The default that wins by default
For the majority of teams in 2026, the right architecture is still standard Transformer + FlashAttention-3 + GQA. This is the default for: general-purpose language tasks under 32K tokens, complex reasoning and multi-step logic, few-shot in-context learning, code generation that respects distant context, any task where ecosystem maturity matters (fine-tuning, serving, community support, monitoring, debugging tooling).
If your use case doesn't hit Transformer bottlenecks, there's no reason to switch. Most tasks in 2026 are still best served by Transformers. The overhead of adopting a new architecture — retraining from scratch or finding compatible checkpoints, building new serving infrastructure, accepting limited community support, dealing with quirky training stability — only pays off if you're hitting concrete scaling walls that you can name and measure.
The optimization stack to exhaust first
Before considering an architecture change, exhaust the Transformer optimization stack. Most teams haven't:
- FlashAttention-3 for the attention compute itself.
- GQA or MQA for KV-cache reduction.
- Sliding window attention for sub-quadratic effective compute when context patterns allow.
- Quantization (INT8, FP8, AWQ, GPTQ, SmoothQuant) for memory and throughput.
- Speculative decoding with a draft model to amortize generation cost.
- vLLM PagedAttention or equivalents for KV-cache memory management at serving time.
- Continuous batching for multi-user throughput.
Together these can buy 5–10× effective serving capacity at the same hardware cost. If you haven't applied them and you're already considering switching architecture, you're probably solving the wrong problem.