# PERF-013: M12 FP16 KV Cache for 65536+ Context

**Status:** RESOLVED
**Priority:** Medium
**Spec Reference:** docs/specifications/performance-parity-ollama-llamacpp-gpu-inference-llms.md
**Parent:** PERF-012 (M11 Complete)

## M12 Resolution Summary

**Key Achievement**: FP16 KV Cache enables 65536 context with half the memory!

The StreamingKVCacheFp16 implementation successfully supports 65536 positions. The M12 benchmark validates:

1. **65536 positions filled** successfully
2. **Fill rate** > 30 positions/sec (achieved)
3. **Memory** < 36 GB for KV cache (achieved ~34.4 GB - same as M11 FP32 32768!)
4. **GPU-015 Benchmark**: PASS

## Objective

Implement FP16 KV Cache to enable 65536+ context positions while keeping memory under 48GB VRAM, making ultra-long context feasible on high-end consumer GPUs (A100 40GB, 2x RTX 4090).

| Milestone | Target | Current | Status |
|-----------|--------|---------|--------|
| M12: FP16 65K Context | 65536 positions | **65536 positions** | ✅ COMPLETE |

## Current State

- M1-M11: All complete
- Current Context: 32768 positions (GPU-014 benchmark)
- Current KV Cache: FP32 (~34.4 GB for 32768 ctx)
- Memory scaling: Linear with context length

## M12 Requirements

### Memory Analysis

For 65536 context with 7B model config (FP32):
- 32 layers x 65536 positions x 32 heads x 128 dim x 2 (K+V) x 4 bytes
- = **68.72 GB** KV cache (exceeds all consumer GPUs)

For 65536 context with FP16:
- 32 layers x 65536 positions x 32 heads x 128 dim x 2 (K+V) x 2 bytes
- = **34.36 GB** KV cache (fits on A100 40GB)

### Target Performance

| Metric | M11 (Current) | M12 Target |
|--------|---------------|-----------|
| Context Length | 32768 | **65536** |
| KV Cache Memory | ~34.4 GB (FP32) | < 36 GB (FP16) |
| Token Gen | ~80 tok/s | > 5 tok/s |
| Total VRAM (7B) | ~40 GB | < 48 GB |

### Success Criteria

1. **Context length 65536+ supported** without OOM
2. **FP16 KV Cache** implemented with proper conversion
3. **GPU-015 benchmark passes** with 65536 positions
4. **Memory < 36 GB** for KV cache

## Implementation Plan

### Phase 1: FP16 Conversion Utilities

Add f32 <-> f16 conversion using half crate:
```rust
use half::f16;

fn f32_to_f16(values: &[f32]) -> Vec<u16> {
    values.iter().map(|&v| f16::from_f32(v).to_bits()).collect()
}

fn f16_to_f32(values: &[u16]) -> Vec<f32> {
    values.iter().map(|&v| f16::from_bits(v).to_f32()).collect()
}
```

### Phase 2: StreamingKVCacheFp16

Create new FP16 variant of StreamingKVCache:
```rust
pub struct StreamingKVCacheFp16 {
    num_layers: usize,
    max_positions: usize,
    num_heads: usize,
    head_dim: usize,
    keys: Vec<Vec<u16>>,   // FP16 stored as u16
    values: Vec<Vec<u16>>, // FP16 stored as u16
    position: usize,
}
```

### Phase 3: GPU-015 Benchmark

Add ultra-mega-long context benchmark:
```rust
/// GPU-015: Ultra-Mega-Long Context (M12 target: 65536 positions, FP16)
fn bench_ultra_mega_long_context_fp16() -> BenchResult {
    let num_layers = 32;
    let max_positions = 65536; // M12 target
    // Use FP16 KV cache
    // Target: < 36 GB memory, > 5 tok/s
}
```

## Test Plan (EXTREME TDD)

### Unit Tests

1. `test_f32_f16_conversion_roundtrip` - Conversion preserves values
2. `test_streaming_kv_cache_fp16_65536_positions` - Cache handles 65536 positions
3. `test_fp16_kv_cache_memory_half` - Memory is ~half of FP32

### Benchmark Tests

1. GPU-015: Ultra-Mega-Long Context benchmark (65536 positions, FP16)

## Memory Budget (65536 Context, FP16)

| Component | Size | Notes |
|-----------|------|-------|
| Model Weights | 4.0 GB | 7B Q4_K |
| KV Cache (FP16) | 34.4 GB | 65536 ctx, 32 layers |
| Activations | 2.0 GB | Peak during forward |
| Buffer Pool | 0.5 GB | Reusable GPU buffers |
| **Total** | **40.9 GB** | Fits A100 40GB |

## Commands

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

# Run specific FP16 KV cache tests
cargo test --lib test_fp16_kv_cache --features gpu
```

## Related

- PERF-012: M11 Mega-Long Context (Complete - 32768 positions)
- StreamingKVCache implementation in src/gpu.rs
