# PERF-012: M11 Mega-Long Context (32768+ Positions)

**Status:** RESOLVED
**Priority:** Medium
**Spec Reference:** docs/specifications/performance-parity-ollama-llamacpp-gpu-inference-llms.md
**Parent:** PERF-011 (M10 Complete)

## M11 Resolution Summary

**Key Achievement**: Mega-long context support works!

The StreamingKVCache implementation successfully supports 32768 positions. The M11 benchmark validates:

1. **32768 positions filled** successfully
2. **Fill rate** > 60 positions/sec (achieved)
3. **Retrieve rate** > 600 ops/sec (achieved)
4. **Memory** < 36 GB for KV cache (achieved ~34.4 GB)
5. **GPU-014 Benchmark**: PASS

## Objective

Extend context length support to 32768+ positions for mega-long context generation, enabling advanced use cases like entire book summarization, large codebase analysis, and multi-document reasoning.

| Milestone | Target | Current | Status |
|-----------|--------|---------|--------|
| M11: Mega-Long Context | 32768 positions | **32768 positions** | ✅ COMPLETE |

## Current State

- M1-M11: All complete ✅
- Current Context: 32768 positions (GPU-014 benchmark)
- Memory Efficiency: 6.15 GB for 7B simulation (2048 ctx)
- All 22/22 benchmarks passing, 1786 tests passing

## M11 Requirements

### Target Performance

| Metric | M10 (Current) | M11 Target |
|--------|---------------|-----------|
| Context Length | 16384 | **32768** |
| KV Cache Memory | ~17.2 GB | < 36 GB |
| Token Gen | ~81 tok/s | > 10 tok/s |
| Total VRAM (7B) | ~21.2 GB | < 48 GB |

### Success Criteria

1. **Context length 32768+ supported** without OOM
2. **KV Cache scales efficiently** with doubled context from M10
3. **GPU-014 benchmark passes** with 32768 positions
4. **Throughput maintained** at > 10 tok/s

## Implementation Plan

### Phase 1: Validate StreamingKVCache Scaling

The StreamingKVCache already supports arbitrary `max_positions`. We need to:
1. Add benchmark with 32768 positions
2. Verify memory bounds are maintained
3. Test fill and retrieval performance at scale

### Phase 2: Memory Budget Analysis

For 32768 context with 7B model config:
- 32 layers × 32768 positions × 32 heads × 128 dim × 2 (K+V) × 4 bytes
- = 34.36 GB KV cache (vs 17.18 GB for 16384)

Memory optimizations for production:
1. **FP16 KV Cache** - Halves memory to ~17.2 GB
2. **Sliding Window Attention** - Only attend to recent N positions
3. **Sparse Attention** - Attend to subset of positions
4. **KV Cache Quantization** - INT8 reduces to ~8.6 GB

### Phase 3: GPU-014 Benchmark

Add mega-long context benchmark:
```rust
/// GPU-014: Mega-Long Context (M11 target: 32768 positions)
fn bench_mega_long_context() -> BenchResult {
    let num_layers = 32;
    let max_positions = 32768; // M11 target
    // Fill cache to capacity
    // Verify memory bounds and performance
    // Target: > 10 tok/s throughput
}
```

## Test Plan (EXTREME TDD)

### Unit Tests

1. `test_streaming_kv_cache_32768_positions` - Cache handles 32768 positions
2. `test_mega_long_context_memory_bound` - Memory stays bounded
3. `test_mega_long_context_fill_performance` - Fill rate maintained

### Benchmark Tests

1. GPU-014: Mega-Long Context benchmark (32768 positions)

## Memory Budget (32768 Context)

| Component | Size | Notes |
|-----------|------|-------|
| Model Weights | 4.0 GB | 7B Q4_K |
| KV Cache | 34.4 GB | 32768 ctx, 32 layers |
| Activations | 2.0 GB | Peak during forward |
| Buffer Pool | 0.5 GB | Reusable GPU buffers |
| **Total** | **40.9 GB** | Needs 48GB+ VRAM |

**Note:** For 32768 context, users will need 48GB+ VRAM (e.g., A6000, 2xRTX 4090, A100).

## Use Cases

Mega-long context enables:
- **Book summarization** - Process entire books in single context
- **Large codebase analysis** - Analyze 10K+ line codebases
- **Multi-document reasoning** - Compare multiple documents
- **Extended RAG** - Include many retrieved chunks
- **Long-form content generation** - Generate novel-length content

## Commands

```bash
# Run with mega-long context benchmark
cargo run --example performance_parity --features gpu --release

# Run specific mega-long context tests
cargo test --lib test_mega_long_context --features gpu
```

## Related

- PERF-010: M9 Ultra-Long Context (✅ Complete - 8192 positions)
- PERF-011: M10 Super-Long Context (✅ Complete - 16384 positions)
- StreamingKVCache implementation in src/gpu.rs
