Multimodal Approaches Compared:

Approach 1: Bolted-on (GPT-4 Vision)
Text model + Vision model -> Combined output
Two separate systems, limited integration

Approach 2: Native Multimodal (Gemini)
Single model trained on text + images + audio + video
From the first training step, all modalities are unified
True understanding across modalities

Approach 3: Cascade (Earlier models)
Image -> Description -> Text model -> Answer
Lost information at each stage

Gemini Architecture:

+-----------------------------------------------------+
|              Unified Transformer                     |
|                                                      |
|  +----------+  +----------+  +----------+          |
|  |Text      |  |Vision    |  |Audio     |          |
|  |Tokenizer |  |Encoder   |  |Encoder   |          |
|  |          |  |(ViT)     |  |(USM)     |          |
|  | "hello"  |  | 224×224  |  | 16kHz    |          |
|  | -> [155]  |  | patches  |  | waveform |          |
|  +----+-----+  +----+-----+  +----+-----+          |
|       +--------------+-------------+                |
|                      v                              |
|         +------------------------+                  |
|         |  Unified Embedding    |                  |
|         |  Space               |                  |
|         |  (All modalities     |                  |
|         |   share same space)  |                  |
|         +----------+-----------+                  |
|                    v                               |
|         +------------------------+                  |
|         |  Transformer Layers   |                  |
|         |  (MoE architecture)   |                  |
|         |                       |                  |
|         |  Cross-modal attention|                  |
|         |  Text attends to      |                  |
|         |  image tokens and     |                  |
|         |  vice versa           |                  |
|         +----------+-----------+                  |
|                    v                               |
|         +------------------------+                  |
|         |  Output Generation    |                  |
|         |  (Text, image, audio) |                  |
|         +------------------------+                  |
+-----------------------------------------------------+

Video Processing Pipeline:

Video Input (e.g., 2-hour movie)
       |
       v
+------------------+
|  Frame Sampling  | <- Sample 1 frame per second
|  (7,200 frames   |   for a 2-hour video
|   for 2 hours)   |
+--------+---------+
         |
         v
+------------------+
|  Visual Encoder  | <- Each frame -> embedding
|  (Per-frame)     |   7,200 embeddings
+--------+---------+
         |
         v
+------------------+
|  Temporal        | <- Understands sequence
|  Attention       |   "First X happened,
|  Layers          |    then Y, then Z"
+--------+---------+
         |
         v
+------------------+
|  Query Processing| <- Answer questions about
|  with context    |   the entire video
+------------------+

Example:
Q: "What color shirt was the person wearing at 1:23:45?"
A: "At 1:23:45, the person was wearing a blue shirt."

What 1M Tokens Looks Like:

1M tokens ≈ 750,000 words
1M tokens ≈ 10,000 pages of text
1M tokens ≈ 30 novels
1M tokens ≈ 1,000 research papers

Comparison:
GPT-4o:     128K tokens ≈ 96,000 words ≈ 12 novels
Claude:     200K tokens ≈ 150,000 words ≈ 20 novels
Gemini:   1,000K tokens ≈ 750,000 words ≈ 30 novels

Use cases for 1M context:
- Analyze entire codebases (millions of lines)
- Process complete books or document sets
- Analyze long videos (hours of footage)
- Cross-reference across hundreds of documents

Handling 1M tokens efficiently:

Standard Attention: O(n²)
1M tokens -> 1 trillion operations (impossible!)

Gemini uses:
1. Grouped Query Attention (GQA)
   - Reduces KV cache by 8x
   - Memory: O(n × g) where g << n

2. Sliding Window Attention
   - Local attention for nearby tokens
   - Global attention for distant tokens

3. Sparse Attention Patterns
   - Not all tokens attend to all tokens
   - Learned patterns for efficient attention

4. Gradient Checkpointing
   - Trade compute for memory
   - Recompute activations during backward pass

Result: 1M tokens fit in ~128GB VRAM (TPU pod)

Image Analysis Example:

Input: [Photo of a restaurant menu] + "What are the vegetarian options?"

Gemini processes:
1. Encodes image patches (224×224 each)
2. OCR text recognition on menu
3. Understands food categories
4. Identifies vegetarian items
5. Generates response

Output: "The vegetarian options on this menu are:
- Margherita Pizza ($12)
- Mushroom Risotto ($15)
- Vegetable Curry ($13)
- Caprese Salad ($9)"

Video Analysis Example:

Input: [2-hour cooking video] + "Summarize the recipe steps"

Gemini processes:
1. Samples frames throughout video
2. Recognizes cooking actions
3. Identifies ingredients
4. Tracks temporal sequence
5. Generates recipe summary

Output: "Here's the recipe from the video:
Step 1: Preheat oven to 375°F
Step 2: Mix flour, sugar, and baking powder
Step 3: Add eggs and milk, whisk until smooth
...
Step 12: Bake for 25 minutes until golden"

Audio Analysis Example:

Input: [Audio recording of a meeting] + "What were the key decisions?"

Gemini processes:
1. Transcribes audio (automatic)
2. Identifies speakers
3. Extracts key topics
4. Identifies decisions and action items
5. Generates summary

Output: "Key decisions from the meeting:
1. Launch date moved to March 15
2. Budget approved for $50K
3. Alice will lead the marketing campaign
4. Bob to handle technical implementation"