LLM Cluster Quantization & VRAM Topology Simulator

Model Architecture Preset

1. Model Parameters & Precision

Total Parameters 2800 Billion

Precision & Quantization

Active MoE Params / Token 320 Billion (11%)

2. Cluster Hardware & Inference Load

Target Accelerator Node

Total GPUs

Tensor Parallel (TP)

Context Window Length 32k tokens

Concurrent Request Batch Size 64 requests

Hardware Configuration Feasible

Model fits comfortably on 8x NVIDIA B300 nodes with 100 GB VRAM headroom remaining per GPU.

65% Utilized

Per-GPU VRAM Allocation Stack

Interactive breakdown of VRAM across Model Weights, KV Cache, Activations, & Overhead

Per-GPU Limit: 288 GB

Weights 175.0 GB

KV Cache 8.0 GB

Activations 3.2 GB

Overhead 1.8 GB

Headroom 100.0 GB

Cluster Telemetry & Memory Budget Manifest

Live Hardware Audit

Memory Category	Per-GPU Allocation	Total Cluster Footprint	Computation Formula / Notes
Model Weights	175.00 GB	1400.00 GB	Params × Bits/Param + 1.2% Quant Metadata
KV Cache Allocation	8.00 GB	64.00 GB	2 × Layers × Heads × Dim × Batch × Context × Precision
Activation Memory	3.20 GB	25.60 GB	Forward pass tensors (Active MoE layers + TP overhead)
CUDA / PyTorch Overhead	1.80 GB	14.40 GB	Context buffers, PyTorch memory fragmentation (1%)
Total VRAM Allocation	188.00 GB	1504.00 GB	65.28% of 288.00 GB Capacity

Saved GPU memory budgeting arithmetic

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Read the explanation

The saved quantization map assigns eight point five bits per parameter to its FP8 option and sixteen to FP16, with separate metadata multipliers. Bars share zero at forty pixels per bit. These labels and hardware/model presets are source assumptions, not verified product specifications or runtime measurements. Saved model enters twenty-eight hundred billion total parameters and eleven percent active, giving three hundred eight billion active. Bars share zero at point two pixels per billion. The executable weight-storage calculation still uses all twenty-eight hundred billion; activity only enters its activation-memory heuristic. Active compute fraction does not automatically remove inactive expert weights from storage. Eight assumed devices with two hundred eighty-eight capacity units each give twenty-three hundred four cluster units. Bars compare one device and cluster at quarter pixel per unit. The weight byte calculation divides by two to power thirty although its display says GB, so decimal naming and binary conversion must be distinguished. Actual fit depends on weights, KV, activation and fragmentation assumptions; no real GPU execution or hardware capacity is verified.