RWKV

Health v2 · maintenance only

Adoption not part of the score

14683 stars · 1020 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

RWKV is a novel language model architecture that combines RNN efficiency (linear time, constant memory, no KV-cache) with transformer-level LLM performance and parallelizable GPT-style training. The current RWKV-7 'Goose' release includes model weights, training code, and inference tooling, and is a Linux Foundation AI project under Apache-2.0.

Use cases

• train a large language model with linear-time attention instead of transformers
• run LLM inference with constant memory and no KV-cache
• deploy a chatbot locally on mobile or desktop
• run LLM inference on GPU with high throughput for large batch sizes
• get free sentence embeddings from a language model
• experiment with RNN-based alternatives to GPT architectures
• run an LLM with effectively infinite context length

When to choose

• you need constant-memory, linear-time LLM inference without KV-cache growth
• you want to train a GPT-like model but with RNN efficiency at inference
• you need efficient on-device or mobile LLM inference
• you want very high batch-size inference throughput on a single GPU

When to avoid

• you need the broad ecosystem and tooling compatibility of mainstream transformer models
• your stack depends on fine-tuning frameworks built only for standard attention transformers
• you require maximum community support and prebuilt integrations over architectural efficiency

Facets

library · maturity active

machine-learning deep-learning llm-inference llm-training transformers sdk large-language-models deep-learning machine-learning artificial-intelligence python cross-platform windows rnn linear-attention attention-free language-model pytorch rwkv gpt constant-memory infinite-context chat-model natural-language-processing gpu linux macos docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="BlinkDL/RWKV-LM")