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What Meta Grand Teton is—and what it is not
Grand Teton is a GPU hardware platform Meta designed for data-center AI training and inference. Its design combines compute, power delivery, management, and fabric interfaces in one chassis. Meta contributes the design to the Open Compute ecosystem, where data-center hardware designs can be shared and adapted.
It is not a consumer desktop product with a standard retail listing. Meta’s published descriptions concern an open hardware platform and large-scale deployments, not a single consumer-facing “Grand Teton 8x H100” model with a price and retail SKU.
Does “8x NVIDIA H100” describe Grand Teton?
Eight H100 GPUs do describe a server configuration: NVIDIA’s DGX H100 datasheet defines an eight-GPU system. But that does not establish that Meta sells or publicly specifies a matching eight-GPU Grand Teton retail server. Meta’s disclosures emphasize systems assembled into clusters of thousands of GPUs. Treat “8x H100” as a useful way to describe one possible server-scale configuration in the H100 generation, not as Meta’s name for its overall Grand Teton deployment.
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- The H100 NVL graphics card is designed to scale the support of large language models, such as GPT3-175B, in mainstream PCIe-based server systems, providing up to 12X the throughput performance of HGX A100 systems when configured with 8 units.
- Equipped with advanced features, including 94GB of high-speed HBM3 memory, NVLink connectivity for enhanced inter-GPU communication, and an impressive memory bandwidth of 3938 GB/sec, the H100 NVL is built for high-performance AI inference tasks.
- The card showcases a robust performance spectrum across various compute types: 68 TFLOPS for FP64, 134 TFLOPS for both FP64 Tensor Core and FP32, escalating up to 7916 TFLOPS/TOPS for FP8 and INT8 Tensor Core operations, all benefiting from sparsity optimizations.
- It enables standard mainstream servers to deliver high-performance capabilities for generative AI inference, simplifying the deployment process for partners and solution providers with fast time to market and ease of scalability.
- The H100 NVL's power efficiency is optimized with a configurable maximum power consumption ranging between 2x 350-400W, supporting extensive computational tasks without excessive power usage.
| Option | What the published material establishes | What to take from it |
|---|---|---|
| Meta Grand Teton | Meta-designed open data-center platform; Meta describes large H100 clusters. | Infrastructure design and deployment, rather than a consumer retail SKU. |
| NVIDIA DGX H100 | NVIDIA documents an eight-H100 system. | A concrete eight-GPU product reference, but not evidence that it is the same machine as Grand Teton. |
| Hosted H100 capacity | NVIDIA named AWS, Microsoft Azure, and Oracle Cloud Infrastructure among providers introducing H100 instances or clusters. | A way to use H100 compute without buying and operating a complete data-center system; availability and price depend on provider, region, and date. |
How large Meta’s H100 deployments are
In 2024, Meta Engineering reported 24,576 NVIDIA Tensor Core H100 GPUs in each of two announced clusters. Meta also said more than 16,000 H100 GPUs were used to train Llama 3.1 405B. Those figures describe cluster-scale computing; they should not be mistaken for the GPU count in one server.
Meta’s 2024 infrastructure article also set a historical target of 350,000 H100 GPUs by the end of 2024. That was a roadmap target stated at the time, not a verified current count.
What changed compared with Meta’s earlier Zion EX platform
In its 2022 comparison, Meta and NVIDIA reported that Grand Teton offered four times the host-to-GPU bandwidth, twice the compute and data-network bandwidth, and twice the power envelope of Zion EX. These are comparisons to Meta’s earlier platform, not universal performance multipliers for every workload or every H100 server.
The design brings accelerator trays, power, system management, and fabric interfaces together to simplify provisioning and deployment. For the announced cluster designs, Meta described 400 Gbps network endpoints using either RoCE Ethernet or NVIDIA Quantum InfiniBand. Those are alternative fabric choices, not interchangeable labels: buyers need to assess the network and software stack as part of the system.
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What eight H100 GPUs can do
H100 GPUs are data-center accelerators for demanding AI workloads. NVIDIA’s Hopper architecture includes a Transformer Engine that supports FP8, a reduced-precision mode intended to support modern AI training and inference. Actual training time or inference capacity depends on the model, software, memory and GPU topology, network, storage, and workload settings; the published material does not establish a general benchmark for a “Grand Teton 8x H100” server.
Meta reports using H100 and Grand Teton infrastructure for large-language-model training, generative-AI research and production, recommender systems, and content understanding. A multi-GPU system can provide substantial parallel compute, but eight GPUs alone do not tell you how quickly a particular model will run or how much data it can hold. GPU memory, how GPUs communicate, and the host and cluster configuration all matter.
Cooling and deployment details are configuration-specific
Meta later described H100-era changes that included GPUs with a 700 W TDP and HBM3 memory, while retaining air cooling in that deployment. These are details of Meta’s described configuration, not specifications to assume for every H100 server. When evaluating a system, confirm its actual GPU power limits, cooling design, facility power requirements, and operating conditions with the system supplier.
How to compare an eight-GPU H100 system
GPU count is only a starting point. Before choosing an owned server, an integrated enterprise system, or rented cloud capacity, compare the parts that determine useful performance and operating complexity:
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- Standard Memory: 40 GB
- Host Interface: PCI Express 4.0
- Cooler Type: Passive Cooler
- Product Type: Graphics Card
- GPU memory and topology: Confirm the memory available per GPU and how the GPUs are connected. A machine with the same accelerator count can behave differently depending on its topology.
- GPU-to-host bandwidth: Check how quickly data can move between the host system and accelerators, particularly for workloads that exchange data frequently.
- Cluster fabric: Determine whether the system uses InfiniBand or Ethernet/RoCE, the endpoint speeds, and whether the software and cluster configuration suit the intended workload.
- Power and cooling: Verify the complete system’s power draw, cooling requirements, and data-center compatibility rather than extrapolating from a GPU’s TDP alone.
- Software and management: Check how provisioning, monitoring, scheduling, and updates are handled, especially if you are building a multi-server cluster.
- Storage and checkpoints: Large training jobs depend on storage throughput and reliable checkpointing as well as accelerator speed.
- Ownership versus rental: Owned hardware requires procurement, facilities, deployment, and operations. Hosted instances avoid buying a full system but have provider-specific availability, pricing, and configuration.
Meta’s engineering descriptions underline why these choices are co-design problems: networking, storage, cooling, and software affect how effectively a cluster can use its GPUs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you buy a Grand Teton machine?
Meta’s public material does not provide a consumer price, retail SKU, or benchmark for a “Grand Teton 8x H100 machine.” The documented subject is an open platform and enterprise-scale deployments. A specific purchasable system, if offered through an OEM or integrator, would depend on that supplier’s configuration and current availability; do not assume it is identical to Meta’s deployed hardware.
If you want to purchase physical hardware, the clearer product query is “NVIDIA H100 Tensor Core GPU,” followed by a check of the complete system configuration. DGX H100 is the closest documented eight-GPU product reference in the supplied material, but it is not a substitute name for Grand Teton. Check seller authenticity, form factor, warranty, and what is actually included before ordering.
When hosted H100 compute makes more sense
If you need H100 capacity but do not want to acquire and operate enterprise hardware, compare hosted instances or clusters from providers offering them. NVIDIA’s announcement named AWS, Microsoft Azure, and Oracle Cloud Infrastructure. Their present-day prices, regional availability, instance specifications, and capacity can change, so verify those details directly with the provider before committing.
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