NVIDIA RTX A5000

Error budget used

Floating-point answers are never exact, so each check allows a rounding error and proves how large it can honestly be. The red line is that proven limit. A bar shows the largest error this chip produced, as a share of the limit, across every run. Anything past the line would be a wrong answer.

Checked exactly, where any difference at all fails: INT8 matrix multiply, INT8 data patterns. Every one matched.

Bars close to the line at FP16 and BF16 are expected: there the limit is dominated by the single rounding of the output, so a correct chip has almost nothing to spare. A bar past the line is a failed run.

Every planted fault was caught

A clean result is only worth something if the tester would have seen a fault. So next to every clean run, the self-test plants faults on purpose, including the patterns NVIDIA measured at gate level, and every one must be caught. How the self-test works.

Compute probe

Matrix multiplies at several shapes and precisions. Every output element is checked against an exact or float64 reference under a proven rounding-error bound, and every run is compared bit for bit with the first run.

Tool v0.3.2, PyTorch 2.8.0+cu128, 11 October 2026. Result file · Self-test file

Error counters: ECC off, so the error counters cannot see memory errors.

Transformer kernel probe

Softmax, layer norm, GELU, rotary embeddings and fused attention, each checked against a float64 reference under its own proven bound and against the first run bit for bit.

Tool v0.2.2, PyTorch 2.8.0+cu128, 11 October 2026. Result file · Self-test file

Error counters: ECC off, so the error counters cannot see memory errors.

Checksum probe

A matrix multiply protected by row and column checksums (Huang and Abraham) with proven thresholds, so a wrong element is located to its exact row and column.

Tool v0.1.0, PyTorch 2.8.0+cu128, 11 October 2026. Result file · Self-test file

Error counters: ECC off, so the error counters cannot see memory errors.

Data pattern probe

Inputs built to reach particular bits (wide exponents, long mantissas, cancellation, alternating signs, sparse inputs, subnormals and values near the top of the range), fed through the same matrix multiply checks.

Tool v0.1.2, PyTorch 2.8.0+cu128, 11 October 2026. Result file · Self-test file

Error counters: ECC off, so the error counters cannot see memory errors.

Memory probe

Most of the device memory written with six patterns, left to dwell, and read back word by word.

Tool v0.1.1, 11 October 2026. Result file · Self-test file

Error counters: ECC off, so the error counters cannot see memory errors.

Setup and files

The chip

The software

The test

Result files

• rtxa5000_abft.jsonl result
• rtxa5000_abft_inject.jsonl self-test
• rtxa5000_clean.jsonl result
• rtxa5000_inject.jsonl self-test
• rtxa5000_kernels.jsonl result
• rtxa5000_kernels_inject.jsonl self-test
• rtxa5000_memcheck.jsonl result
• rtxa5000_memcheck_inject.jsonl self-test
• rtxa5000_patterns.jsonl result
• rtxa5000_patterns_inject.jsonl self-test

Run it on your chip

The same tests on your own hardware, in one command. Every result file it writes can be published here as a new row.

pip install ai-chip-integrity
chip-integrity run --name nvidia-rtx-a5000

Cite the tool as: Yasha Khandelwal, ai-chip-integrity, version 0.4.2, 2026, https://doi.org/10.5281/zenodo.23393790

Other chips measured