Hardware guide

The consensus function is defined on integers in {±1..±16}, with every accumulation below 2^24. Any device whose GEMM has an exact int32 or a true FP32 accumulator gets the canonical bits. The miner chooses the storage format per device. A wrong path cannot split consensus, because nodes verify on the CPU. It can only waste the miner's work, and zpu-cli detects that (every winner plus random spot checks are re-verified on the CPU).

Which path per GPU

GPU	arch	recommended path	alternative	FP8 encoding	notes
H100 / H200	sm_90	`--gemm blas-int8` (cuBLAS int8 -> int32, exact)	`lt-fp8`	OCP E4M3	Hopper FP8 MMA accumulation precision is the known hazard; run `selftest` + `stress` before trusting `lt-fp8`
B200	sm_100	`blas-int8`	`lt-fp8`	OCP E4M3	measure both
B300 (Blackwell Ultra)	sm_103	`lt-fp8`	`blas-int8` (slow)	OCP E4M3	dense INT8 is about 0.15 to 0.19 POPS vs 4.5 on B200 (traded for NVFP4), so FP8 is the only fast path and its exactness under `stress` decides whether B300 can mine efficiently
RTX 4090 / 5090, RTX PRO 6000	sm_89 / sm_120	`blas-int8`	`lt-fp8`	OCP E4M3	consumer FP8 MMA with FP16 accumulate is inexact (26% of rows diverge in our model); use FP32-output paths only
MI300X	gfx942	`blas-int8` (hipBLAS)	`lt-fp8` (hipBLASLt)	E4M3FNUZ (bias 8, no -0)	the miner selects FNUZ from `gcnArchName`
MI355X	gfx950	`blas-int8`	`lt-fp8`	OCP E4M3
Apple M-series	Metal	`zpu_miner.py --backend mlx`	`--backend torch --device mps`	fp32 GEMM	measured bit-exact
any CPU		`zpu-cli --backend cpu` or `zpu-miner --backend cpu`	NumPy fp32 BLAS		reference

Measured (2026-11-09)

Rerun 2 (bench/results/gpu_rerun_2026-11-09_r2/, ephemeral cloud sandboxes, ledger bench/results/gpu_ledger.json). Every GEMM path passed the 278-check selftest and the 2^20 accumulator stress; every benchmark re-checked sample digests against the CPU reference.

GPU	path	attempts/s	TMAC/s	launch overhead	board power	exact
H100 80GB HBM3	`blas-int8`	8,078,510	417	0.11 s	~690 W	PASS
H100	`lt-fp8` (E4M3)	7,379,190	381	0.43 s	~690 W	PASS
MI355X	`blas-int8`	13,124,061	678	0.13 s	~1,230 W	PASS
MI355X	`lt-fp8` (E4M3)	13,102,900	677	1.25 s	~1,200 W	PASS

Real mining on the production v0.1 profile (bench/gpu/box_mine.sh): GPU miner -> zpu-cli -> CPU-only node, 120 s, then a second CPU-only node syncs and re-validates the chain from scratch.

GPU	effective mining rate	blocks accepted	rejected	independent re-sync
H100	7.56 M attempts/s	42	0	same tip (height 42)
MI355X	12.39 M attempts/s	57	0	same tip (height 57)

Rerun 1 found that zpu-cli handed GPU miners 2,000-attempt jobs, capping real mining at 29k attempts/s; adaptive job sizing (--job-secs, default 0.5 s) fixed it. The earlier H100/MI355X measurements in bench/results/gpu_summary.json agree within 1%.

Not measured on hardware (no device reachable): B200/B300 (sm_100), RTX 50 / RTX PRO 6000 (sm_120), MI300X (gfx942, E4M3FNUZ). Device code for all of them compiles in make check (gpu-device-compile).

ROCm from pip (_rocm_sdk_devel, e.g. the rocm/vllm images) keeps rocBLAS/hipBLASLt code objects in _rocm_sdk_libraries/lib/{rocblas,hipblaslt}/library. Point ROCBLAS_TENSILE_LIBPATH / HIPBLASLT_TENSILE_LIBPATH there (bench/gpu/box_run.sh does it automatically), or hipBLAS/hipBLASLt fail with "Could not initialize Tensile host" / "hipModuleLoad failed".

Build

make -C miner/cuda cpu                                   # any OS
make -C miner/cuda cuda CUDA_ARCH="90 100 120"           # needs CUDA 12.8+ (sm_120)
make -C miner/cuda hip  HIP_ARCH="gfx942 gfx950"         # needs ROCm 6.4+ with hipBLAS and hipBLASLt

Bring-up checklist on a new GPU (in order)

./build/zpu-miner-<vendor> selftest tests/vectors/fp8c_v0.1.json --backend gpu --gemm blas-int8: all 278 checks must PASS.
Repeat with --gemm lt-fp8 and --gemm naive. Record FAIL as a result, not as an error to hide.
python miner/python/zpu_miner.py stress --backend torch --device cuda --gemm int8|fp8|fp32 must show 0 mismatches. It forces |sum| = 2^20 rows that a random test would miss.
bench/run_benchmarks.sh records the throughput rows into bench/results/miners.jsonl.
Mine on the testnet: zpu-cli mine --backend exec --exec "./build/zpu-miner-cuda --backend gpu --gemm blas-int8".

Sources for the B300 INT8 figure (third-party spec compilations; confirm against NVIDIA's datasheet before relying on exact values): Tom's Hardware, DataCrunch, flopper.io, Glenn Lockwood.