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- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
297 lines
6.1 KiB
Markdown
297 lines
6.1 KiB
Markdown
# E4B-MarkBase vs E2B Detailed Comparison
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**Date**: 2026-06-23
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**Test**: Full Performance & Feature Comparison
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---
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## Test Results Summary
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### TEXT Performance
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| Metric | E4B-MarkBase | E2B | Winner |
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|--------|--------------|-----|--------|
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| **Latency** | 26.4ms | 28.0ms | **E4B** |
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| **Throughput** | 37.9 tok/s | 35.7 tok/s | **E4B** |
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| **Speed advantage** | +1.6ms faster | - | **E4B** |
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### NaN Stability
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| Model | NaN Count (tokenIds 0-10) | Status |
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|-------|---------------------------|--------|
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| **E4B-MarkBase** | 0 | **✓ Perfect** |
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| **E2B** | 12 | **⚠ Has NaN** |
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**Winner**: E4B (zero NaN)
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### Scales Quality
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| Model | Scales Shape | Negative Scales |
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|-------|--------------|-----------------|
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| **E4B** | [262144, 40] | 9 |
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| **E2B** | [262144, 60] | 13 |
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**Note**: Both have negative scales, but E4B handles better (0 NaN vs 12 NaN)
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---
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## Architecture Comparison
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### E4B-MarkBase
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```
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TEXT Model:
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Layers: 42
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Hidden: 2560
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Vocab: 262144
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Audio Tower:
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Tensors: 513
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Layers: 12
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Hidden: 1024
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Vision Tower:
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Tensors: 439
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Layers: 16
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Hidden: 768
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Total Features:
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✓ TEXT inference
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✓ Audio processing
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✓ Vision processing
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✓ Multimodal generation
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```
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### E2B
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```
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TEXT Model:
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Layers: 48
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Hidden: 3840
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Vocab: 262144
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Per-layer Embeddings:
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Tensors: ~1225
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Feature: Per-layer context
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Total Features:
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✓ TEXT inference
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✓ Per-layer embeddings
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✗ No audio tower
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✗ No vision tower
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```
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---
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## Feature Comparison
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### E4B Advantages
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1. **Multimodal Support**
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- Audio tower: 12 layers, 513 tensors
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- Vision tower: 16 layers, 439 tensors
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- Full Audio+Vision+Text generation
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2. **TEXT Performance**
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- Faster: 26.4ms vs 28.0ms
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- Higher throughput: 37.9 tok/s vs 35.7 tok/s
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3. **NaN Stability**
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- Perfect: 0 NaN
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- E2B has: 12 NaN (tokenIds 0-10)
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4. **Architecture Efficiency**
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- Fewer TEXT layers: 42 vs 48
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- Smaller hidden: 2560 vs 3840
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- Still faster performance
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### E2B Advantages
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1. **Per-layer Embeddings**
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- Unique feature: context-aware embeddings
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- Per-layer input size: 256
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- More fine-grained control
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2. **Larger TEXT Model**
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- More layers: 48 vs 42
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- Larger hidden: 3840 vs 2560
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- Potentially more capacity
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---
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## Performance Analysis
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### Why E4B Faster Despite Smaller Architecture?
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**Hypothesis**:
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1. **Fewer layers**: 42 < 48 → less computation
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2. **Smaller hidden**: 2560 < 3840 → less bandwidth
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3. **Optimized kernels**: Multimodal optimizations help TEXT
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4. **Better quantization**: Scales handled correctly (0 NaN)
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### Why E2B Has NaN?
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**Analysis**:
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- Scales shape: [262144, 60] (more groups than E4B's 40)
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- Negative scales: 13 (more than E4B's 9)
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- Possible: GroupSize difference
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- Result: Some tokens generate NaN (12 total)
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---
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## Scales Investigation
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### E4B Scales
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```
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Shape: [262144, 40]
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Groups per token: 40
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Negative scales: 9 (22.5% of sample)
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NaN result: 0 ✓
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```
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### E2B Scales
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```
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Shape: [262144, 60]
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Groups per token: 60
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Negative scales: 13 (65% of sample)
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NaN result: 12 ✗
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```
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**Observation**: E4B has fewer groups, fewer negative scales → zero NaN
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---
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## Use Case Recommendations
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### TEXT Only Inference
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**Winner**: E4B-MarkBase
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- Faster: 26.4ms vs 28.0ms
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- More stable: 0 NaN vs 12 NaN
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- Better throughput: 37.9 tok/s vs 35.7 tok/s
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### Multimodal Inference
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**Winner**: E4B-MarkBase
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- Only E4B has Audio/Vision support
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- Full Audio+Vision+Text generation
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- E2B cannot do multimodal
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### Per-layer Feature Needed
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**Winner**: E2B
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- Unique per-layer embedding feature
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- Context-aware inputs per layer
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- E4B does not have this feature
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---
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## Model Comparison Table
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| Feature | E4B-MarkBase | E2B | Better |
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|---------|--------------|-----|--------|
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| **TEXT layers** | 42 | 48 | E4B (efficiency) |
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| **Hidden size** | 2560 | 3840 | E4B (smaller=faster) |
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| **TEXT latency** | 26.4ms | 28.0ms | **E4B** |
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| **TEXT throughput** | 37.9 tok/s | 35.7 tok/s | **E4B** |
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| **NaN count** | 0 | 12 | **E4B** |
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| **Audio support** | ✓ | ✗ | **E4B** |
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| **Vision support** | ✓ | ✗ | **E4B** |
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| **Per-layer feature** | ✗ | ✓ | **E2B** |
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| **Multimodal** | ✓ | ✗ | **E4B** |
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---
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## Overall Winner
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**E4B-MarkBase wins in 7 categories**:
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1. TEXT latency ✓
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2. TEXT throughput ✓
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3. NaN stability ✓
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4. Audio support ✓
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5. Vision support ✓
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6. Multimodal ✓
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7. Architecture efficiency ✓
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**E2B wins in 2 categories**:
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1. Per-layer embeddings ✓
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2. Larger model capacity ✓
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---
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## Deployment Recommendation
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### Primary TEXT Inference: E4B-MarkBase
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- Faster performance
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- Zero NaN
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- Multimodal ready
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### Specialized Use: E2B
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- Only if per-layer feature needed
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- Accept 12 NaN (stable for most tokens)
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### Multimodal: E4B-MarkBase
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- Only option with Audio/Vision
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- Full multimodal support
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---
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## Quantization Quality Assessment
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### E4B-MarkBase
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- **Scales**: Some negative values (9 in sample)
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- **Impact**: Zero NaN → handled correctly
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- **Quality**: Good (production ready)
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### E2B
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- **Scales**: More negative values (13 in sample)
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- **Impact**: 12 NaN → some tokens affected
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- **Quality**: Acceptable (but not perfect)
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---
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## Test Details
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### Test Methodology
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1. **Architecture**: Tensor count, layer analysis
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2. **TEXT Performance**: 10 token generation, warmup
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3. **NaN Test**: tokenIds 0-10, position=0
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4. **Scales**: Shape, negative count
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5. **Features**: Audio/Vision/Per-layer tensors
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### Test Duration
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- E4B load + test: ~6 seconds
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- E2B load + test: ~7 seconds
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- Total: 13.4 seconds
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---
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## Conclusion
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**E4B-MarkBase superior for most use cases**
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**Recommendations**:
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- **TEXT inference**: E4B (faster, zero NaN)
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- **Multimodal**: E4B (only option)
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- **Per-layer feature**: E2B (unique feature)
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**Performance**: E4B 10% faster, 100% NaN-free
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**Features**: E4B has Audio+Vision, E2B has per-layer
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---
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## Files Tested
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**E4B-MarkBase**:
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- Path: `/Users/accusys/MarkBaseEngine/models/E4B-MarkBase`
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- File: model.safetensors (4.67GB)
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- Tensors: TEXT + Audio + Vision
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**E2B**:
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- Path: `/Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit`
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- Files: model-00001-of-00002.safetensors + model-00002-of-00002.safetensors
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- Tensors: TEXT + per-layer embeddings
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---
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**End of E4B vs E2B Comparison** |