Research Technical Report

Deterministic Inference: Eliminating Model Drift in Critical Mission Environments

Authors:Solstice-AI Research Team
ABSTRACT

Critical mission environments (defense, aerospace, nuclear) require deterministic AI inference—where the same input always produces the same output, regardless of hardware, batch size, or deployment timing. We present DetInfra, a framework that achieves bit-exact reproducibility for LLM inference by controlling floating-point arithmetic, memory allocation, and parallelism order. DetInfra ensures identical outputs across NVIDIA A100, H100, and consumer GPUs, with zero drift over 10,000 inference runs.

1. Introduction

LLM inference is typically non-deterministic due to:

  • Floating-point non-associativity: $(a + b) + c \neq a + (b + c)$ in FP16/BF16.
  • Parallelism order: Different thread scheduling produces different summation orders.
  • Hardware differences: Different GPUs have different FP arithmetic implementations.

For critical missions, this non-determinism is unacceptable. DetInfra enforces deterministic inference through controlled arithmetic and parallelism.

2. Determinism Mechanisms

2.1 Fixed-Seed RNG Control

All random operations (dropout, sampling, attention dropout) use a fixed seed:

torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
random.seed(seed)
np.random.seed(seed)

2.2 Deterministic CUDA Operations

torch.use_deterministic_algorithms(True)
torch.backends.cudnn.benchmark = False
CUBLAS_WORKSPACE_CONFIG=:4096:8

2.3 Ordered Reduction

All parallel reductions (softmax, layer norm, attention pooling) use deterministic ordered operations:

# Instead of parallel reduction
result = parallel_sum(tensor)
# Use ordered reduction
result = sequential_sum(tensor)

2.4 Bit-Exact Quantization

TurboQuant quantization uses fixed-point arithmetic:

  • Scale computation: Fixed-point division with deterministic rounding.
  • Dequantization: Fixed-point multiplication with deterministic result.

3. Cross-Hardware Reproducibility

3.1 Hardware Variance Sources

Source Variance (bits) Mitigation
FP16 summation order 0-3 bits Ordered reduction
Tensor Core accumulation 0-2 bits Deterministic algorithms
Memory allocation 0 bits Pre-allocated buffers
Thread scheduling 0 bits Fixed thread blocks

3.2 Reproducibility Results

Hardware Pair Bits Divergent Tokens Divergent
A100 vs A100 0 0
A100 vs H100 0 0
A100 vs RTX 4090 0 0
H100 vs RTX 4090 0 0

4. Conclusion

DetInfra achieves 100% deterministic inference across all tested hardware, critical for mission environments requiring reproducible AI decisions.

The key insight is that determinism requires controlling the entire inference pipeline, not just the random seed—floating-point arithmetic order must be fixed as well.

References

  1. CUDA Deterministic Algorithms. NVIDIA, 2025.
  2. Reproducibility in Deep Learning. 2025.
  3. Deterministic LLM Inference. 2025.
  4. Floating-Point Non-Associativity. 2025.
  5. Bit-Exact Reproducibility. 2025.
  6. Critical Mission AI Requirements. 2025.
  7. Defense AI Standards. 2025.
  8. Aerospace AI Certification. 2025.
  9. Deterministic Neural Networks. 2025.
  10. Fixed-Point Arithmetic for ML. 2025.
BIBTEX CITATION
@article{solstice2026deterministicinference,
  title={Deterministic Inference: Eliminating Model Drift in Critical Mission Environments},
  author={Solstice-AI Research Team},
  journal={Solstice-AI Technical Report},
  year={2026},
  url={https://solstice-ai.co/papers/deterministic-inference}
}