---
name: rwkv-architecture
description: RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [RWKV, Model Architecture, RNN, Transformer Hybrid, Linear Complexity, Infinite Context, Efficient Inference, Linux Foundation, Alternative Architecture]
dependencies: [rwkv, torch, transformers]
---
# RWKV - Receptance Weighted Key Value
## Quick start
RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).
**Installation**:
```bash
# Install PyTorch
pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121
# Install dependencies
pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade
# Install RWKV
pip install rwkv
```
**Basic usage** (GPT mode + RNN mode):
```python
import os
from rwkv.model import RWKV
os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # Use CUDA kernel for speed
# Load model
model = RWKV(
model='/path/to/RWKV-4-Pile-1B5-20220903-8040',
strategy='cuda fp16'
)
# GPT mode (parallel processing)
out, state = model.forward([187, 510, 1563, 310, 247], None)
print(out.detach().cpu().numpy()) # Logits
# RNN mode (sequential processing, same result)
out, state = model.forward([187, 510], None) # First 2 tokens
out, state = model.forward([1563], state) # Next token
out, state = model.forward([310, 247], state) # Last tokens
print(out.detach().cpu().numpy()) # Same logits as above!
```
## Common workflows
### Workflow 1: Text generation (streaming)
**Efficient token-by-token generation**:
```python
from rwkv.model import RWKV
from rwkv.utils import PIPELINE
model = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')
pipeline = PIPELINE(model, "20B_tokenizer.json")
# Initial prompt
prompt = "The future of AI is"
state = None
# Generate token by token
for token in prompt:
out, state = pipeline.model.forward(pipeline.encode(token), state)
# Continue generation
for _ in range(100):
out, state = pipeline.model.forward(None, state)
token = pipeline.sample_logits(out)
print(pipeline.decode(token), end='', flush=True)
```
**Key advantage**: Constant memory per token (no growing KV cache)
### Workflow 2: Long context processing (infinite context)
**Process million-token sequences**:
```python
model = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')
# Process very long document
state = None
long_document = load_document() # e.g., 1M tokens
# Stream through entire document
for chunk in chunks(long_document, chunk_size=1024):
out, state = model.forward(chunk, state)
# State now contains information from entire 1M token document
# Memory usage: O(1) (constant, not O(n)!)
```
### Workflow 3: Fine-tuning RWKV
**Standard fine-tuning workflow**:
```python
# Training script
import pytorch_lightning as pl
from rwkv.model import RWKV
from rwkv.trainer import RWKVTrainer
# Configure model
config = {
'n_layer': 24,
'n_embd': 1024,
'vocab_size': 50277,
'ctx_len': 1024
}
# Setup trainer
trainer = pl.Trainer(
accelerator='gpu',
devices=8,
precision='bf16',
strategy='deepspeed_stage_2',
max_epochs=1
)
# Train
model = RWKV(config)
trainer.fit(model, train_dataloader)
```
### Workflow 4: RWKV vs Transformer comparison
**Memory comparison** (1M token sequence):
```python
# Transformer (GPT)
# Memory: O(n²) for attention
# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)
# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)
# RWKV
# Memory: O(1) per token
# State: hidden_dim × n_layers = 4096 × 24 = ~400KB
# 1,000,000× more efficient!
```
**Speed comparison** (inference):
```python
# Transformer: O(n) per token (quadratic overall)
# First token: 1 computation
# Second token: 2 computations
# ...
# 1000th token: 1000 computations
# RWKV: O(1) per token (linear overall)
# Every token: 1 computation
# 1000th token: 1 computation (same as first!)
```
## When to use vs alternatives
**Use RWKV when**:
- Need very long context (100K+ tokens)
- Want constant memory usage
- Building streaming applications
- Need RNN efficiency with Transformer performance
- Memory-constrained deployment
**Key advantages**:
- **Linear time**: O(n) vs O(n²) for Transformers
- **No KV cache**: Constant memory per token
- **Infinite context**: No fixed window limit
- **Parallelizable training**: Like GPT
- **Sequential inference**: Like RNN
**Use alternatives instead**:
- **Transformers**: Need absolute best performance, have compute
- **Mamba**: Want state-space models
- **RetNet**: Need retention mechanism
- **Hyena**: Want convolution-based approach
## Common issues
**Issue: Out of memory during training**
Use gradient checkpointing and DeepSpeed:
```python
trainer = pl.Trainer(
strategy='deepspeed_stage_3', # Full ZeRO-3
precision='bf16'
)
```
**Issue: Slow inference**
Enable CUDA kernel:
```python
os.environ["RWKV_CUDA_ON"] = '1'
```
**Issue: Model not loading**
Check model path and strategy:
```python
model = RWKV(
model='/absolute/path/to/model.pth',
strategy='cuda fp16' # Or 'cpu fp32' for CPU
)
```
**Issue: State management in RNN mode**
Always pass state between forward calls:
```python
# WRONG: State lost
out1, _ = model.forward(tokens1, None)
out2, _ = model.forward(tokens2, None) # No context from tokens1!
# CORRECT: State preserved
out1, state = model.forward(tokens1, None)
out2, state = model.forward(tokens2, state) # Has context from tokens1
```
## Advanced topics
**Time-mixing and channel-mixing**: See [references/architecture-details.md](references/architecture-details.md) for WKV operation, time-decay mechanism, and receptance gates.
**State management**: See [references/state-management.md](references/state-management.md) for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.
**RWKV-7 improvements**: See [references/rwkv7.md](references/rwkv7.md) for latest architectural improvements (March 2025) and multimodal capabilities.
## Hardware requirements
- **GPU**: NVIDIA (CUDA 11.6+) or CPU
- **VRAM** (FP16):
- 169M model: 1GB
- 430M model: 2GB
- 1.5B model: 4GB
- 3B model: 8GB
- 7B model: 16GB
- 14B model: 32GB
- **Inference**: O(1) memory per token
- **Training**: Parallelizable like GPT
**Performance** (vs Transformers):
- **Speed**: Similar training, faster inference
- **Memory**: 1000× less for long sequences
- **Scaling**: Linear vs quadratic
## Resources
- Paper (RWKV): https://arxiv.org/abs/2305.13048 (May 2023)
- Paper (RWKV-7): https://arxiv.org/abs/2503.14456 (March 2025)
- GitHub: https://github.com/BlinkDL/RWKV-LM ⭐ 12,000+
- Docs: https://wiki.rwkv.com/
- Models: https://huggingface.co/BlinkDL
- Linux Foundation AI: Official project
- Production: Microsoft Windows, Office integration, NeMo support