AdaptJax™ Technology¶

The Translation Layer That Unlocks Google's TPU Supercomputers

AdaptJax™ is Adaptensor's proprietary JAX-native embedding engine that runs directly on Google Cloud TPUs without TensorFlow or PyTorch dependencies.

The Problem: The Static Shape Barrier¶

Google's TPUs deliver exceptional performance—180+ TFLOPS per v2-8 chip—but they're notoriously difficult to use. TPUs require:

• Static tensor shapes for XLA compilation
• Fixed batch sizes determined at compile time
• Predictable control flow without dynamic branching

Most AI workloads (PyTorch, JAX, real-world inference) are inherently dynamic:

• Variable-length sequences (text, audio, time series)
• Changing batch sizes per request
• Conditional model execution
• Ragged inputs from production systems

This mismatch causes:

Result: The world's most powerful AI hardware sits underutilized behind a usability wall.

The Solution: AdaptJax Middleware¶

AdaptJax inserts a translation layer between your code and the TPU that:

1. Normalizes shapes via adaptive bucketing
2. Enforces XLA-friendly control flow patterns
3. Optimizes memory for TPU HBM architecture ┌─────────────────────────────────────────────┐ │ Your Dynamic Workloads │ │ (variable sequences, ragged batches) │ └─────────────────┬───────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ AdaptJax™ Middleware │ │ ┌─────────────────────────────────────┐ │ │ │ Adaptive Bucketing & Padding │ │ │ │ • Variable inputs → Fixed shapes │ │ │ │ • {128, 256, 512} token buckets │ │ │ │ • Automatic mask generation │ │ │ └─────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────┐ │ │ │ JAX/Flax Transformer │ │ │ │ • XLA-optimized forward pass │ │ │ │ • Batched inference │ │ │ │ • Compiled graph reuse │ │ │ └─────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────┐ │ │ │ TPU v2-8 Execution │ │ │ │ • 180+ TFLOPS compute │ │ │ │ • 64GB HBM memory │ │ │ │ • 8 TPU cores parallel │ │ │ └─────────────────────────────────────┘ │ └─────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────┐ │ 384-dim Semantic Embeddings │ └─────────────────────────────────────────────┘

Three Pillars of AdaptJax¶

Pillar 1: Adaptive Bucketing & Padding¶

Converts dynamic inputs into a small set of fixed bucket sizes:

# Internal bucketing logic
BUCKETS = [128, 256, 512]

def pad_to_bucket(sequence, bucket_size):
    """Pad sequence to nearest bucket size."""
    padded_length = ((len(sequence) // bucket_size) + 1) * bucket_size
    padding = padded_length - len(sequence)
    return sequence + [PAD_TOKEN] * padding, create_mask(len(sequence))

Benefits:

Pillar 2: Tensor Adapters (PEFT/LoRA-style)¶

Hot-swap small adapter weights per customer while keeping one backbone model resident:

# Adapter injection pattern
output = (W @ x) + (A @ B @ x) * scale

Where:

• W = frozen backbone weight (billions of parameters)
• A, B = small adapter matrices (millions of parameters)
• scale = normalization factor

Benefits:

• Fast context switches between users (~10ms)
• Single backbone serves all tenants
• 90% memory reduction vs per-user models

Pillar 3: Elastic Compute (Entropy-Based Early Exit)¶

Simple queries exit early, complex ones use full depth:

def entropy(logits):
    """Low entropy = high confidence = exit early."""
    probs = jax.nn.softmax(logits)
    return -jnp.sum(probs * jnp.log(probs + 1e-9))

• "What is 2+2?" → exits after 2 layers
• "Explain quantum entanglement" → uses all 12 layers
• XLA-compatible via fixed loop bounds + masking

Performance Specifications¶

Comparison: AdaptJax vs Alternatives¶

AdaptJax vs OpenAI Embeddings¶

AdaptJax vs Sentence Transformers (GPU)¶

Code Example: AdaptJax Embedder¶

"""
AdaptJax Embedder - JAX-Native Document Embeddings for TPU
==========================================================
Pure JAX/Flax implementation that runs natively on TPU without
TensorFlow or PyTorch dependencies.
"""
import jax
import jax.numpy as jnp
from flax import linen as nn

class TransformerBlock(nn.Module):
    """Single transformer block with self-attention and FFN."""
    hidden_size: int = 384
    num_heads: int = 6
    mlp_dim: int = 1536

    @nn.compact
    def __call__(self, x, mask=None):
        # Self-attention with residual
        attn_out = nn.MultiHeadDotProductAttention(
            num_heads=self.num_heads,
            qkv_features=self.hidden_size,
        )(x, x, mask=mask)
        x = nn.LayerNorm()(x + attn_out)

        # Feed-forward with residual
        ff_out = nn.Dense(self.mlp_dim)(x)
        ff_out = nn.gelu(ff_out)
        ff_out = nn.Dense(self.hidden_size)(ff_out)
        x = nn.LayerNorm()(x + ff_out)

        return x


class AdaptJaxEmbedder(nn.Module):
    """Lightweight transformer encoder for TPU inference."""
    vocab_size: int = 30522
    hidden_size: int = 384
    num_layers: int = 6

    @nn.compact
    def __call__(self, input_ids, attention_mask):
        x = nn.Embed(self.vocab_size, self.hidden_size)(input_ids)

        for _ in range(self.num_layers):
            x = TransformerBlock()(x, mask=attention_mask)

        # Mean pooling
        return jnp.mean(x * attention_mask[:, :, None], axis=1)


# JIT compile for TPU
@jax.jit
def embed_batch(params, input_ids, attention_mask):
    return model.apply(params, input_ids, attention_mask)

# Parallel across all 8 TPU cores
embeddings = jax.pmap(embed_batch)(params, batched_inputs, batched_masks)

Patent Protection¶

AdaptJax™ technology is protected under U.S. Patent Application 63/930,053.

The patent covers:

"AdaptensorCore middleware - the translation layer that unlocks Google's TPU supercomputers for the 99% of AI teams who don't want to rewrite their code."

Next Steps¶

• Quick Start Guide - Get running in 5 minutes
• API Reference - Complete endpoint documentation
• Python SDK - Client library reference