Transformer is Dead : Best Transformer alternates for LLMs
Mamba, Google Titans, Byte Latent transformers, Kimi-Linear and others
Transformers have run the world long enough. Every big model you know, GPT, Gemini, Claude, even the local open-weights stuff, uses that same attention backbone from 2017. It worked like magic for a while. Then context windows started choking, memory bills shot up, and efficiency hit a ceiling. The industry’s been quietly hunting for something better: leaner, longer, maybe even simpler.
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This year, that search actually got somewhere. A handful of architectures — Mamba, Byte Latent Transformer, Google’s Titans, and Kimi Linear — have started to show that you can break free from the Transformer formula without losing the magic.
Let’s go through them one by one, without pretending they’re all silver bullets.
Mamba: The Return of Sequence Thinking
The Mamba architecture was built around an old but powerful idea: state-space models.
https://medium.com/media/5d5dc384a40725f90a97f07b6f7adc47/href
Before attention took over, sequence models relied on things like RNNs and convolutions to move information step by step. Mamba revives that, but in a continuous, mathematically smarter way.
Instead of attending to every token pair (which gives Transformers their O(N²) cost), Mamba builds a structured state that rolls forward in time. Think of it like a smooth conveyor belt where context is encoded as a set of evolving internal states rather than pairwise interactions. The benefit: linear scaling, O(N) rather than O(N²).
That means it can handle long contexts (tens of thousands of tokens) without blowing up your GPU memory. You don’t get the “all-to-all” awareness of Transformers, but for continuous data , speech, text streams, logs, it’s usually enough.
When to use it
You’re dealing with long sequences: documents, transcripts, time-series, anything that doesn’t fit neatly into a small context. Mamba is still experimental, but it’s already making long-context modeling practical.
Byte Latent Transformer: Kill the Tokenizer
Meta’s Byte Latent Transformer (BLT) takes a different swing at the problem. Instead of changing how the model thinks, it changes what it thinks over.
Transformers work on tokens: chunks of text split by some fixed vocabulary. It’s convenient but messy, tokenization is language-biased, brittle for code, and stupidly wasteful for long, simple inputs. BLT throws that out.
It works directly on raw bytes, grouping them into variable-sized patches depending on entropy. Complex regions get fine patches, simple ones get coarse. The model allocates compute where it matters most. It’s almost like the model’s eyes dilate depending on detail.
The upside is huge efficiency gains, fewer tokens to process, and no tokenizer headaches. The downside: it’s still young, and training pipelines for byte-level data aren’t as mature.
When to use it
If you’re handling messy data: multilingual corpora, code, noisy logs. Or if you just hate tokenization and want models that can scale better across domains. It’s still early tech, but it hints at a post-token world.
Google Titans: Memory That Actually Remembers
https://medium.com/media/0fd92c2095e9d9266f5b75d7ca108b98/href
Titans from Google is less about attention and more about memory systems. Transformers, even the large ones, forget context fast. You feed them 2M tokens and they technically “see” it, but not really, the attention cost is too high to actually store or recall long dependencies.
Titans re-architects the model to separate short-term attention from long-term memory. Think of it as a Transformer with an attached brain: attention handles immediate context, while the memory module stores older patterns for later recall.
It’s an early attempt at giving LLMs something like human memory persistent, selective, scalable. Reports say Titans can scale context to millions of tokens without collapsing compute.
When to use it
Long-running systems. Think conversational agents that need to remember past interactions, or models that read books, logs, and multi-session data without retrieval hacks. For now, it’s more research-lab than production-ready, but the direction is right.
Kimi Linear (and its MoE cousin, K2): Efficiency Meets Scale
https://medium.com/media/c9b32ca36ae39477f5c3b51624bf388c/href
Moonshot AI’s Kimi Linear model takes a subtler route.
Instead of replacing attention outright, it reworks it to scale linearly. The core idea: approximate or compress the attention operation so it no longer depends quadratically on sequence length.
This keeps the Transformer’s “relational” power but trims cost. It’s practical, not radical.
Then there’s Kimi K2, the Mixture-of-Experts (MoE) version. It’s massive, trillion parameters, but only activates a fraction of them per token. Like having a panel of specialists where only a few speak at a time. This gives it huge capacity without insane inference cost.
The Kimi linear proves that architecture innovation doesn’t have to be about exotic math, it can be smart engineering around routing, sparsity, and compute awareness.
When to use it
You need performance close to GPT-class models but can’t afford the compute. MoE helps scale efficiently; Linear attention helps keep memory under control. It’s one of the few directions that’s both researchy and production-feasible.
Meta’s Large Concept Model (LCM): Thinking in Ideas, Not Tokens
https://medium.com/media/9dca7848158915a4248b6d7f54712e1c/href
Then there’s Meta’s Large Concept Model, maybe the most radical shift of them all.
Transformers think in tokens: word by word, next prediction by next prediction. LCM thinks in concepts. It models entire sentences or ideas inside a shared high-dimensional space called SONAR. That space is language- and modality-agnostic, meaning it works the same for English, Hindi, or even audio transcripts.
Instead of predicting the next token, LCM predicts the next concept, a semantic leap rather than a lexical one. The result: shorter effective sequence lengths, smoother long-form coherence, and better multilingual transfer.
You can think of it as zooming out from words to thoughts. It doesn’t sweat over commas; it tracks meaning.
Early results show a 7B LCM beating same-sized Llama models in multilingual summarization. But it’s still early-stage: concept-level modeling trades fine-grained control for broader reasoning. And that extra encoding/decoding pipeline (sentence → embedding → concept → output) adds some complexity.
When to use
Long-form, idea-rich content. Multilingual or multimodal projects. Or anytime you want reasoning over semantics instead of syntax.
It’s not a plug-in Transformer replacement yet, but it’s probably a glimpse of what “language modeling” will look like five years from now.
The Broader Shift: From Attention to Allocation
If you zoom out, all these models are chasing the same thing: smarter allocation of compute and memory. Transformers spend equal attention on every token pair, brute force elegance, but reality isn’t that uniform.
- Mamba allocates compute over time, maintaining only necessary state.
- BLT allocates across space, varying patch size.
- Titans allocates across memory, splitting short vs long term.
- Kimi allocates across experts, activating selectively.
It’s not about rejecting Transformers, it’s about escaping their worst inefficiencies.
So, When Should You Move Beyond Transformers?
If you’re working with:
- Short text or general fine-tuning, Transformers still win. Tooling, data, and benchmarks all live there.
- Long documents or streaming inputs, go Mamba or a linear-attention variant.
- Multilingual or byte-heavy data, Byte Latent Transformer could be a better foundation.
- Persistent conversational memory or million-token contexts, Titans (or any memory-augmented model) is the future.
- Scalable, efficient training for large models, Kimi’s MoE and linear ideas are the practical way forward.
For now, most production systems still rely on Transformers. But the foundation is shifting. These new architectures aren’t replacements yet they’re prototypes of the next generation. The real takeaway is that attention isn’t sacred anymore. It’s just one way to reason, and not always the best.
Transformer is Dead : Best Transformer alternates for LLMs was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
