Papers › LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer
Large language models have been widely adopted but require significant GPU memory for inference. We develop a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers, which cut the memory needed for inference by half while retaining full precision performance. With our method, a 175B parameter 16/32-bit checkpoint can be loaded, converted to Int8, and used immediately without performance degradation. This is made possible by understanding and working around properties of highly systematic emergent features in transformer language models that dominate attention and transformer predictive performance. To cope with these features, we develop a two-part quantization procedure, LLM.int8(). We first use vector-wise quantization with separate normalization constants for each inner product in the matrix multiplication, to quantize most of the features. However, for the emergent outliers, we also include a new mixed-precision decomposition scheme, which isolates the outlier feature dimensions into a 16-bit matrix multiplication while still more than 99.9% of values are multiplied in 8-bit. Using LLM.int8(), we show empirically it is possible to perform inference in LLMs with up to 175B parameters without any performance degradation. This result makes such models much more accessible, for example making it possible to use OPT-175B/BLOOM on a single server with consumer GPUs. We open-source our software.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | C4 | Zeropoint LLM.int8 13B (vector-wise + decomp) | Perplexity | 12.45 | #2 of 9 | Archive leaderboard | report |
| Language Modelling | C4 | LLM.float32 6.7B | Perplexity | 13.3 | #5 of 9 | Archive leaderboard | report |
| Language Modelling | C4 | LLM.float32 2.7B | Perplexity | 14.43 | #6 of 9 | Archive leaderboard | report |
| Language Modelling | C4 | LLM.float32 1.3B | Perplexity | 15.91 | #9 of 9 | Archive leaderboard | report |
| Linguistic Acceptability | CoLA | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Accuracy | 68.6% | #17 of 43 | Archive leaderboard | report |
| Natural Language Inference | MultiNLI | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Matched | 90.2 | #11 of 67 | Archive leaderboard | report |
| Natural Language Inference | QNLI | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Accuracy | 94.7% | #13 of 43 | Archive leaderboard | report |
| Natural Language Inference | RTE | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Accuracy | 85.4% | #25 of 90 | Archive leaderboard | report |
| Semantic Textual Similarity | MRPC | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Accuracy | 91.0% | #7 of 45 | Archive leaderboard | report |
| Semantic Textual Similarity | STS Benchmark | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Pearson Correlation | 0.919 | #9 of 66 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned) | Accuracy | 96.4 | #16 of 87 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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