Multi-Agent GPU VRAM & Tensor Parallelism Cluster Sizer
Compute exact physical GPU memory allocation: Weights, PagedAttention KV-cache, and Tensor Parallelism.
🖥️ Autonomous Agent GPU VRAM & Cluster Sizer
Compute exact VRAM allocation: Model weights, KV-cache scaling, and Tensor Parallelism (TP).
Mathematical Methodology & Benchmark Assumptions
1. Memory Component Breakdown
Total GPU cluster memory required ($M_{\text{total}}$) is partitioned into three discrete pools:
M_weights = (P_params * Bytes_per_param) / (10^9) GB
M_kv_cache = 2 * N_layers * N_heads * Dim_head * Bytes_per_param * Context_len * Batch_size
M_total = (M_weights + M_kv_cache + M_activations) * 1.20 (CUDA Headroom Factor)2. Quantization Precision Standards
- FP8 (E4M3 / E5M2): 1.0 byte per parameter. Default sovereign standard delivering 99.4% BF16 accuracy with 50% memory footprint and 2.1x throughput.
- AWQ / INT4: 0.55 bytes per parameter (including scales and zero-points). High compression for edge inference.
- BF16 (Uncompressed): 2.0 bytes per parameter. Required for precision-sensitive reasoning passes and fine-tuning.
3. Tensor Parallelism (TP) Topologies
When $M_{\text{weights}} > 80\text{ GB}$, models must be sharded across multiple GPUs using high-bandwidth interconnects (NVLink 900 GB/s on H100):
- TP=2: 2x NVIDIA H100 SXM5 (160 GB aggregate VRAM).
- TP=4: 4x NVIDIA H100 SXM5 (320 GB aggregate VRAM).
- TP=8: 8x NVIDIA H100 SXM5 (640 GB aggregate VRAM, full HGX chassis).