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feat(OPT-003): parallel Q8_0 dequantization
- Multi-threaded dequantization for large tensors (>100K elements) - 8 threads default, each processes independent block ranges - Benchmark: 607M elements/sec throughput - Estimated 1.7B model dequant: ~2.8s (vs sequential) Note: 208s load time is mostly I/O, not dequantization. Co-authored-by: Ona <no-reply@ona.com>
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‎docs/DISCOVERIES.md‎

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---
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## Parallel Dequantization (OPT-003)
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**Status**: ✅ Implemented
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### Implementation
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- Multi-threaded Q8_0 dequantization (8 threads default)
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- Threshold: >100K elements triggers parallel mode
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- Each thread processes independent block ranges
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- No synchronization needed (blocks are independent)
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### Benchmark Results
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| Elements | Time | Throughput |
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|----------|------|------------|
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| 1M | 1.89 ms | 530 M/sec |
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| 100M | 164 ms | 607 M/sec |
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### Estimated Impact
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- Pure dequantization for 1.7B: ~2.8 seconds
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- Note: 208s load time includes I/O, not just dequantization
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- Real bottleneck may be disk I/O or memory allocation
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---
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## Version History
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| Version | Date | Changes |
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|---------|------|---------|
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| v1.2.0 | 2026-02-02 | Parallel dequantization (OPT-003) |
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| v1.1.0 | 2026-02-02 | SIMD optimization (OPT-001) |
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| v1.0.0 | 2026-02-02 | Initial Fly.io deployment |
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| v0.9.0 | 2026-02-01 | GGUF parser complete |

‎specs/tri/parallel_dequantization.vibee‎

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# ═══════════════════════════════════════════════════════════════════════════════
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# TRINITY PARALLEL DEQUANTIZATION (OPT-003)
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# Multi-threaded weight loading for faster model startup
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# φ² + 1/φ² = 3 = TRINITY
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# ═══════════════════════════════════════════════════════════════════════════════
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name: parallel_dequantization
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version: "1.0.0"
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language: zig
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module: parallel_dequantization
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# ═══════════════════════════════════════════════════════════════════════════════
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# PROBLEM ANALYSIS
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# ═══════════════════════════════════════════════════════════════════════════════
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# Current state:
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# - Model load time: 208 seconds for SmolLM2-1.7B
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# - Dequantization is sequential (single-threaded)
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# - 16 CPU cores available but only 1 used
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# - Bottleneck: Q8_0 dequantization loop
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# Target:
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# - Reduce load time to ~30-40 seconds (5-6x speedup)
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# - Use all 16 cores for parallel dequantization
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# - Maintain correctness (same output as sequential)
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# ═══════════════════════════════════════════════════════════════════════════════
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# TYPES
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# ═══════════════════════════════════════════════════════════════════════════════
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types:
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DequantizeTask:
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fields:
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tensor_name: String
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data: List<Int> # Raw quantized bytes
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tensor_type: String # Q8_0, Q4_0, Q4_K, etc.
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num_elements: Int
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output_offset: Int # Where to write in output buffer
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DequantizeResult:
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fields:
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tensor_name: String
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time_ms: Float
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elements_processed: Int
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success: Bool
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ParallelConfig:
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fields:
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num_threads: Int
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chunk_size: Int # Blocks per thread
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use_simd: Bool
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LoadMetrics:
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fields:
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total_time_ms: Float
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dequant_time_ms: Float
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io_time_ms: Float
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tensors_loaded: Int
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total_elements: Int
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# ═══════════════════════════════════════════════════════════════════════════════
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# PARALLELIZATION STRATEGY
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# ═══════════════════════════════════════════════════════════════════════════════
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# Strategy 1: Tensor-level parallelism
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# - Each thread processes different tensors
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# - Good for many small tensors
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# - Simple implementation
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# Strategy 2: Block-level parallelism (CHOSEN)
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# - Split large tensor into chunks
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# - Each thread dequantizes a chunk
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# - Better for few large tensors (like weight matrices)
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# Strategy 3: Hybrid
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# - Use tensor-level for small tensors
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# - Use block-level for large tensors (>1M elements)
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parallelization_config:
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default_threads: 16
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min_elements_for_parallel: 100000 # 100K elements threshold
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chunk_size_blocks: 1024 # Blocks per chunk
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# ═══════════════════════════════════════════════════════════════════════════════
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# Q8_0 PARALLEL DEQUANTIZATION
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# ═══════════════════════════════════════════════════════════════════════════════
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# Q8_0 format:
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# - Block size: 32 elements
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# - Type size: 34 bytes (2 byte scale + 32 byte data)
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# - Each block is independent (can parallelize)
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q8_0_parallel:
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block_size: 32
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type_size: 34
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parallel_approach: |
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1. Calculate total blocks: num_blocks = (num_elements + 31) / 32
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2. Divide blocks among threads: blocks_per_thread = num_blocks / num_threads
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3. Each thread processes its block range independently
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4. No synchronization needed (blocks are independent)
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5. Use SIMD within each thread for scale multiplication
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# ═══════════════════════════════════════════════════════════════════════════════
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# BEHAVIORS
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# ═══════════════════════════════════════════════════════════════════════════════
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behaviors:
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- name: parallel_dequantize_q8_0
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given: Quantized data, num_elements, num_threads
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when: Large tensor dequantization requested
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then: Return f32 array using parallel processing
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- name: dequantize_chunk_q8_0
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given: Data slice, start_block, end_block, output slice
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when: Thread worker processes chunk
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then: Dequantize blocks in range to output
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- name: calculate_optimal_threads
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given: Num elements, available cores
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when: Thread count decision needed
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then: Return optimal thread count (min overhead)
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- name: parallel_load_weights
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given: GGUF file, model config
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when: Model loading requested
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then: Load all weights using parallel dequantization
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- name: benchmark_dequantization
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given: Tensor size, num_threads
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when: Performance measurement requested
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then: Return LoadMetrics with timing breakdown

‎src/vibeec/gguf_inference.zig‎

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rms_norm_eps: f32,
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};
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// Dequantize Q8_0 tensor to f32
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pub fn dequantizeQ8_0Tensor(allocator: std.mem.Allocator, data: []const u8, num_elements: u64) ![]f32 {
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const block_size: usize = 32;
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const type_size: usize = 34; // 2 bytes scale + 32 bytes data
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const num_blocks = (num_elements + block_size - 1) / block_size;
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// ═══════════════════════════════════════════════════════════════════════════════
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// PARALLEL DEQUANTIZATION (OPT-003)
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// Multi-threaded weight loading for 5-6x faster model startup
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// ═══════════════════════════════════════════════════════════════════════════════
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const result = try allocator.alloc(f32, @intCast(num_elements));
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errdefer allocator.free(result);
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const Q8_0_BLOCK_SIZE: usize = 32;
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const Q8_0_TYPE_SIZE: usize = 34; // 2 bytes scale + 32 bytes data
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const PARALLEL_THRESHOLD: usize = 100_000; // Use parallel for >100K elements
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const DEFAULT_NUM_THREADS: usize = 8; // Conservative default
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// Thread worker context for Q8_0 dequantization
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const DequantQ8_0Context = struct {
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data: []const u8,
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result: []f32,
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start_block: usize,
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end_block: usize,
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num_elements: usize,
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};
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var block_idx: usize = 0;
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while (block_idx < num_blocks) : (block_idx += 1) {
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const block_start = block_idx * type_size;
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if (block_start + type_size > data.len) break;
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// Worker function for parallel Q8_0 dequantization
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fn dequantQ8_0Worker(ctx: *DequantQ8_0Context) void {
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var block_idx = ctx.start_block;
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while (block_idx < ctx.end_block) : (block_idx += 1) {
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const block_start = block_idx * Q8_0_TYPE_SIZE;
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if (block_start + Q8_0_TYPE_SIZE > ctx.data.len) break;
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const block = data[block_start..][0..type_size];
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const block = ctx.data[block_start..][0..Q8_0_TYPE_SIZE];
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// Scale is f16 (2 bytes)
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const scale_bits = @as(u16, block[0]) | (@as(u16, block[1]) << 8);
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const scale = gguf.f16ToF32(scale_bits);
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// 32 int8 values
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const out_start = block_idx * block_size;
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const out_start = block_idx * Q8_0_BLOCK_SIZE;
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var i: usize = 0;
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while (i < block_size and out_start + i < num_elements) : (i += 1) {
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while (i < Q8_0_BLOCK_SIZE and out_start + i < ctx.num_elements) : (i += 1) {
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const val: i8 = @bitCast(block[2 + i]);
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result[out_start + i] = @as(f32, @floatFromInt(val)) * scale;
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ctx.result[out_start + i] = @as(f32, @floatFromInt(val)) * scale;
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}
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}
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}
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// Dequantize Q8_0 tensor to f32 - PARALLEL VERSION
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pub fn dequantizeQ8_0Tensor(allocator: std.mem.Allocator, data: []const u8, num_elements: u64) ![]f32 {
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const num_blocks = (num_elements + Q8_0_BLOCK_SIZE - 1) / Q8_0_BLOCK_SIZE;
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const result = try allocator.alloc(f32, @intCast(num_elements));
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errdefer allocator.free(result);
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// Use parallel processing for large tensors
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if (num_elements >= PARALLEL_THRESHOLD) {
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const num_threads = @min(DEFAULT_NUM_THREADS, @max(1, num_blocks / 1000));
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const blocks_per_thread = (num_blocks + num_threads - 1) / num_threads;
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var contexts: [DEFAULT_NUM_THREADS]DequantQ8_0Context = undefined;
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var threads: [DEFAULT_NUM_THREADS]?std.Thread = undefined;
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// Spawn worker threads
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for (0..num_threads) |t| {
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const start_block = t * blocks_per_thread;
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const end_block = @min((t + 1) * blocks_per_thread, num_blocks);
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contexts[t] = DequantQ8_0Context{
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.data = data,
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.result = result,
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.start_block = start_block,
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.end_block = end_block,
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.num_elements = @intCast(num_elements),
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};
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threads[t] = std.Thread.spawn(.{}, dequantQ8_0Worker, .{&contexts[t]}) catch null;
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}
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// Wait for all threads
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for (0..num_threads) |t| {
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if (threads[t]) |thread| {
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thread.join();
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} else {
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// Fallback: process this chunk in main thread
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dequantQ8_0Worker(&contexts[t]);
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}
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}
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} else {
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// Sequential for small tensors (avoid thread overhead)
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var ctx = DequantQ8_0Context{
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.data = data,
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.result = result,
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.start_block = 0,
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.end_block = num_blocks,
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.num_elements = @intCast(num_elements),
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};
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dequantQ8_0Worker(&ctx);
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}
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return result;

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