 specifications
Per-rack specifications. Smaller rentals 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.
GB300 NVL72 vs GB200 NVL72 vs HGX H100
| Spec (per rack) | GB300 NVL72New | GB200 NVL72 | HGX H100 (8x) |
|---|---|---|---|
| Architecture | Blackwell Ultra | Blackwell | Hopper |
| GPUs / rack | 72 × B300 | 72 × B200 | 8 × H100 |
| Total HBM | 20 TB HBM3e | 13.4 TB HBM3e | 640 GB HBM3 |
| FP4 sparse | 1.44 EFLOPS | 1.44 EFLOPS | N/A |
| FP4 dense | 1.08 EFLOPS | 0.72 EFLOPS | 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-8 · 800 Gb/s | ConnectX-7 · 400 Gb/s | ConnectX-7 · 400 Gb/s |
| Spheron availability | Live now | Available | 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. GB300 specs match the NVIDIA GB300 NVL72 reference design.
Workloads built for GB300
Trillion-Parameter Pre-Training
Each B300 GPU has 288 GB HBM3e. A single 8-GPU node holds 2.3 TB; a full NVL72 rack holds 20 TB inside one NVLink domain, plus 37 TB of fast memory once Grace LPDDR5X is counted. NVIDIA rates the rack at 1,440 PFLOPS FP4 with sparsity and 1,080 PFLOPS dense, the latter being 1.5x GB200 NVL72. Rent the count that fits your run.
Frontier-Scale FP4 Inference
B300 delivers 1.5x more dense FP4 than B200 (15 vs 10 PFLOPS) and adds 2x attention compute on top. 130 TB/s of NVLink fabric inside an NVL72 rack supports tensor and pipeline parallelism for the largest models without leaving the rack. Smaller rentals fit 70B to 400B parameter inference on 8 to 32 GPUs.
Reasoning and Agentic Workloads
Long reasoning chains generate large KV-cache footprints. 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. Rent from a few GPUs for prototyping up to full racks for production serving.
Fine-Tuning and Post-Training
Rent 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 GB300
Pick GB300 if
You want Blackwell Ultra. We have 1024+ GPUs available to rent at any quantity, from a single 8-GPU node to multi-rack deployments. Models too large for one node sit inside an NVL72 rack with 20 TB of unified HBM3e at 130 TB/s. Rent the count you actually need.
Pick B300 (per GPU) instead if
You want per-minute billing with no commitment. B300 SXM6 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, B300 is the simpler call.
Pick GB200 instead if
Budget matters and you don't need 288 GB per GPU. GB200 has 186 GB per GPU inside the rack versus 288 GB on GB300, with identical NVLink fabric and Grace CPU layout. For sub-1T parameter workloads, GB200 is often the better price-to-performance pick.
Pick R100 if you can wait
R100 Rubin is available H2 2026 with 22 TB/s per-GPU bandwidth (2.75x B300) and 50 PFLOPS FP4 (3.33x B300). For new training runs with flexible timelines, R100 is the higher-ceiling option. If you need to start now, GB300 is live today.
Other GPUs on Spheron
For workloads that don't need GB300 specifically, Spheron also offers B300, B200, and H200 on per-minute billing with no commitments.
GB300 Guides & Benchmarks
More GPU Pricing guides →GB300 NVL72 vs GB200 NVL72
The real rack price gap and who is actually renting GB300 in 2026.
NVIDIA B300 (Blackwell Ultra) Guide
288GB specs, live cloud pricing, and which workloads justify Blackwell Ultra.
B300 vs B200 Cost Per Token
FP4 throughput and real cost-per-token math for Blackwell Ultra rental.
GB300 FAQ
1024+ NVIDIA GB300 GPUs are live across our Tier 3 and Tier 4 liquid-cooled data center partner regions and ready to provision. We add more capacity through 2026. Submit the form with your GPU count and our team confirms availability, region, and pricing within one business day.
Rent 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.
B300 is the individual GPU: 288 GB HBM3e, 8 TB/s bandwidth, 15 PFLOPS FP4. It runs in standard 8-GPU SXM6 nodes connected to an x86 host. GB300 is the same B300 silicon deployed in NVL72 racks that pair 72 GPUs with 36 Grace CPUs over NVLink 5, exposing 20 TB of unified memory inside one NVLink domain. B300 is rented per GPU on per-minute billing. GB300 is rented by request in any quantity and our team provisions.
Pricing depends on quantity, commitment length, region, and networking requirements. Smaller rentals 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 (186 GB HBM3e each inside the rack) for B300 GPUs (288 GB HBM3e), raising total rack GPU memory from 13.4 TB to 20 TB and fast memory to 37 TB. Sparse FP4 is unchanged at 1,440 PFLOPS; dense FP4 rises from 720 to 1,080 PFLOPS, the 1.5x figure NVIDIA cites, and B300 adds 2x attention compute over B200. NVIDIA publishes no rack power figure for either system. NVLink 5 fabric, Grace CPU layout, and software stack remain consistent, while networking upgrades from ConnectX-7 (400 Gb/s) to ConnectX-8 (800 Gb/s) per GPU.
Models that exceed 2.3 TB of HBM memory (the ceiling of a single 8x B300 node) are the clearest fit at full-rack scale: 1T+ parameter dense and MoE training, frontier inference at high concurrency, and long-context serving with large KV cache. Smaller GB300 allocations are also a strong pick when you specifically need Blackwell Ultra compute and bandwidth for inference serving, fine-tuning, or experimentation on the latest architecture.
Our team confirms availability within one business day. From there, smaller rentals (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.