The technical reports for Gemini (Google) and GPT-4o (OpenAI) represent the transition from “Modular Multimodality” (connecting a vision encoder to an LLM) to Native Multimodality.
While the exact weights are proprietary, both models utilize a Multimodal Transformer architecture where different modalities (text, images, audio, video) are converted into a unified token stream and processed by a single, massive Transformer backbone.
1. Unified Tokenization
The core “Native” secret is that images are not just “appended” as a single feature vector. Instead, they are quantized into visual tokens (using a ViT-based VQ-VAE or similar) so the LLM perceives them as just another “language.”
2. PyTorch Implementation: The Multimodal Backbone
To implement this, we need a Transformer that can handle an interleaved sequence of different token types.
Python
import torch
import torch.nn as nn
class UnifiedMultimodalTransformer(nn.Module):
def __init__(self, vocab_size, visual_vocab_size, audio_vocab_size, d_model, n_layers):
super().__init__()
# Separate embedding layers for different modalities
self.text_embed = nn.Embedding(vocab_size, d_model)
self.vision_embed = nn.Embedding(visual_vocab_size, d_model)
self.audio_embed = nn.Embedding(audio_vocab_size, d_model)
# Shared Transformer layers
# Gemini/GPT-4o use massive Decoder-only stacks (Pre-LN, GQA)
self.layers = nn.ModuleList([
GPT2Block(d_model, n_heads=16, d_ff=4*d_model, max_len=32768)
for _ in range(n_layers)
])
self.final_norm = nn.LayerNorm(d_model)
# Multiple output heads for generating different modalities
self.text_head = nn.Linear(d_model, vocab_size)
self.vision_head = nn.Linear(d_model, visual_vocab_size)
# GPT-4o specifically focuses on native audio output (very low latency)
self.audio_head = nn.Linear(d_model, audio_vocab_size)
def forward(self, input_ids, modality_mask):
"""
input_ids: Unified sequence of indices
modality_mask: 0 for text, 1 for vision, 2 for audio
"""
# 1. Project all inputs into the same d_model space
embeddings = torch.zeros((*input_ids.shape, self.d_model))
# Vectorized embedding selection based on modality
embeddings[modality_mask == 0] = self.text_embed(input_ids[modality_mask == 0])
embeddings[modality_mask == 1] = self.vision_embed(input_ids[modality_mask == 1])
embeddings[modality_mask == 2] = self.audio_embed(input_ids[modality_mask == 2])
x = embeddings
for layer in self.layers:
x = layer(x)
x = self.final_norm(x)
# Depending on the task, we sample from the appropriate head
return self.text_head(x), self.vision_head(x), self.audio_head(x)
3. Key Technical Advances in 2023-2024
The reports highlight several critical optimizations for these “Omni” models:
- Grouped-Query Attention (GQA): Used extensively in Gemini to maintain a massive context window (up to 1M+ tokens) while keeping the KV cache small enough for fast inference.
- Late Fusion vs. Early Fusion: GPT-4o is “Early Fusion,” meaning it doesn’t use a separate speech-to-text model. It “hears” the audio tokens directly, allowing it to sense emotion and tone (the “Omni” capability).
- RoPE (Rotary Positional Embeddings): Used to handle extremely long sequences without the degradation seen in absolute position embeddings.
4. Comparison: Gemini Pro vs. GPT-4o
| Feature | Gemini 1.5 Pro | GPT-4o (“Omni”) |
| Max Context | 2,000,000 Tokens | ~128,000 Tokens |
| Modality Integration | Video-Native (Spatial tokens) | Audio-Native (Low-latency vocal) |
| Architecture | Mixture-of-Experts (MoE) | Dense or Small-MoE (Speculated) |
| Primary Strength | Deep Reasoning over long docs | Real-time human-like interaction |
Why these are 2024 Milestones
Gemini and GPT-4o ended the era of “Text-only” LLMs. By training on a unified objective (predict the next token, whether it’s a pixel, a phoneme, or a word), these models achieved cross-modal reasoning. For example, they can watch a video of a physics experiment and explain the math behind it in text, or hear a person’s tone of voice and generate a corresponding image of how that person might be feeling.
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