Estimating VRAM and Deployment Costs for a 2.8-Trillion-Parameter MoE Model

The AI industry is approaching the release of a new large language model with 2.8 trillion parameters, built on a mixture-of-experts (MoE) architecture. As of late July 2026, the full weights and technical report have not yet been published, with an official release expected before July 27. Until the actual weights become available, the inference speed, memory usage, and hardware requirements discussed below are based on a careful estimation using publicly known architectural details and API pricing. Once the weights are released, developers will need to validate these assumptions through standard benchmark testing.

Understanding the Memory Footprint of a 2.8T-Parameter MoE Model

The model contains a total of 2.8 trillion parameters. Its MoE architecture includes 896 individual expert modules, of which only 16 are activated per token during inference—an activation ratio of roughly 1.79%. It is important to note that this ratio applies only to the expert networks, not to the overall parameter activation rate of the model.

The backbone architecture integrates Delta Attention and Attention Residuals, with gated multi-head latent attention as a core component. For deployment, the key metric is the total parameter count, as all 896 experts must be fully loaded into memory before inference begins. Dynamic routing then selects only the relevant 16 experts to perform computations at each step. The activation ratio primarily affects compute load, not the overall memory baseline.

Quantization Strategy and Precise VRAM Estimation

To reduce the memory burden of such a large model, the training pipeline includes quantization-aware training during the supervised fine-tuning (SFT) stage. Weights are stored in MXFP4 format, while activations use MXFP8. Even with 4-bit quantization, the raw weight data alone requires a minimum of approximately 1400 GB of VRAM.

A more detailed calculation reveals that MXFP4 is not a pure 4-bit format. According to the relevant open compute project specification, every 32 4-bit elements require an additional 8-bit scaling factor. Taking this into account, the combined bit count for weights and scaling factors is 2.8 trillion × 4.25 bits ÷ 8, resulting in a total of roughly 1487.5 GB. This figure does not yet include non-4-bit tensors, routing metadata, memory alignment overhead, runtime workspace, or KV cache. When these necessary runtime components are factored in, the actual memory requirement increases further.

As a point of reference, an existing 671-billion-parameter model requires approximately 685 GB of VRAM in FP8 format, including the main weights and output head. Even with AWQ 4-bit quantization, that model needs about 335 GB for weights and an additional 37 GB for activations—totaling close to 400 GB before it can run on a node with 8 H100 GPUs (640 GB total). The new model is more than four times larger, and the minimum memory baseline follows directly from this scale.

The Role of Supernode Architecture in Overcoming Communication Bottlenecks

The official recommendation is to deploy this model on a supernode cluster with at least 64 accelerators. The reasoning centers on the extreme sensitivity of MoE inference to all-to-all interconnect bandwidth.

Because all 896 experts are physically distributed across multiple accelerators, each token’s activations must be routed across the interconnect to the accelerator hosting the selected expert. After computation, the results are sent back. This frequent cross-device data movement introduces significant communication latency. If the interconnect bandwidth is insufficient, latency will consume the compute throughput, effectively idling the accelerators while they wait for data.

A supernode architecture addresses this by creating a single, large communication domain using high-speed point-to-point interconnects or similar low-latency networking. Within this domain, cross-device transfers no longer traverse traditional network boundaries, minimizing latency and ensuring efficient MoE routing.

Hardware and Cost Planning for Enterprise On-Premises Deployment

With a single node approaching 1.5 TB of weight-only VRAM, enterprises planning on-premises deployment must carefully evaluate hardware selection and total cost.

1. Accelerator Selection and Cluster Size
Current high-end accelerators typically offer around 80 GB of VRAM per card. To hold over 1500 GB of base weights, plus KV cache and communication framework overhead, a minimum of 20 to 24 high-end accelerators is required. To provide sufficient throughput for concurrent requests and hardware fault tolerance, a starting configuration of a 64-accelerator cluster—a full supernode—is the more practical baseline.

2. High-Speed Interconnect Costs
Beyond the accelerators themselves, the supernode architecture demands a full all-to-all network topology. The associated switches, cabling, network interface cards, and any necessary data center modifications will account for a substantial portion of the total hardware expenditure.

3. Storage and Infrastructure Requirements
A cluster of this scale requires a high-throughput shared file system to load weight files measured in the hundreds of gigabytes. Additionally, a single node can consume tens of kilowatts of power, making liquid cooling systems, high-density racks, and uninterruptible power supplies essential capital expenditures.

For enterprises that want to validate the model quickly or use it on a flexible basis, purchasing physical hardware involves both high fixed costs and lengthy procurement cycles. Established cloud providers offering high-performance compute clusters and container platforms allow direct access to pre-configured supernode instances, reducing the burden of upfront capital investment. The elastic scaling capabilities of cloud infrastructure also help lower ongoing operational costs during periods of lower demand.

Summary and Outlook

Deploying a 2.8-trillion-parameter MoE model on-premises is not simply a matter of adding more GPUs. It is a system-level engineering effort that involves maximizing memory efficiency, applying appropriate quantization techniques, redesigning cross-node communication, and upgrading data center infrastructure.

The base weight memory requirement alone approaches 1.5 TB, meaning traditional 8-GPU server architectures are no longer viable for single-node deployment. A 64-GPU supernode cluster becomes a practical minimum. When assessing deployment costs, enterprises must account not only for the direct purchase of dozens of top-tier accelerators but also for the hidden costs of high-speed networking and cooling infrastructure. After the official weights are released, it is advisable for development teams to run small-scale communication and memory benchmarks on target hardware first, using the resulting data to guide the final large-scale deployment architecture.