| Component | Formula / Architecture Basis | Per GPU | Cluster Total |
|---|---|---|---|
| Model Weights | 314B Parameters × 1.0 byte (FP8) | 39.2 GB | 314.0 GB |
| KV Cache Memory | 32 streams × 32,768 context (GQA 8 heads) | 34.1 GB | 272.6 GB |
| Activations & CUDA Context | PyTorch context, scratchpad, NCCL communication buffers | 7.5 GB | 60.0 GB |
| Total Allocated VRAM | Weights + KV Cache + Overheads | 80.8 GB | 646.6 GB |
| Available Free Headroom | Remaining headroom before Out-Of-Memory (OOM) | 60.2 GB | 481.4 GB |
Memory-Bound vs Compute-Bound Phases
Large Language Models execute in two distinct operational regimes: Prefill (prompt ingestion, compute-bound matrix multiplies where Tensor Cores dominate) and Decode (token generation, memory-bandwidth bound where memory bus speed dictates latency). High-bandwidth memories like HBM3e (4.8 TB/s on H200 vs 8.0 TB/s on B200) directly accelerate the generation speed of coding and reasoning agents.
Mixture of Experts (MoE) Memory Advantage
Frontier architectures like Grok 4.7, DeepSeek V3, and Mixtral decouple total parametric capacity from compute cost per token. While all parameters (e.g. 314B) must reside in GPU memory across the cluster, only a fraction of feed-forward expert weights (e.g. 40B active) are accessed per generated token, providing high intelligence with high token speeds.
Serving Economics & Tokens per Dollar
Maximizing tokens per dollar requires saturating the GPU memory bus with continuous batching without exceeding VRAM capacity. FP8 quantization halves weight memory compared to FP16, leaving headroom for extended KV cache contexts (32k–128k tokens) essential for enterprise codebase analysis and multi-step tool reasoning.