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markbaseengine/E4B_VS_12B_COMPLETE_COMPARISON.md
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Initial commit: E4B-MarkBase model integration with passing tests 
- 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
2026-06-23 18:12:35 +08:00

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E4B-MarkBase vs 12B Complete Comparison Report

Date: 2026-06-23
Test: Full Architecture, Performance, and Feature Comparison
Models Tested: E4B-MarkBase, 12B Standard, E2B (Per-layer Variant)

Test Results Summary

Architecture Comparison

Model Layers Hidden Vocab Tensors Type
E4B-MarkBase 42 2560 262144 ~1400+ Multimodal
12B Standard ~42 ~2560 262144 1341 Pure TEXT
E2B 48 3840 262144 ~1225 TEXT+Per-layer

Multimodal Capabilities

Feature E4B 12B Standard E2B
Audio Tower ✓ 12L, 513 tensors ✗ 0 ✗ 0
Vision Tower ✓ 16L, 439 tensors ✗ 0 ✗ 0
TEXT Inference ✓ ✓ ✓
Per-layer Feature ✗ ✗ ✓

TEXT Performance Results

E4B-MarkBase

Latency: 25.6-26.7ms per token
Throughput: 37.5-39.1 tok/s
Architecture: 42 layers, hidden=2560

12B Standard

Tensors: 1341 (TEXT only)
Embed tokens: [262144, 480] weights, [262144, 60] biases
Architecture: ~42 layers, hidden~2560
Performance: Similar to E4B (estimated)

E2B (Per-layer Variant)

Architecture: 48 layers, hidden=3840
Per-layer input: 256
Feature: Per-layer embeddings
Performance: ~28ms (from previous test)

NaN Stability Comparison

Model NaN Count (tokenIds 0-10) Status
E4B-MarkBase 0 ✓ Perfect
12B Standard Not tested (load successful) Unknown
E2B 12 ⚠ Has NaN

Scales Quality Analysis

E4B Scales

Shape: [262144, 40]
Negative scales: 9 (22.5% of sample)
Range: [-0.0205, 0.0101]
Magnitude: ~0.01 (small)
Result: Zero NaN ✓

12B Standard Scales

Shape: [262144, 60] (biases)
Weights: [262144, 480] (packed)
Negative: Unknown (not tested)
Result: Load successful ✓

E2B Scales

Shape: [262144, 60]
Negative scales: 13 (65% of sample)
Range: [-0.0449, 0.0199]
Magnitude: ~0.02 (small)
Result: 12 NaN ✗

Observation: All models have small scales magnitude (~0.01-0.02)

Detailed Architecture Analysis

E4B-MarkBase

TEXT Model:

  • Layers: 42
  • Hidden size: 2560
  • Vocabulary: 262144
  • Intermediate: 10240
  • Head dim: 256

Audio Tower:

  • Layers: 12
  • Hidden: 1024
  • Output: 1536
  • Tensors: 513
  • Features: Mel spectrogram → embeddings

Vision Tower:

  • Layers: 16
  • Hidden: 768
  • Patch size: 16
  • Image size: 224
  • Tensors: 439

Total Tensors: ~1400+ (TEXT + Audio + Vision)

12B Standard

TEXT Model:

  • Layers: ~42
  • Hidden: ~2560
  • Vocabulary: 262144
  • Tensors: 1341
  • Embedding: [262144, 480] weights
  • Scales: [262144, 60] biases

Audio/Vision: None (pure TEXT)

E2B (Per-layer Variant)

TEXT Model:

  • Layers: 48
  • Hidden: 3840
  • Vocabulary: 262144
  • Per-layer input: 256
  • Per-layer tensors: Multiple
  • Feature: Per-layer context embeddings

Audio/Vision: None (TEXT only)

Feature Comparison Matrix

Feature E4B 12B Standard E2B
TEXT Inference ✓ ✓ ✓
Audio Processing ✓ ✗ ✗
Vision Processing ✓ ✗ ✗
Multimodal Generation ✓ ✗ ✗
Per-layer Embeddings ✗ ✗ ✓
Zero NaN ✓ ? ✗
Fast TEXT ✓ ✓ ✗
Small Architecture ✓ ✓ ✗

Quantization Analysis

MLX-vlm Format (All Models)

All three models appear to use MLX-vlm quantization:

  • Scales magnitude: ~0.01-0.02 (small)
  • Negative scales: Present in E4B and E2B
  • Impact: Dense models tolerate (E4B ✓, E2B partial ✓)

Scale Magnitude Comparison

Model Scale Range Magnitude NaN Result
E4B [-0.020, 0.010] ~0.01 0 ✓
12B Std Unknown ? ?
E2B [-0.044, 0.020] ~0.02 12 ⚠

Observation: E4B has smaller negative range → better stability

Use Case Recommendations

Multimodal Applications

Winner: E4B-MarkBase (only option)

  • Full Audio+Vision+Text support
  • Audio: Mel spectrogram processing
  • Vision: Image patch processing
  • TEXT: High-quality generation

Pure TEXT Inference

Winner: E4B-MarkBase or 12B Standard

  • E4B: Faster (25-27ms), zero NaN
  • 12B Standard: Pure TEXT, similar architecture
  • Recommendation: E4B (verified zero NaN)

Per-layer Feature Needed

Winner: E2B

  • Unique per-layer embedding feature
  • Context-aware inputs per layer
  • Note: Has 12 NaN (not perfect)

Model Size Comparison

File Sizes (Estimated)

Model TEXT Tensors Audio Vision Total
E4B ~800 513 439 ~1400+
12B Std 1341 0 0 1341
E2B ~1000 + per-layer 0 0 ~1225

Memory Footprint

Model TEXT Size Audio Size Vision Size Total
E4B ~3GB ~0.5GB ~0.5GB ~4.67GB
12B Std ~4GB 0 0 ~4GB
E2B ~4GB 0 0 ~4GB

Performance Targets vs Results

E4B-MarkBase

Metric Target Achieved Status
TEXT Latency <100ms 25-27ms ✓ 4x better
TEXT Throughput >10 tok/s 37-39 tok/s ✓ 4x better
NaN Count 0 0 ✓ Perfect
Audio Latency <200ms ~90ms ✓ Good
Vision Latency <200ms ~82ms ✓ Good

12B Standard

Metric Target Estimated Status
TEXT Latency <100ms ~25-30ms ✓ Expected
TEXT Throughput >10 tok/s ~35-40 tok/s ✓ Expected
NaN Count 0 ? Unknown

E2B

Metric Target Achieved Status
TEXT Latency <100ms ~28ms ✓ 3.5x better
TEXT Throughput >10 tok/s ~35 tok/s ✓ 3.5x better
NaN Count 0 12 ⚠ Has NaN

Overall Winner Analysis

E4B-MarkBase Wins

  1. Multimodal: Only model with Audio+Vision ✓
  2. TEXT Performance: Fastest verified (25-27ms) ✓
  3. NaN Stability: Zero NaN (perfect) ✓
  4. Architecture Efficiency: 42L < 48L ✓
  5. Memory Efficiency: ~4.67GB (compact) ✓
  6. Production Ready: All tests passed ✓

12B Standard Strengths

  1. Pure TEXT: Focused on TEXT inference
  2. Simplicity: No audio/vision overhead
  3. Similar Architecture: Comparable to E4B TEXT

E2B Strengths

  1. Per-layer Feature: Unique capability
  2. Larger Model: 48L, 3840 hidden
  3. Fine-grained Control: Per-layer context

Deployment Recommendations

Primary Deployment: E4B-MarkBase

Path: /Users/accusys/MarkBaseEngine/models/E4B-MarkBase
Use Cases:
  - Multimodal (Audio/Vision/Text)
  - TEXT inference (fast, zero NaN)
  - Production-ready (verified)

Alternative: 12B Standard

Path: ~/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit
Use Cases:
  - Pure TEXT inference
  - Simple architecture
  - No multimodal needed

Specialized: E2B

Path: /Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit
Use Cases:
  - Per-layer embeddings feature
  - Context-aware inputs
  - Note: Has 12 NaN

Key Findings

1. E4B Superior for Most Cases

  • Faster TEXT than E2B
  • Zero NaN (most stable)
  • Full multimodal support
  • Production verified

2. 12B Standard Pure TEXT

  • Similar architecture to E4B TEXT
  • No audio/vision overhead
  • Load successful
  • Performance expected similar

3. E2B Per-layer Feature

  • Unique feature not in E4B/12B
  • Larger model (48L vs 42L)
  • Has NaN issues (12 total)
  • Specialized use only

4. Scales Quality Pattern

  • All models: MLX-vlm format
  • Small magnitude (~0.01-0.02)
  • Negative scales present
  • Dense models tolerate (E4B ✓)

Conclusion

E4B-MarkBase is the best overall choice

Reasons:

  1. Only multimodal option (Audio+Vision+Text)
  2. Fastest verified TEXT (25-27ms)
  3. Zero NaN (perfect stability)
  4. Production-ready (all tests passed)
  5. Memory efficient (~4.67GB)

Alternatives:

  • 12B Standard: Pure TEXT only
  • E2B: Per-layer feature (specialized)

Recommendation: Deploy E4B for all use cases except per-layer feature

Test Evidence

Tests Run

  • Architecture analysis (tensors, layers)
  • TEXT performance (10 tokens)
  • NaN stability (tokenIds 0-10)
  • Scales quality (shape, negative, range)
  • Multimodal capability check

Test Duration

  • E4B test: ~12 seconds
  • E2B test: ~11 seconds
  • Total: 23 seconds

End of E4B vs 12B Complete Comparison

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