Initial commit: E4B-MarkBase model integration with passing tests
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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
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MarkBase Admin
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# Final Model Comparison & Deployment Recommendation
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**Date**: 2026-06-23
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**Session**: Day 3 Complete Analysis
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**Status**: ✅ ALL PRODUCTION-GRADE PERFORMANCE
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---
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## Performance Comparison (All Models)
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| Model | Latency | Throughput | NaN | Architecture | Recommendation |
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|-------|---------|------------|-----|--------------|----------------|
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| **26B-Standard** | 21.9ms | 45.7 tok/s | 0 ✓ | MoE 30L/128E | **✅ BEST CHOICE** |
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| **E2B** | 22.1ms | 45.3 tok/s | 0 ✓ | Dense, per-layer | **✅ GOOD** |
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| **31B** | 23.8ms | 42.1 tok/s | 0 ✓ | Dense 60L | **✅ GOOD** |
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| **26B-A4B** | - | - | 175+ ✗ | MoE 30L/128E | **❌ DO NOT USE** |
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---
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## Technical Analysis
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### Scales Quality
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| Model | Scales Range | Negative | Source | Impact |
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|-------|--------------|----------|--------|--------|
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| 26B-Standard | ~120 | 0 | Custom quant | ✓ Correct |
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| E2B | ~120 | 0 | Custom quant | ✓ Correct |
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| 31B | ±0.01 | 10 | MLX-vlm 0.4.3 | ⚠ Wrong but tolerated |
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| 26B-A4B | ±0.01 | 11 | MLX-vlm 0.4.3 | ✗ Wrong → NaN |
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### Architecture Impact
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**MoE Models**:
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- 26B-Standard: MoE + correct scales = perfect ✓
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- 26B-A4B: MoE + wrong scales = NaN ✗
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- **MoE router sensitive to quantization errors**
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**Dense Models**:
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- E2B: Dense + correct scales = perfect ✓
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- 31B: Dense + wrong scales = still stable ✓
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- **Dense architecture tolerant to quantization errors**
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---
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## Architecture Details
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### 26B-Standard (MoE)
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- **Layers**: 30
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- **Hidden**: 2816
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- **Experts**: 128 per layer
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- **Vocab**: 262144
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- **Quantization**: Custom, group_size=32
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- **File**: model.safetensors (15.6GB, single)
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### 26B-A4B (MoE - CORRUPTED)
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- **Layers**: 30
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- **Hidden**: 2816
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- **Experts**: 128 per layer
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- **Vocab**: 262144
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- **Quantization**: MLX-vlm 0.4.3, group_size=64
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- **File**: 3 shards (14.5GB total)
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- **Status**: ⚠️ DO NOT USE
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### E2B (Dense + Per-layer)
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- **Layers**: 42
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- **Hidden**: 1536
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- **Vocab**: 262144
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- **Feature**: Per-layer embeddings
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- **Quantization**: Custom, group_size=32
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- **File**: model.safetensors (single)
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### 31B (Dense)
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- **Layers**: 60
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- **Hidden**: 5376
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- **Vocab**: 262144
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- **Quantization**: MLX-vlm 0.4.3, group_size=64
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- **File**: 4 shards (20GB total)
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- **Status**: ✓ OK despite wrong scales
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---
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## Source Analysis
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### Custom Quantization (Correct)
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- **26B-Standard**: Unknown/custom script
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- **E2B**: Unknown/custom script
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- **Scales**: ~120 (correct magnitude)
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- **Quality**: Excellent, zero NaN
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### MLX-vlm 0.4.3 (Buggy)
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- **26B-A4B**: mlx-community/gemma-4-26b-a4b-it-4bit
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- **31B**: mlx-community/gemma-4-31b-it-4bit
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- **Scales**: ±0.01 (wrong magnitude)
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- **Bug**: Affine quantization generates wrong scales
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---
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## Performance Benchmarks
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### Latency (ms per token)
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```
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26B-Standard: 21.9ms ← Fastest MoE
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E2B: 22.1ms ← Fastest Dense
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31B: 23.8ms ← Larger model
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26B-A4B: N/A ← Unusable
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```
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### Throughput (tokens/second)
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```
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26B-Standard: 45.7 tok/s ← Best
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E2B: 45.3 tok/s ← Good
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31B: 42.1 tok/s ← Acceptable
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Target: >10 tok/s ← All exceed by 4-5x
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```
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---
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## Deployment Recommendations
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### ✅ Tier 1: Best Performance (Deploy Immediately)
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**26B-Standard MoE**:
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- Path: `/Users/accusys/MarkBaseEngine/models/gemma-4-26b-standard`
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- Performance: 21.9ms, 45.7 tok/s
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- Quality: Zero NaN, correct scales
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- Use: **Primary TEXT inference**
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### ✅ Tier 2: Good Performance (Deploy as Alternative)
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**E2B Per-layer**:
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- Path: `/Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit`
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- Performance: 22.1ms, 45.3 tok/s
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- Quality: Zero NaN, correct scales
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- Use: **Alternative TEXT inference (per-layer feature)**
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**31B Dense**:
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- Path: `/Users/accusys/MarkBaseEngine/models/gemma-4-31b-it-4bit`
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- Performance: 23.8ms, 42.1 tok/s
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- Quality: Zero NaN, wrong scales tolerated
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- Use: **Large model TEXT inference**
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### ❌ Tier 3: Do Not Deploy
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**26B-A4B MoE**:
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- Path: `/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit`
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- Status: Corrupted weights (98% tokens NaN)
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- Replace with: **26B-Standard** (same architecture)
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---
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## Why MLX-vlm 0.4.3 Failed for MoE
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### Root Cause
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- **Affine quantization bug**: Generates scales 100x too small
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- **Negative scales**: Invalid for quantization
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- **MoE router**: Amplifies errors → NaN in softmax
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### Why Dense Models Survived
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- **Dense attention**: More stable softmax
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- **No router**: No expert selection error amplification
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- **More layers**: Errors smoothed across 60 layers
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---
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## Production Guidelines
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### 1. Model Selection
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- **MoE inference**: Use 26B-Standard (NOT 26B-A4B)
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- **Dense inference**: Use E2B or 31B
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- **Per-layer feature**: Use E2B
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### 2. Quality Check
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- **Scales validation**: Expect ~100-200 range
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- **Negative check**: Scales must be positive
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- **NaN test**: Run tokenId=0-10 before deployment
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### 3. Performance Target
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- **Latency**: <100ms/token (all models exceed by 4x)
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- **Throughput**: >10 tok/s (all models exceed by 4-5x)
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- **Stability**: Zero NaN (26B-Standard, E2B, 31B)
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---
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## Quantization Lessons
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### 1. MoE Requires Careful Quantization
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- Router network sensitive to errors
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- Scales must be correct magnitude (~100-200)
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- Negative scales cause NaN in router softmax
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### 2. Dense More Robust
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- Standard attention stable
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- Tolerates small/negative scales
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- More layers = error smoothing
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### 3. Validation Essential
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- Check scales before deployment
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- Test multiple tokenIds (0-50)
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- Compare with known-good model (26B-Standard)
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---
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## Future Actions
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### Immediate (Production)
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1. Deploy 26B-Standard for MoE inference
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2. Deploy E2B for Dense inference
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3. Deploy 31B as large model option
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4. Remove 26B-A4B from deployment list
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### Medium-term (Quality)
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1. Add scales validation in weight loading
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2. Auto-detect MLX-vlm quantization issues
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3. Report bug to mlx-vlm GitHub
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4. Provide correct quantization script
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### Long-term (Optimization)
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1. Re-quantize 26B-A4B with fixed script
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2. Benchmark all models with real prompts
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3. Optimize kernel performance
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4. Add batched inference support
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---
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## Summary Table
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### Production Status
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| Model | Deploy? | Reason | Alternative |
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|-------|---------|--------|-------------|
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| 26B-Standard | ✅ YES | Best performance, zero NaN | Primary choice |
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| E2B | ✅ YES | Good performance, per-layer | Alternative |
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| 31B | ✅ YES | Large model, stable | Option |
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| 26B-A4B | ❌ NO | Corrupted weights | Use 26B-Standard |
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### Performance Summary
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- **All usable models**: <25ms/token, >40 tok/s
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- **Target exceeded**: 4-5x better than <100ms goal
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- **Quality**: Zero NaN for all deployed models
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---
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## Final Recommendation
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**Deploy 26B-Standard, E2B, and 31B**
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- All production-grade performance
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- All zero NaN (numerically stable)
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- All exceed performance targets by 4-5x
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**Avoid 26B-A4B**
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- MLX-vlm 0.4.3 quantization bug
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- MoE router + wrong scales = NaN
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- Use 26B-Standard instead (same architecture)
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---
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**End of Final Comparison**
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