CARBON

Same seed. Different GPU. Identical weights.

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Carbon makes training bit-exact reproducible across different GPU architectures.

Train on an RTX 4090. Train on an RTX 5090. Same SHA-256 hash on every weight tensor. Same optimizer state. Same loss. Different silicon, identical bits.

Built by Tushar Sharma at ALIA Labs.

Install

pip install alia-carbon

The Problem

RTX 4090:  hash = 6a6e2bc1...  loss = 0.069844
RTX 5090:  hash = 46681ef8...  loss = 0.069844

Same loss. Different weights. cuBLAS picked different internal algorithms on different silicon. Standard PyTorch cannot reproduce this training run on different hardware.

The Fix

import carbon
carbon.enable(seed=42)

RTX 4090:  hash = 62118e9c...  loss = 0.070026
RTX 5090:  hash = 62118e9c...  loss = 0.070026

Identical. 5 seeds tested, up to 500 steps, every configuration matches.

How It Works

Every non-deterministic op replaced with a deterministic one:

Op	Standard	Carbon
MatMul	cuBLAS (arch-dependent)	Tiled fp64 + Kahan accumulation
LayerNorm	Parallel reduction (thread-dependent)	fp64 mean/variance
AllReduce	NCCL (arrival-order-dependent)	AllGather + rank-order reduce
Scatter	Atomic race conditions	Sorted index ops

The mechanism: split every matmul into tiles, upcast to float64, accumulate with Kahan-Babushka-Neumaier compensation in a fixed order. The float64 computation eliminates architecture-dependent rounding. The fixed order eliminates parallelism-dependent summation differences.

The Proof

Toy Model (500K params, 500 steps, 5 seeds)

Seed	Steps	4090 Hash	5090 Hash	Match
42	500	d6830c89...	d6830c89...	yes
123	500	44843c64...	44843c64...	yes
7	500	7bf2c902...	7bf2c902...	yes
999	500	8cc60024...	8cc60024...	yes
2024	500	1e420d04...	1e420d04...	yes

5 out of 5 configurations. Every hash matches.

GPT-2 124M Fine-Tune (60M trainable, 20 steps)

Run	Hash	Loss
Standard PyTorch (5090)	85b72d9f...	7.1904
Carbon run 1 (5090)	995d4c9b...	7.1904
Carbon run 2 (5090)	995d4c9b...	7.1904
Carbon cross-GPU (4090)	995d4c9b...	7.1904

Three Carbon runs, two GPUs, one hash. Standard PyTorch produces a different hash.

Overhead

Scale	Overhead
500K toy model	1.07x
GPT-2 124M (60M trainable)	10.1x

The cost of bit-exact determinism. fp64 Kahan-compensated matmul is slower than cuBLAS. For alignment research and debugging where you need exact reproducibility, it's worth it.

Important: What Carbon Requires

Cross-architecture determinism requires replacing nn.Linear with DeterministicLinear and nn.LayerNorm with CarbonLayerNorm. The carbon.enable() call patches torch.matmul globally, but standard PyTorch modules use internal C++ paths that bypass the patch.

This is not a one-line fix for existing training code. It's a mechanism that works when you build with Carbon's layers.

Tested On

RTX 4090 (Ada) | RTX 5090 (Blackwell) | H100 SXM | A100 SXM

Consumer GPUs match each other. Datacenter GPUs match each other. Cross-tier (consumer vs datacenter) produces different hashes. Documented, not hidden.

Citation

@article{sharma2026carbon,
  title={Carbon: Bit-Exact Deterministic Training Across Consumer GPU Architectures},
  author={Sharma, Tushar},
  year={2026},
  url={https://github.com/ALIA-Engineering/Carbon}
}

Roadmap

v0.1 - Proof of concept. KBN summation, tiled fp64 matmul, Python-level tiling.

v0.2 (current) - WebGPU backend. The same bit-exact algorithms (Kahan-compensated tiled matmul, fixed-order reduction, compensated summation) as WGSL compute shaders, no CUDA required. Works on any WebGPU-capable GPU (NVIDIA, AMD, Intel, Apple Silicon). Install with pip install alia-carbon[wgpu].

v0.3 - Performance. Replace Kahan accumulation with superaccumulators for order-independent exact summation. This eliminates the need for fixed tile order and could close the consumer-vs-datacenter hash gap. CUDA-native tiled matmul to cut the 10x overhead to under 2x.

v0.4 - Scale. Mixed precision (bf16 forward, fp32 master weights). Multi-GPU deterministic allreduce. FSDP/DDP integration. Target: deterministic fine-tuning of 7B+ models.

License

Apache-2.0 | ALIA Labs