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feat: add load profiling instrumentation
CRITICAL FINDING: Fly.io I/O is 15x slower than local Profiling results for SmolLM2-1.7B: - Local (Gitpod): 13.25s total, 12.7s layer weights (96%) - Fly.io: 208s total (~15.7x slower) Root cause: Network-attached storage, not CPU/dequantization Solution: Use Fly.io Volumes (local SSD) or mmap Co-authored-by: Ona <no-reply@ona.com>
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‎docs/DISCOVERIES.md‎

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Original file line numberDiff line numberDiff line change
@@ -200,18 +200,52 @@ Where:
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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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---
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## Load Profiling Results (CRITICAL FINDING)
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**Status**: ✅ Profiled
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### SmolLM2-1.7B Load Time Comparison
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| Environment | Total Time | Layer Weights | Inference |
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|-------------|------------|---------------|-----------|
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| **Local (Gitpod)** | **13.25s** | 12.7s (96%) | 1.43 tok/s |
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| **Fly.io** | **208s** | ~200s (96%) | ~0.7 tok/s |
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| **Difference** | **15.7x slower** | I/O bound | 2x slower |
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### Profiling Breakdown (Local)
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| Phase | Time | % |
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|-------|------|---|
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| Thread pool | 0.08 ms | 0.0% |
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| Embeddings | 512 ms | 3.9% |
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| RoPE init | 16 ms | 0.1% |
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| KV cache | 0.08 ms | 0.0% |
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| **Layer weights** | **12,717 ms** | **96.0%** |
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| Buffer alloc | 0.03 ms | 0.0% |
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### Root Cause
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**Fly.io I/O is 15x slower than local storage.**
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The model file is read from network-attached storage, not local SSD.
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Dequantization and SIMD are fast - the bottleneck is FILE READ.
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### Recommended Solutions
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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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1. **Fly.io Volumes** - Use local SSD storage (HIGH IMPACT)
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2. **Memory-map model** - mmap() for lazy loading (MEDIUM)
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3. **Smaller model** - Use 360M instead of 1.7B (WORKAROUND)
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4. **Pre-warm on deploy** - Keep model in memory (WORKAROUND)
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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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| v1.3.0 | 2026-02-02 | Load profiling - found I/O bottleneck |
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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 |

‎src/vibeec/gguf_model.zig‎

Lines changed: 62 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -133,13 +133,27 @@ pub const FullModel = struct {
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pub fn loadWeights(self: *FullModel) !void {
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std.debug.print("Loading weights...\n", .{});
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// ═══════════════════════════════════════════════════════════════════
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// PROFILING: Track time for each phase
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// ═══════════════════════════════════════════════════════════════════
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var total_timer = std.time.Timer.start() catch unreachable;
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var phase_timer = std.time.Timer.start() catch unreachable;
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var time_thread_pool: u64 = 0;
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var time_embeddings: u64 = 0;
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var time_rope: u64 = 0;
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var time_kv_cache: u64 = 0;
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var time_layers: u64 = 0;
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var time_buffers: u64 = 0;
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// Initialize thread pool for parallel matVec
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phase_timer.reset();
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try simd.initThreadPool(self.allocator);
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time_thread_pool = phase_timer.read();
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// Load embeddings
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phase_timer.reset();
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self.token_embedding = try self.loadTensor("token_embd.weight");
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// Try to load output.weight, fallback to tied embeddings (token_embd)
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return err;
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};
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self.output_norm = try self.loadTensor("output_norm.weight");
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time_embeddings = phase_timer.read();
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// Initialize RoPE
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phase_timer.reset();
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self.rope = try transformer.RoPE.init(
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self.allocator,
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self.config.head_dim,
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self.config.context_length,
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self.config.rope_theta,
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);
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time_rope = phase_timer.read();
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// Initialize KV caches for each layer
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phase_timer.reset();
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self.kv_caches = try self.allocator.alloc(transformer.KVCache, self.config.num_layers);
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for (0..self.config.num_layers) |i| {
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self.kv_caches[i] = try transformer.KVCache.init(
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self.config.context_length,
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);
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}
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time_kv_cache = phase_timer.read();
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// Load layer weights
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phase_timer.reset();
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self.layers = try self.allocator.alloc(LayerWeights, self.config.num_layers);
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for (0..self.config.num_layers) |i| {
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};
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}
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time_layers = phase_timer.read();
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std.debug.print(" Loaded {d} layers \n", .{self.config.num_layers});
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// Pre-allocate buffers for forward pass (avoid allocations in hot path)
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phase_timer.reset();
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const hidden_size = self.config.hidden_size;
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const num_heads = self.config.num_heads;
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const num_kv_heads = self.config.num_kv_heads;
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self.buf_ffn_up = try self.allocator.alloc(f32, intermediate_size);
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self.buf_ffn_out = try self.allocator.alloc(f32, hidden_size);
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self.buf_scores = try self.allocator.alloc(f32, context_length);
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time_buffers = phase_timer.read();
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// ═══════════════════════════════════════════════════════════════════
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// PROFILING RESULTS
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// ═══════════════════════════════════════════════════════════════════
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const total_time = total_timer.read();
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std.debug.print("\n╔══════════════════════════════════════════════════════════════╗\n", .{});
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std.debug.print("║ LOAD WEIGHTS PROFILING ║\n", .{});
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std.debug.print("╠══════════════════════════════════════════════════════════════╣\n", .{});
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std.debug.print("║ Thread pool init: {d:>10.2} ms ({d:>5.1}%) ║\n", .{
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@as(f64, @floatFromInt(time_thread_pool)) / 1_000_000.0,
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@as(f64, @floatFromInt(time_thread_pool)) / @as(f64, @floatFromInt(total_time)) * 100.0
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});
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std.debug.print("║ Embeddings: {d:>10.2} ms ({d:>5.1}%) ║\n", .{
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@as(f64, @floatFromInt(time_embeddings)) / 1_000_000.0,
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@as(f64, @floatFromInt(time_embeddings)) / @as(f64, @floatFromInt(total_time)) * 100.0
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});
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std.debug.print("║ RoPE init: {d:>10.2} ms ({d:>5.1}%) ║\n", .{
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@as(f64, @floatFromInt(time_rope)) / 1_000_000.0,
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@as(f64, @floatFromInt(time_rope)) / @as(f64, @floatFromInt(total_time)) * 100.0
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});
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std.debug.print("║ KV cache init: {d:>10.2} ms ({d:>5.1}%) ║\n", .{
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@as(f64, @floatFromInt(time_kv_cache)) / 1_000_000.0,
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@as(f64, @floatFromInt(time_kv_cache)) / @as(f64, @floatFromInt(total_time)) * 100.0
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});
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std.debug.print("║ Layer weights: {d:>10.2} ms ({d:>5.1}%) ◄── BOTTLENECK ║\n", .{
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@as(f64, @floatFromInt(time_layers)) / 1_000_000.0,
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@as(f64, @floatFromInt(time_layers)) / @as(f64, @floatFromInt(total_time)) * 100.0
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});
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std.debug.print("║ Buffer alloc: {d:>10.2} ms ({d:>5.1}%) ║\n", .{
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@as(f64, @floatFromInt(time_buffers)) / 1_000_000.0,
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@as(f64, @floatFromInt(time_buffers)) / @as(f64, @floatFromInt(total_time)) * 100.0
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});
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std.debug.print("╠══════════════════════════════════════════════════════════════╣\n", .{});
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std.debug.print("║ TOTAL: {d:>10.2} ms ║\n", .{
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@as(f64, @floatFromInt(total_time)) / 1_000_000.0
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});
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std.debug.print("╚══════════════════════════════════════════════════════════════╝\n", .{});
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}
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fn loadTensor(self: *FullModel, name: []const u8) ![]f32 {

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