Custom Brain Architecture Designer

Configure custom input dimensions, attention heads, and depth layers.

Llama / GPT Decoder Architecture

Embedding Dimension ($d_{model}$)

Attention Heads ($n_{heads}$)

Transformer Blocks / Depth ($N_L$)

Context Sequence Window ($T_{ctx}$)

FFN Hidden Expansion Ratio

Activation Function

Calculated In-Browser Parameters: ~85,400 Params

Estimated Active Memory (FP32): ~0.34 MB

Browser Real-Time Viability: 100% NATIVE ZERO-FREEZE

500 Billion Parameter Scaler & Blueprint

THEORETICAL SPECS

Test and model frontier architectural scaling up to 500 Billion Parameters. Compare requirements for memory, tensor parallelism, and KV-cache footprints.

Scale Preset 500B Frontier Foundation

Total Weight Count: 500,000,000,000

Model Dimensions: d=16384, L=128, H=128

FP16 Weight Memory: ~1,000 GB (1.0 TB VRAM)

INT4 Quantized Memory: ~250 GB VRAM

Minimum Hardware: 16x NVIDIA H100 (80GB)

Autoregressive Generation (KV-Cache Powered)

STANDBY

Prompt / Starting Prefix

Temperature 0.75

Top-P Nucleus 0.90

Max Tokens 45

Generated continuations will stream here. Rate the generation with thumbs up or down to apply REINFORCE policy gradients in real time.

RLHF Policy Gradient (REINFORCE) Awaiting rollout

Live Chat Mode (Interactive Brain Simulation)

OFFLINE SESSION

System: Train your model on a corpus to test conversational responses.

Export AI as a Self-Contained Web App

Zero External Dependencies

With one click, compile your trained weights, vocabulary maps, and an optimized runtime into a single standalone .html file. Share it with friends or host it on any static server—it runs entirely offline in any browser without needing Python, Node.js, or cloud APIs!

Compiled ChatBot App Features:

Responsive cyberpunk mobile & desktop chat interface
Embedded Float32 forward-pass matrix inference engine
Interactive temperature, top-P, and response length controls
Real-time token streaming with animated typing indicators
Exported file size is under 2MB

Generated ChatBot App Preview

Synapse AI

STANDALONE WEB APP

Hello! I am your custom offline AI. Ask me anything based on my training corpus.

Can you explain self-attention mechanisms?

Synapse: Self-attention computes soft alignment across sequence vectors using dot-product queries and keys.

Type a message...

Send

Multi-Head Causal Attention Heatmap

Head:

Generate sample text to render query-key causal attention heatmaps

Query Tokens (Rows) ➔ Key Tokens (Columns) Scale: 0.0 (Dark) ➔ 1.0 (Purple/Cyan)

2D Dynamic Token Embedding Space (PCA)

W_embed Projection

Real-time linear principal component projection of high-dimensional token representations down to 2D Cartesian coordinates.

Corpus Engine (50,000+ Words Ready)

Loss Dynamics (Train Cyan vs. Holdout Validation Amber)