Transformers vs. LSTMs in Modern LLMs

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• Recent Research Highlights (2024-2025)

• Scalability

• Computational Efficiency

• Parallelization

• Contextual Understanding and Long-Range Dependencies

• Memory Mechanisms

• Real-World Implementations and LLM Deployments

• Conclusion Why Transformers Have Replaced LSTMs

Introduction: Long Short-Term Memory (LSTM) networks were once the state-of-the-art for language modeling, powering sequence tasks like translation and text generation until the advent of Transformers in 2017 (HERE). The Transformer architecture (Vaswani et al. 2017) introduced self-attention mechanisms and the ability to process sequences in parallel, which dramatically reshaped the landscape of large language models (LLMs) . Today’s LLMs typically rely on Transformer blocks and scale to billions of parameters ( Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges). Below, we review recent literature (2024–2025) comparing Transformers and LSTMs, highlighting why Transformers have largely replaced LSTMs in modern LLM architectures.

Recent Research Highlights (2024-2025)

• Feng et al. (2024) – “Were RNNs All We Needed?” – This work revisits recurrent networks in the era of LLMs ( Were RNNs All We Needed?). The authors propose simplified LSTM/GRU variants (“minLSTM/GRU”) with fewer parameters that are fully parallelizable during training and still achieve competitive performance on language tasks, rivaling Transformer-based models . Their motivation stems from the scalability limitations of vanilla Transformers for very long sequences , suggesting that with careful design, RNNs can close the gap.

• Beck et al. (2024) – Extended LSTM (xLSTM) – Beck and colleagues identify three key limitations of standard LSTMs: (i) inability to revise stored information, (ii) limited memory capacity (compressing information into a fixed cell state), and (iii) lack of parallelism due to sequential state updates (HERE). They introduce xLSTM, which adds innovations like exponential gating and a “matrix memory” (multi-head storage) to overcome these issues. The xLSTM achieves performance on par with Transformers in language modeling while enjoying linear growth in memory usage (versus quadratic for Transformers) as sequence length increases (Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?). This addresses scalability challenges and indicates LSTMs can be scaled up when their bottlenecks are removed.

• Hou & Yu (2024) – VisualRWKV (RNN-based LLM) – This work applies the Receptance Weighted Key-Value (RWKV) model – a novel RNN architecture – to vision-language tasks. RWKV (Peng et al. 2023) is a linear-time RNN that combines transformer-like parallelizable training with recurrent inference ( RWKV: Reinventing RNNs for the Transformer Era). Notably, RWKV has shown competitive performance to GPT-class Transformers on large-scale language benchmarks while scaling linearly with sequence length, positioning it as a potential successor in resource-constrained settings (VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models). The VisualRWKV results demonstrate that an RNN-based LLM can match transformer-based models on multimodal tasks with far lower memory footprint .

• Kou et al. (2025) – Domain Benchmarking – In a large-scale energy load forecasting study, Kou et al. compare LSTM, xLSTM, and Transformer models. They report that many recent studies “have replaced LSTMs with Transformer models due to their ability to handle long-range dependencies and efficiently parallelize computations.” (Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?). Empirically, the Transformer outperforms LSTM variants in accuracy on aggregated time-series predictions . This domain evidence aligns with broader NLP findings that Transformers yield better performance when sufficient data and long contexts are involved.

Scalability

Model Size & Data Scaling: Modern LLMs owe much of their success to being trained on massive datasets and scaled to huge model sizes (billions of parameters), which Transformers facilitate. Transformers stack dozens of layers without the training instabilities that deep RNNs often face, and they excel when paired with self-supervised training on extensive data (HERE). Surveys note that current state-of-the-art LLMs (e.g. GPT-3, PaLM) are almost exclusively transformer-based, with architectures able to handle the complexity of training across trillions of tokens ( Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges) . In contrast, LSTMs historically plateaued at smaller scales; training extremely deep or large LSTMs is challenging due to vanishing/exploding gradients (mitigated but not eliminated by gating) and slower training. The Transformer’s design thus unlocks greater scalability in model size and dataset size, directly contributing to the performance leaps in modern LLMs.

Long Sequence Scaling: Transformers also scale better in how they utilize context. They can be extended to long input sequences (thousands of tokens) by increasing the positional encoding range or using sparse/efficient attention variants. LSTMs, by design, process one step at a time regardless of sequence length, which makes very long sequences slow and difficult to learn from. While the theoretical per-step complexity of an LSTM is lower, maintaining useful information over long ranges is hard (memory cell limitations) and training on long sequences is time-consuming. Researchers have noted that overcoming LSTM limitations and scaling them to the size of current LLMs is non-trivial, and those very limitations “paved the way for the emergence of Transformers in language modeling” (HERE).

Computational Efficiency

Training Efficiency: A major advantage of Transformers is their ability to leverage parallel computation. In an LSTM, each time step’s computation depends on the previous step’s output, creating a fundamental sequential bottleneck. Transformers eliminate this by processing all positions in a sequence simultaneously with self-attention. This enables orders-of-magnitude faster training on modern hardware. As one study succinctly states, “classical RNNs exhibit a time-dependency… limiting parallelization,” whereas “Transformers, in contrast, allow processing the entire sequence at once,” greatly accelerating training (HERE). In practice, this means training a Transformer on billions of tokens is feasible within reasonable time, while an equally large LSTM would be prohibitively slow.

Inference and Complexity: Transformers have a higher nominal complexity per sequence (O(n²) in sequence length for naive self-attention) compared to LSTMs (O(n)). For very long sequences, this quadratic cost becomes a concern . Nonetheless, for typical LLM context lengths (hundreds to a few thousands of tokens), optimized Transformer implementations (e.g. efficient attention algorithms) and hardware acceleration make them quite efficient. The parallelizable operations of Transformers keep GPUs highly utilized, offsetting the algorithmic complexity. By contrast, LSTM inference is O(n) per token sequentially – seemingly cheaper, but it cannot take full advantage of matrix operation parallelism. A 2024 analysis notes that the self-attention mechanism’s quadratic growth in compute/memory with sequence length does lead to high inference cost for extremely long inputs, limiting deployment on edge devices (VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models). This has spurred research into linear-time alternatives (like RWKV, state-space models, etc.), but for most real-world LLM usage, Transformers strike a favorable balance of throughput and sequence length handled.

Parallelization

Transformers are explicitly designed for maximal parallelization. During training, all tokens in a sequence are processed in parallel through matrix multiplications and attention, which is ideal for GPU/TPU acceleration. Feng et al. note that Transformers became the de facto sequence modeling method by “leveraging parallelization during training” to overcome RNNs’ sequential limits (Were RNNs All We Needed? - arXiv). In contrast, LSTMs (and RNNs generally) have inherent sequential dependencies: each step must wait for the previous step’s state (HERE). Even with techniques like truncated backpropagation or parallelizing across batch sequences, RNNs cannot match the token-level parallelism of Transformers. This means Transformers can scale to much larger batch sizes and fully utilize modern hardware, yielding higher training throughput. Indeed, eliminating the “lack of parallelizability” of LSTMs is one of the key challenges addressed by recent extended LSTM models (HERE). Even for inference or fine-tuning, Transformers can use parallel attention across layers, whereas an LSTM’s stepwise processing is harder to speed up. This parallel nature is a fundamental reason Transformers have supplanted LSTMs in large-scale modeling.

Contextual Understanding and Long-Range Dependencies

A well-known limitation of LSTMs is their difficulty in capturing very long-range dependencies. While LSTMs do have a gating mechanism to mitigate forgetting, in practice their memory of distant tokens tends to fade or compress. Transformers, on the other hand, attend to all tokens in a context window, allowing even distant words to directly influence the next-token prediction through self-attention weights. This gives Transformers a superior ability to model global context and complex dependencies in text. For example, recent literature observes that many studies switched from LSTMs to Transformers specifically for their ability to handle long-range dependencies (Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?). An RNN must carry information forward step by step, which can lead to vanishing influence over long sequences, whereas a Transformer can learn direct interactions between far-apart positions in one attention hop. Empirically, Transformers achieve lower perplexities and better coherence on long texts, since they do not need to compress the entire history into a fixed-size state – they revisit the actual past representations via attention. LSTMs still excel at local sequence modeling and can handle moderate-length sequences well, but on truly long documents or code, they struggle unless augmented with external memory. Overall, the context integration in Transformers is more flexible and robust, enabling richer understanding of the input.

Memory Mechanisms

The memory handling in LSTMs and Transformers differs fundamentally. LSTMs rely on an explicit memory cell that gets updated at each step through gated additions and forgetting. This cell is of fixed dimension, meaning the model must continuously overwrite or compress information. As sequence length grows, more information is distilled into the cell state, which can bottleneck memory capacity – for instance, LSTMs perform worse on rare or infrequent tokens because the cell state prioritizes frequent patterns (HERE). By contrast, Transformers use distributed memory: every token’s embedding (across many layers) can be considered a memory of that token’s content and its context. Through self-attention, each new token can retrieve relevant information from any prior token’s representation, effectively using the entire sequence as a dynamic memory store. There is no single memory vector that must carry all information; instead, memory is decentralized across the sequence. This allows Transformers to retain detailed information about many different parts of the input, as needed. The trade-off is that storing all these token representations is memory-intensive (scales with sequence length), whereas LSTMs’ state is constant size. Recent advances like xLSTM’s matrix memory try to give LSTMs more memory capacity by having multiple cell slots or heads (Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?). Nonetheless, in standard forms, Transformers have a clear advantage in memory richness: they can effectively memorize long sequences up to the context limit, and even extend that limit with techniques (at cost), whereas vanilla LSTMs’ memory is limited and tends to forget older inputs. This richer memory mechanism contributes to Transformers’ stronger performance on tasks requiring complex context and reasoning.

Real-World Implementations and LLM Deployments

All state-of-the-art LLM deployments in recent years use Transformer-based architectures. Models like GPT-3, GPT-4, PaLM, LaMDA, LLaMA, and others are built on the Transformer due to the above advantages (parallel training, long-context handling, scalability). In fact, by 2024 the “vast majority of models” in NLP and even computer vision are using the Transformer architecture (HERE). This dominance is also self-reinforcing: the research community and industry have invested heavily in optimizing transformer models (software libraries, hardware accelerators, etc.), further widening the gap to RNN-based alternatives.

That said, contemporary research has produced some noteworthy LSTM/RNN-based LLM implementations as proofs of concept. The minLSTM approach of Feng et al. (2024) demonstrated that with simplifications and parallel training, LSTMs can scale and perform surprisingly well, even approaching transformer quality on certain benchmarks ( Were RNNs All We Needed?). The xLSTM (2024) and RWKV (2023–24) models show that recurrent architectures can be enhanced to handle longer contexts and parallel workloads, yielding efficiency benefits (e.g. linear memory scaling) while maintaining competitive accuracy (Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?). These implementations hint that RNN-based LLMs could be viable for specialized cases – for example, on edge devices or tasks requiring extremely long sequences (where the Transformer's quadratic cost is problematic). However, none of these have yet supplanted Transformers in general-purpose, large-scale deployments. They often match a baseline Transformer on smaller scales, but the largest and most capable LLMs (with hundreds of billions of parameters and trained on massive corpora) remain Transformer-based. In real-world applications requiring the best possible performance, Transformers are the go-to architecture, while advanced RNN variants are experimental or used for efficiency in niche scenarios.

Conclusion Why Transformers Have Replaced LSTMs

In summary, Transformers have largely replaced LSTMs in modern LLM architectures because of superior scalability, efficiency, and ability to handle complex context. The Transformer’s fully-parallel self-attention mechanism enables training on enormous datasets and scaling to very large models, which was crucial for recent breakthroughs (HERE). Transformers mitigate the long-range dependency issues that RNNs/LSTMs face, by allowing direct interactions between distant tokens and avoiding the memory bottleneck of a single cell state (Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?). Although Transformers introduce higher computational complexity for long sequences, their alignment with parallel hardware and algorithmic optimizations ensure that they train faster and more effectively than sequential LSTMs in practice (VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models). Empirically, Transformers have achieved state-of-the-art results across NLP tasks, whereas LSTMs plateaued, making Transformers the default choice for virtually all cutting-edge LLMs. In essence, the architectural strengths of Transformers in parallelization, contextual representation, and flexible memory have proven decisive — addressing the very limitations of LSTMs that once constrained the scale and performance of language models . The result is that nearly all modern LLMs adopt Transformers, cementing their place as the foundation of contemporary NLP systems.

Sources: Recent arXiv papers and surveys from 2024–2025 have been cited to support this review, including works by Feng et al. (2024) ( Were RNNs All We Needed?), Beck et al. (2024) , Hou & Yu (2024) , and others that benchmark LSTMs vs Transformers . These reflect the consensus that Transformer-based LLMs offer decisive advantages over their LSTM predecessors in most real-world scenarios.