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

BATCH_PROCESSING_ANALYSIS.md

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+# Batch Processing Analysis Report
+
+## Current Status
+
+**Test Results** (E4B-MarkBase):
+
+```
+Single token: 29.7 ms/token ✓✓✓
+Batch(2):     270.6 ms/token (9.1x SLOWER!)
+Batch(4):     140.6 ms/token (4.7x SLOWER)
+Batch(8):     76.3 ms/token  (2.6x SLOWER)
+```
+
+**Problem**: Batch processing is **significantly slower** than single token processing.
+
+## Root Cause Analysis
+
+### 1. Sequential Embedding Lookup
+
+**Current implementation** (BatchGenerationTrue.swift:26-52):
+
+```swift
+for i in 0..<batchSize {
+    let embedCmdBuf = engine.commandQueue.makeCommandBuffer()!
+    try dequantizeRowOptimized(...)
+    embedCmdBuf.commit()
+    embedCmdBuf.waitUntilCompleted()  // ← WAIT per token
+    memcpy(...)
+}
+```
+
+**Bottleneck**: batchSize × waitUntilCompleted()
+
+For batch(8): **8 waits** for embedding alone!
+
+### 2. Batch Embedding Kernel Attempt
+
+**Created kernel**: `dequantize_row_batch` (MetalKernels.metal:1988-2019)
+
+**Status**: ❌ CRASH (SIGSEGV - segmentation fault)
+
+**Reason**: Memory access violation, needs debugging
+
+**Deferred**: Using sequential approach for stability
+
+### 3. Layer Processing
+
+**Current**: Uses batch kernels (LayerBatch.swift)
+
+**Status**: ✓✓✓ Working correctly
+
+**Performance**: Unknown ( overshadowed by embedding bottleneck)
+
+## Performance Impact
+
+**Embedding bottleneck dominates**:
+
+```
+Embedding: batchSize × ~5ms = 40ms for batch(8)
+Layer processing: ~25ms
+Total: 65ms+ → 76.3ms/token observed ✓
+```
+
+**Without optimization**: Batch is **slower** than single!
+
+## Optimization Priority
+
+### Phase 1: Fix Batch Embedding Kernel (CRITICAL)
+
+**Goal**: Single GPU dispatch for entire batch
+
+**Current**: 8 waits → Target: 1 wait
+
+**Expected impact**: 
+- Embedding: 40ms → ~5ms (8x faster)
+- Batch(8): 76ms → ~35ms (2x faster)
+- Per-token: 35ms/8 = 4.4ms ✓✓✓
+
+**Status**: ❌ Crash, needs debugging
+
+### Phase 2: Optimize Batch Layer Processing
+
+**Current**: Batch kernels exist but performance unknown
+
+**Goal**: Verify and optimize batch layer kernels
+
+**Expected**: Additional 2-3x speedup
+
+### Phase 3: Model Loading Optimization
+
+**31B loading**: 65 seconds
+
+**Goal**: Parallel weight loading
+
+**Expected**: 50% reduction (32s)
+
+## Lessons Learned
+
+1. **Batch processing ≠ automatic speedup**
+   - Sequential operations in batch code kill performance
+   - Need true parallel GPU dispatch for all phases
+
+2. **Embedding is critical bottleneck**
+   - Small operation but high overhead (multiple waits)
+   - Must be batched for effective performance
+
+3. **Kernel debugging is time-consuming**
+   - SIGSEGV requires careful memory bounds checking
+   - Better to defer and use stable approach first
+
+## Next Steps
+
+**Immediate**: Document findings, move to next optimization
+
+**Short-term**: 
+1. Debug batch embedding kernel (when time permits)
+2. Optimize model loading (higher ROI, easier)
+
+**Long-term**:
+1. Metal kernel fusion
+2. SIMD expansion
+3. Expert caching
+
+## Conclusion
+
+**Batch processing currently SLOWER** due to embedding bottleneck.
+
+**Key insight**: Sequential waits in "batch" code defeat parallelism.
+
+**Recommendation**: Focus on model loading optimization first (higher ROI, easier implementation), then revisit batch embedding kernel debugging.