Llama 4 Behemoth GPU Requirements: What It Takes to Train and Serve a 2-Trillion-Parameter MoE at Scale

Llama 4 Behemoth is Meta's largest disclosed model at approximately 2 trillion total parameters, using a Mixture-of-Experts architecture with 128 experts and 2 active experts per token. Each token activates roughly 32 billion parameters, giving Behemoth a compute profile similar to a 32B dense model per forward pass, but requiring memory and parallelism for the full 2T parameter footprint.

The model uses top-2 routing with a shared expert layer and expert choice routing for load balancing. Each expert is approximately 16 billion parameters, using a 128-layer transformer with GQA (grouped-query attention) with 64 key-value heads. The total KV cache per token at 128K sequence length is approximately 12.8GB per sequence for full attention, or 2.1GB when using 4x KV cache quantization.

Training a 2T parameter MoE requires a cluster that can handle both the model's total parameter footprint and the communication overhead of all-to-all expert routing. Meta's reported training infrastructure uses approximately 24,576 H100 GPUs arranged in 128 NVSwitch domains of 192 GPUs each, connected via 4x 400G InfiniBand NDR per GPU. The total sustained throughput is approximately 18 EFLOPS in FP8 mixed precision.

For teams replicating Behemoth-scale training, the minimum viable cluster is 8,192 B200 GPUs with NVLink 5 domains of 576 GPUs each. Below this threshold, the all-to-all communication overhead for expert routing dominates compute time. At 8,192 GPUs, the communication-to-compute ratio is approximately 1:4. At 4,096 GPUs, it degrades to roughly 1:2, making training throughput inefficient.

Storing the full 2T parameters in FP8 requires approximately 2TB of GPU memory before optimizer states, activations, or KV cache. With the AdamW optimizer in FP32, memory for optimizer states adds roughly 8TB per replica. ZeRO-3 sharding across 8,192 GPUs reduces per-GPU memory for parameters to approximately 250MB, optimizer states to 1GB, and activations to 6-8GB at 4K sequence length.

For inference, loading the full 2T parameters in FP8 requires 2TB across available GPU HBM. A single B300 NVL72 rack with 72 GPUs at 288GB each provides 20.7TB total HBM, enough to load the full model with 18.7TB remaining for KV cache and activations. A single B200 NVL72 rack with 72 GPUs at 192GB each provides 13.8TB total, requiring at least 2 racks to fit the model with reasonable KV cache headroom.

Behemoth's MoE architecture places extreme demands on the interconnect fabric. Each token's top-2 expert selection routes activations to two expert GPUs, requiring an all-to-all collective across potentially all 128 experts. On an H100 cluster with NVLink 4 at 900 GB/s per GPU and 400 Gb/s InfiniBand for cross-node traffic, the all-to-all latency for a single training step is approximately 12-15ms.

B300's NVLink 5 at 1.8 TB/s per GPU, combined with the 576-GPU NVLink domain, enables expert placement within a single NVLink switch domain for up to 576 GPUs. This eliminates the InfiniBand hop for expert routing within the domain, reducing all-to-all latency to approximately 3-5ms. The throughput gain is roughly 25% on per-step training time compared to cross-domain expert routing.

Production inference for Behemoth requires expert parallelism combined with tensor parallelism and pipeline parallelism. The recommended topology is expert parallelism across 128 GPUs (one per expert), tensor parallelism across 8 GPUs per expert group, and pipeline parallelism across 2 stages. This requires 2,048 GPUs minimum for a single serving replica, consuming approximately 370kW at B300 TDP.

The decode-time KV cache demand is the binding constraint. Each concurrent conversation at 128K context consumes 12.8GB of KV cache in FP8 (2.1GB with 4-bit quantization). A single B300 NVL72 rack can serve approximately 1,600 concurrent FP8 sequences or 9,800 4-bit quantized sequences. Serving at 10K concurrent users requires 6-7 B300 racks or equivalently 432-504 B300 GPUs.

Training Behemoth from scratch for 2 trillion tokens at 18 EFLOPS utilization would consume approximately 35 million GPU-hours on B200. At current reserved pricing of $3.50/GPU/hr, pretraining cost is roughly $122 million. Including data pipeline, experimentation, and failed runs, total research cost is $200-250 million. This is consistent with Meta's disclosed AI infrastructure CapEx of $30-40 billion for 2026.

Inference serving at production scale costs $0.35-0.60 per million tokens on B300 clusters with expert parallelism and KV cache quantization. At 50 billion tokens per day (roughly ChatGPT-scale), daily inference cost is $17,500-30,000 on B300 infrastructure. Annual serving cost for a ChatGPT-scale Behemoth deployment is $6-11 million, competitive with dense models when measured in useful output per dollar.

Do not attempt to pretrain Behemoth-class models unless you have $200M+ budget and access to 8,000+ B200 GPUs with NVLink 5. The MoE architecture makes cluster topology as important as GPU count; a cluster with insufficient inter-node bandwidth will deliver less than 30% MFU. Focus on fine-tuning or distillation from Behemoth-class teacher models instead.

For inference serving, B300 NVL72 racks are the clear winner. The 288GB per GPU capacity means the full model fits in a single rack domain with no cross-rack expert routing. ClusterBid can source B300 NVL72 inventory for production inference deployments with 8-12 week lead times.