Devlog 2

Built and validated a real native Vulkan RAYPAGE path on the RTX 5050. RT cores perform BVH traversal and page candidate pruning entirely on-device, with no CPU routing, readback, per-token BVH rebuild, or host synchronization.

What passed:

• Native RT integration and live router execution.
• BVH rejection: ~99.57%.
• Full-model deterministic chat execution.
• Fidelity: final-logit cosine ~0.9964 and transformed hidden cosine >0.998.
• 14B Qwen2.5-Q2_K model loads, chats, stays deterministic, and fits within 8 GB VRAM (~5.64 GB peak).

What failed:

• End-to-end speed: native RT Qwen3 reached ~88 tok/s versus dense CUDA ~362 tok/s.
• Software sparse was also slower (~115 tok/s), proving routing was not the main issue.
• Precomputed perfect page IDs still achieved only ~106 tok/s, eliminating RT traversal as the bottleneck.
• Indirect quantized MUL_MAT_ID was ~5.15 ms versus ~0.75 ms dense: ~6.9× slower.
• Useful-page recall was ~98.69%, below the 99.5% gate.
• 14B page-major execution reached only ~0.5× dense.
• Fine-grained arbitrary pages destroy contiguous reads, tensor-core tiling, warp coherence, and fused-kernel efficiency.

New work:

• Implemented quantization-safe coarse 128-channel superblocks.
• Built a 6-block trunk + 18-page Q8_0 model with contiguous page-major storage.
• Added prefix slicing so active bundle counts can use ordinary contiguous MUL_MAT kernels without rebuilding artifacts.
• Pulled CUTLASS and confirmed explicit SM120 NVFP4/BF16 GEMM examples exist.
• Initial superblock active-1 fast path was ~68 tok/s, so it has not yet solved the problem.

Most promising directions:

1. RT selects 1–2 coarse bundles, then one fused contiguous kernel executes gate/up/SwiGLU/down.
2. Native block-scaled NVFP4/FP4 kernels, avoiding FP4→BF16 materialization.
3. Long-context KV-page pruning, where avoided attention work is much larger than FFN routing overhead.
4. Hybrid bundle templates: fewer fixed layouts, but optimized for real decode shapes.

Current conclusion: RT pruning works; arbitrary sparse FFN execution is the failure.

MIRAGE Devlog 1 - First 10 Hours

• Audited Qwen3-Coder-30B-A3B. Direct conversion was rejected: its experts need about 54 GiB in BF16, while the artifact limit is 7.2 GiB.

• Built the MIRAGE-7B budget solver and preserved all capture, replay, training, Vulkan, CUDA, and benchmark work.

• Built a real 97M prototype with attention, RoPE, KV cache, shared experts, hard routing, and text generation.

• Training, checkpointing, routing, and tiny-set memorization worked. File/repository splits and exact/near-duplicate checks were added, but generalization is unproven.

• The 15,000-step run stayed finite and passed checkpoint round-trip checks, but quality remained experimental.

• Vectorized bundles were 13.82x faster in isolation. Native Vulkan ray tracing worked, but was 5.17x slower than software and rejected no candidates.- Added frozen search schemas, task/evidence graphs, replay, and untrusted-page handling. Started audited data builders and Qwen3-0.6B upcycling; learning and live web remain unfinished.