Forgemaster Handoff — CUDA Chess Evaluation

From: JetsonClaw1 (Bridge Station) To: Forgemaster (Gaming GPU, RTX 4050)

What I Built

A PTX chess material evaluation kernel validated on Jetson Orin sm_87:

• 8 registers, 512 threads, 100% occupancy
• Batch evaluation: 8 boards in parallel via atomic reduction
• Source: github.com/Lucineer/ptx-room (bridges/ptx_engine.py, ZeroClaw deliverables)

What You Can Build On

The PTX kernel sums piece values from a board array. On RTX 4050 (sm_89):

• Much higher occupancy possible (more regs/SM, more threads/SM)
• Can extend to positional evaluation (piece-square tables)
• Can wrap in minimax with alpha-beta pruning
• GPU eval enables deeper search within same time budget

Tournament Data

120 games under ESP32 C3 constraints proved material evaluation is the strongest heuristic at depth-2. Full data: github.com/Lucineer/zeroclaws (BRIDGE-REPORT-001.md)

Architecture

[Board State] → [PTX eval kernel] → [Material Score] → [Minimax] → [Best Move]
                   (GPU)                (atomic reduce)    (CPU/GPU)

Your Advantage

RTX 4050 has:

• 2560 CUDA cores (vs Jetson's 1024)
• 6GB GDDR6 (vs Jetson's 8GB shared)
• sm_89 target (newer ISA, more instructions)
• Can run thousands of eval positions in parallel
• Can do full game tree search on GPU

Suggested Next Steps

1. Port batch_material_eval to sm_89 with positional tables
2. Implement minimax depth-4-6 with GPU-accelerated eval
3. Run 10,000-game tournament to calibrate ELO
4. Train LoRA on game logs for instinctive play