# PERF-011: M10 Super-Long Context (16384+ Positions)

**Status:** RESOLVED
**Priority:** Medium
**Spec Reference:** docs/specifications/performance-parity-ollama-llamacpp-gpu-inference-llms.md
**Parent:** PERF-010 (M9 Complete)

## Objective

Extend context length support to 16384+ positions for super-long context generation, enabling use cases like document summarization, code analysis, and long-form conversations.

| Milestone | Target | Current | Status |
|-----------|--------|---------|--------|
| M10: Super-Long Context | 16384 positions | **16384 positions** | ✅ COMPLETE |

## M10 Resolution Summary

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

The StreamingKVCache implementation successfully supports 16384 positions. The M10 benchmark validates:

1. **16384 positions filled** successfully
2. **Fill rate** > 125 positions/sec (achieved)
3. **Retrieve rate** > 1250 ops/sec (achieved)
4. **Memory** < 18 GB for KV cache (achieved ~17.2 GB)
5. **GPU-013 Benchmark**: PASS

## Current State

- M1-M9: All complete ✅
- Current Context: 8192 positions (GPU-012 benchmark)
- Memory Efficiency: 6.15 GB for 7B simulation (2048 ctx)
- All 20/20 benchmarks passing, 1777 tests passing

## M10 Requirements

### Target Performance

| Metric | M9 (Current) | M10 Target |
|--------|--------------|-----------|
| Context Length | 8192 | **16384** |
| KV Cache Memory | ~8.6 GB | < 18 GB |
| Token Gen | ~80 tok/s | > 20 tok/s |
| Total VRAM (7B) | ~12.6 GB | < 24 GB |

### Success Criteria

1. **Context length 16384+ supported** without OOM
2. **KV Cache scales efficiently** with quadrupled context from M8
3. **GPU-013 benchmark passes** with 16384 positions
4. **Throughput maintained** at > 20 tok/s

## Implementation Plan

### Phase 1: Validate StreamingKVCache Scaling

The StreamingKVCache already supports arbitrary `max_positions`. We need to:
1. Add benchmark with 16384 positions
2. Verify memory bounds are maintained
3. Test fill and retrieval performance at scale

### Phase 2: Memory Budget Analysis

For 16384 context with 7B model config:
- 32 layers × 16384 positions × 32 heads × 128 dim × 2 (K+V) × 4 bytes
- = 17.18 GB KV cache (vs 8.59 GB for 8192)

Memory optimizations if needed:
1. **Quantized KV Cache** - Store KV in FP16 (halves memory to ~8.6 GB)
2. **Sliding Window Attention** - Only attend to recent N positions
3. **Chunked Processing** - Process super-long contexts in chunks
4. **Key-Value Compression** - Compress older KV entries

### Phase 3: GPU-013 Benchmark

Add super-long context benchmark:
```rust
/// GPU-013: Super-Long Context (M10 target: 16384 positions)
fn bench_super_long_context() -> BenchResult {
    let num_layers = 32;
    let max_positions = 16384; // M10 target
    // Fill cache to capacity
    // Verify memory bounds and performance
    // Target: > 20 tok/s throughput
}
```

## Test Plan (EXTREME TDD)

### Unit Tests

1. `test_streaming_kv_cache_16384_positions` - Cache handles 16384 positions
2. `test_super_long_context_memory_bound` - Memory stays bounded
3. `test_super_long_context_fill_performance` - Fill rate maintained

### Benchmark Tests

1. GPU-013: Super-Long Context benchmark (16384 positions)

## Memory Budget (16384 Context)

| Component | Size | Notes |
|-----------|------|-------|
| Model Weights | 4.0 GB | 7B Q4_K |
| KV Cache | 17.2 GB | 16384 ctx, 32 layers |
| Activations | 1.5 GB | Peak during forward |
| Buffer Pool | 0.5 GB | Reusable GPU buffers |
| **Total** | **23.2 GB** | Needs 24GB+ VRAM |

**Note:** For 16384 context, users will need 24GB+ VRAM (e.g., RTX 4090, A5000, A6000).

## Use Cases

Super-long context enables:
- **Document summarization** - Process entire documents in single context
- **Code analysis** - Analyze large codebases in context
- **Long conversations** - Maintain context over extended dialogues
- **RAG applications** - Include more retrieved chunks in context

## Commands

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

# Run specific super-long context tests
cargo test --lib test_super_long_context --features gpu
```

## Related

- PERF-009: M8 Extended Context (✅ Complete - 4096 positions)
- PERF-010: M9 Ultra-Long Context (✅ Complete - 8192 positions)
- StreamingKVCache implementation in src/gpu.rs
