Evaluation

DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026)

Evidence concern: excluded from comparisons

Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4

Published inference benchmark; transcribed, not reproduced.

Evaluation results

1 evaluation · 12 results. Different protocols are not a single leaderboard.

Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) · Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)

Sorted by Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 1024 tokens) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 1024 tokens)	Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 2048 tokens)	Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 256 tokens)	Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 4096 tokens)	Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 512 tokens)	Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 8192 tokens)	Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 1024 tokens)	Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 2048 tokens)	Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 256 tokens)	Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 4096 tokens)	Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 512 tokens)	Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 8192 tokens)
DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)Evaluation	53.4	40.7	68.6	27.6	62.6	15.5	376	90	3802	15	1433	5

All 12 result rows with coverage, uncertainty and sources

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	376 Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 1024 tokens) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 1024, column 'bs'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	53.4 Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 1024 tokens) sample-per-second · higher Uncertainty: Not reported by the source: Mean of 10 timing runs; spread not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 1024, column 'tokens/s'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	90 Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 2048 tokens) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 2048, column 'bs'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	40.7 Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 2048 tokens) sample-per-second · higher Uncertainty: Not reported by the source: Mean of 10 timing runs; spread not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 2048, column 'tokens/s'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	3800 Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 256 tokens) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 256, column 'bs'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	68.6 Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 256 tokens) sample-per-second · higher Uncertainty: Not reported by the source: Mean of 10 timing runs; spread not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 256, column 'tokens/s'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	15 Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 4096 tokens) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 4096, column 'bs'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	27.6 Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 4096 tokens) sample-per-second · higher Uncertainty: Not reported by the source: Mean of 10 timing runs; spread not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 4096, column 'tokens/s'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	1430 Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 512 tokens) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 512, column 'bs'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	62.6 Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 512 tokens) sample-per-second · higher Uncertainty: Not reported by the source: Mean of 10 timing runs; spread not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 512, column 'tokens/s'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	5 Maximum batch size (Encoder forward pass on one A100 80 GB; sequence length 8192 tokens) count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 8192, column 'bs'
Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)	Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2) Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)	15.5 Inference throughput (Encoder forward pass on one A100 80 GB; sequence length 8192 tokens) sample-per-second · higher Uncertainty: Not reported by the source: Mean of 10 timing runs; spread not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix B Table 2 and Figure 1 (axis 'Inference speed (tokens/s)') give throughput in tokens/s, but the printed values are inconsistent with the printed batch sizes: ModernGENA base (FA2 + autocast bf16) at L = 256 prints 552.44 tokens/s with a batch of 3,631 sequences (929,536 tokens), about 28 minutes per batch, and ModernGENA base without FA2 at L = 8192 prints 71.15 tokens/s with a batch of 92 (753,664 tokens), about 2.9 hours per batch, against a stated pretraining throughput of 1.33M tokens/s on eight A100 GPUs (Section 3.2.4). The unit or scale of the throughput column is unclear. Batch sizes share the table. Values are recorded as printed.Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9); Figure 1 (page 4); Section 3.2.4 Methods, coverage and source DNABERT-2 (117M) inference throughput on one A100 (Aspidova et al. 2026) model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Aggregation: Not reported Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Appendix B Table 2 (page 9), row 'DNABERT-2 (117M)', sequence length L = 8192, column 'tokens/s'

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Evaluation procedure

DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2)

Configuration: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)
Protocol: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2)
Dataset: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)

origin: Independent external evaluation
configuration: Primary source as retrieved 2026-10-11
protocol id: model-execution-20261011-protocol-aspidova2026-a100-inference-throughput
dataset version: Not reported
split: Fixed-length inputs, L = 256 to 8192 tokens
population: Fixed-length token sequences
inputs: Token sequences
adaptation: None (pretrained encoder, forward pass only)
metric implementation: Maximum batch size by exponential growth and binary search; mean of 10 timing runs
aggregation: Mean of 10 timing runs per model and length
budget: One NVIDIA A100 80 GB GPU

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split: Fixed-length inputs, L = 256 to 8192 tokens
Adaptation: None (pretrained encoder, forward pass only)
Scoring implementation: Maximum batch size by exponential growth and binary search; mean of 10 timing runs

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Evidence table

Inspect claims, sources and review details

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18 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Comparison: adaptation None (pretrained encoder, forward pass only) Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: aggregation Mean of 10 timing runs per model and length Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: budget One NVIDIA A100 80 GB GPU Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: dataset version Not reported Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	missing or unspecified No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: inputs Token sequences Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: metric implementation Maximum batch size by exponential growth and binary search; mean of 10 timing runs Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.metric_implementation` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: population Fixed-length token sequences Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.population` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: protocol id model-execution-20261011-protocol-aspidova2026-a100-inference-throughput Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.protocol_id` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Comparison: split Fixed-length inputs, L = 256 to 8192 tokens Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.split` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact
Origin independent_paper Context-only references	Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models Original source ↗ Appendix B Table 2 row 'DNABERT-2 (117M)' Version: bioRxiv preprint version 1, posted 2026-04-23 Retrieved: 2026-10-11T21:39:38Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.origin` Source artifact SHA-256: `238ab9916d54b419bf31fd79ecf146d654eb6574d3b441aa2043d39632e709fa` Hash scope: bioRxiv stamps each PDF download, so artifact_sha256 describes one retrieval; retrieved_at is the file's modification time. The pdftotext -layout text layer has SHA-256 ebaf0541c173359940737efde2c15d4ab4aa37d7cc323b3922a818feb5be6b4b. A second download at 21:43Z was refused with HTTP 429. Inspected artifact

How to read the evidence

Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release history

Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models · Original source · bioRxiv preprint version 1, posted 2026-04-23

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Technical metadata and extraction receipts

Stable ID: model-execution-20261011-eval-aspidova2026-dnabert-2

areas: dna-genomes
contexts: research
origin: independent_paper
protocol: model-execution-20261011-protocol-aspidova2026-a100-inference-throughput
version: Primary source as retrieved 2026-10-11
comparison: protocol id: model-execution-20261011-protocol-aspidova2026-a100-inference-throughput; dataset version: Not reported; split: Fixed-length inputs, L = 256 to 8192 tokens; population: Fixed-length token sequences; inputs: Token sequences; adaptation: None (pretrained encoder, forward pass only); metric implementation: Maximum batch size by exponential growth and binary search; mean of 10 timing runs; aggregation: Mean of 10 timing runs per model and length; budget: One NVIDIA A100 80 GB GPU
source locator: Appendix B Table 2 row 'DNABERT-2 (117M)'
missing metadata: comparison.dataset version: reason: unreported

Related records

system: DNABERT-2 on one A100 80 GB (Aspidova et al. 2026)
assessment: DNA language model inference throughput and maximum batch size on one A100 80 GB by sequence length (Aspidova et al. 2026 Table 2)
data: Fixed-length token sequences of 256 to 8,192 tokens (Aspidova et al. 2026)
evaluation: aspidova2026 DNABERT-2 (117M) L=1024 bs
evaluation: aspidova2026 DNABERT-2 (117M) L=1024 tokens/s
evaluation: aspidova2026 DNABERT-2 (117M) L=2048 bs
evaluation: aspidova2026 DNABERT-2 (117M) L=2048 tokens/s
evaluation: aspidova2026 DNABERT-2 (117M) L=256 bs
evaluation: aspidova2026 DNABERT-2 (117M) L=256 tokens/s
evaluation: aspidova2026 DNABERT-2 (117M) L=4096 bs
evaluation: aspidova2026 DNABERT-2 (117M) L=4096 tokens/s
evaluation: aspidova2026 DNABERT-2 (117M) L=512 bs
evaluation: aspidova2026 DNABERT-2 (117M) L=512 tokens/s
evaluation: aspidova2026 DNABERT-2 (117M) L=8192 bs
evaluation: aspidova2026 DNABERT-2 (117M) L=8192 tokens/s

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