RWKV
RNN-meets-transformer linear-attention LM architecture running with O(n) memory—unique open line for long-context and embedded inference.
Why it is included
Non-transformer open architecture with active community ports (CUDA, Metal, Web).
Best for
Experimenters wanting recurrent-style LLMs without full KV cache growth.
Strengths
- Linear time
- Embedded friendly
- Multiple runtimes
Limitations
- Ecosystem smaller than Llama for tooling
Good alternatives
Transformer LMs (Llama-class) · Linear-attention research stacks
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