RuvLLM ESP32 - Tiny LLM inference for ESP32 microcontrollers with INT8 quantization, RAG, HNSW vector search, hyperbolic embeddings (Poincaré/Lorentz), and multi-chip federation. Run AI on $4 hardware.
npm install ruvllm-esp32
Run AI locally on ESP32 microcontrollers - A complete, production-ready LLM inference engine with INT8/Binary quantization, HNSW vector search, RAG (Retrieval-Augmented Generation), and multi-chip federation support. No cloud required.
Run AI directly on microcontrollers without cloud dependencies:
- Privacy: Data never leaves the device
- Latency: No network round-trips (2-5ms/token)
- Cost: Zero API fees, runs on $4 hardware
- Offline: Works without internet connectivity
- Edge AI: Perfect for IoT, robotics, wearables
| Category | Features |
|----------|----------|
| Inference | INT8 quantized transformers, 2-5ms/token @ 240MHz |
| Compression | Binary quantization (32x), Product quantization (8-32x) |
| Adaptation | MicroLoRA on-device fine-tuning (2KB overhead) |
| Attention | Sparse patterns: sliding window, strided, BigBird |
| Vector Search | HNSW index with 1000+ vectors in ~20KB RAM |
| Memory | Semantic memory with context-aware retrieval + TTL |
| RAG | Retrieval-Augmented Generation for knowledge bases |
| Anomaly | Statistical outlier detection via embeddings |
| Speedup | Speculative decoding (2-4x potential) |
| Scaling | Multi-chip federation with pipeline/tensor parallelism |
| Variant | SRAM | CPU | Features |
|---------|------|-----|----------|
| ESP32 | 520KB | Xtensa LX6 @ 240MHz | WiFi, Bluetooth |
| ESP32-S2 | 320KB | Xtensa LX7 @ 240MHz | USB OTG |
| ESP32-S3 | 512KB | Xtensa LX7 @ 240MHz | SIMD/Vector, USB OTG |
| ESP32-C3 | 400KB | RISC-V @ 160MHz | Low power, WiFi 4 |
| ESP32-C6 | 512KB | RISC-V @ 160MHz | WiFi 6, Thread |
Recommended: ESP32-S3 for best performance (SIMD acceleration)
---
``bashInstall ESP32 toolchain
npx ruvllm-esp32 install
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Linux/macOS:
bash
git clone https://github.com/ruvnet/ruvector
cd ruvector/examples/ruvLLM/esp32-flash
./install.sh # Install deps + build
./install.sh flash # Flash to auto-detected port
`Windows (PowerShell):
powershell
git clone https://github.com/ruvnet/ruvector
cd ruvector\examples\ruvLLM\esp32-flash
.\install.ps1 # Install deps (restart PowerShell after)
.\install.ps1 build # Build
.\install.ps1 flash COM6 # Flash
`$3
bash
Install ESP32 toolchain
cargo install espup espflash ldproxy
espup install
source ~/export-esp.sh # Linux/macOSClone and build
git clone https://github.com/ruvnet/ruvector
cd ruvector/examples/ruvLLM/esp32-flash
cargo build --releaseFlash
espflash flash --monitor --port /dev/ttyUSB0 \
target/xtensa-esp32-espidf/release/ruvllm-esp32
`---
Complete Feature Guide
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#### Binary Quantization (32x compression)
Packs weights into 1-bit representation with sign encoding:
Original: [-0.5, 0.3, -0.1, 0.8] (32 bytes)
Binary: [0b1010] (1 byte) + scale
#### Product Quantization (8-32x compression)
Splits vectors into subspaces with learned codebooks:
- 8 subspaces with 16 centroids each
- Asymmetric Distance Computation (ADC) for fast search
- Configurable compression ratio
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Reduce attention complexity from O(n²) to O(n):
| Pattern | Description | Best For |
|---------|-------------|----------|
| Sliding Window | Local context only | Long sequences |
| Strided | Every k-th position | Periodic patterns |
| BigBird | Global + local + random | General purpose |
| Dilated | Exponentially increasing gaps | Hierarchical |
| Causal | Lower triangular mask | Autoregressive |
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On-device model fine-tuning with minimal overhead:
- Rank: 1-2 (trades quality for memory)
- Memory: ~2KB per layer
- Use case: Personalization, domain adaptation
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Hierarchical Navigable Small World index:
- Capacity: 1000+ vectors in ~20KB
- Latency: <1ms search time
- Metrics: Euclidean, Cosine, Dot Product
- Binary mode: For memory-constrained variants
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Context-aware memory with intelligent retrieval:
- Memory types: Factual, Episodic, Procedural
- TTL support: Auto-expire old memories
- Importance scoring: Prioritize critical information
- Temporal decay: Recent memories weighted higher
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Combine retrieval with generation:
> add The capital of France is Paris
Added knowledge #1> ask what is the capital of France
Found: The capital of France is Paris
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Detect outliers using embedding distance:
> anomaly this is normal text
NORMAL (score: 15, threshold: 45)> anomaly xkcd random gibberish 12345
ANOMALY (score: 89, threshold: 45)
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Draft-verify approach for faster generation:
- Draft model generates 4 tokens speculatively
- Target model verifies in parallel
- Accept matching tokens, reject mismatches
- Speedup: 2-4x on supported models
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Scale beyond single-chip memory limits:
#### Pipeline Parallelism
Split model layers across chips:
Chip 1: Layers 0-3 → Chip 2: Layers 4-7 → Output
#### Tensor Parallelism
Split each layer across chips:
┌─ Chip 1: Head 0-3 ─┐
Input ───┤ ├───> Output
└─ Chip 2: Head 4-7 ─┘
---
Serial Commands
Connect at 115200 baud after flashing:
════════════════════════════════════════════
RuvLLM ESP32 Full-Feature v0.2
════════════════════════════════════════════
Features: Binary Quant, PQ, LoRA, HNSW, RAG
Semantic Memory, Anomaly Detection
Speculative Decoding, Federation
════════════════════════════════════════════
Type 'help' for commands
>
| Command | Description | Example |
|---------|-------------|---------|
|
| Generate tokens from prompt | gen Hello world |
| add | Add knowledge to RAG | add Meeting at 3pm |
| ask | Query knowledge base | ask when is meeting |
| anomaly | Check for anomaly | anomaly test input |
| stats | Show system statistics | stats |
| features | List enabled features | features |
| help | Show command help | help |---
Platform-Specific Setup
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`powershell
Install Rust
winget install Rustlang.Rust.MSVCInstall ESP32 toolchain
cargo install espup espflash ldproxy
espup installRESTART PowerShell to load environment
Build and flash
cargo build --release
espflash flash --port COM6 --monitor target\xtensa-esp32-espidf\release\ruvllm-esp32
`$3
bash
Install Rust
brew install rustup
rustup-init -y
source ~/.cargo/envInstall ESP32 toolchain
cargo install espup espflash ldproxy
espup install
source ~/export-esp.shBuild and flash
cargo build --release
espflash flash --port /dev/cu.usbserial-0001 --monitor target/xtensa-esp32-espidf/release/ruvllm-esp32
`$3
bash
Install prerequisites (Debian/Ubuntu)
sudo apt install build-essential pkg-config libudev-dev
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source ~/.cargo/envInstall ESP32 toolchain
cargo install espup espflash ldproxy
espup install
source ~/export-esp.shAdd user to dialout group (for serial access)
sudo usermod -a -G dialout $USER
Log out and back in
Build and flash
cargo build --release
espflash flash --port /dev/ttyUSB0 --monitor target/xtensa-esp32-espidf/release/ruvllm-esp32
`---
Cluster Setup (Multi-Chip)
For models larger than single-chip memory:
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bash
npx ruvllm-esp32 cluster --chips 5
or
make cluster CHIPS=5
`$3
toml
[cluster]
name = "my-cluster"
chips = 5
topology = "pipeline" # or "tensor"[[chips.nodes]]
id = 1
role = "master"
port = "/dev/ttyUSB0"
layers = [0, 1]
[[chips.nodes]]
id = 2
role = "worker"
port = "/dev/ttyUSB1"
layers = [2, 3]
... more chips
`$3
bash
./cluster-flash.sh
or
npx ruvllm-esp32 cluster flash
`$3
bash
./cluster-monitor.sh # Opens tmux with all serial monitors
`---
Memory & Performance
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| Component | RAM | Flash |
|-----------|-----|-------|
| LLM Model (INT8) | ~20 KB | ~16 KB |
| HNSW Index (256 vectors) | ~8 KB | — |
| RAG Knowledge (64 entries) | ~4 KB | — |
| Semantic Memory (32 entries) | ~2 KB | — |
| Anomaly Detector | ~2 KB | — |
| UART + Stack | ~9 KB | — |
| Total | ~45 KB | ~16 KB |
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| Operation | ESP32 @ 240MHz | ESP32-S3 (SIMD) |
|-----------|----------------|-----------------|
| Token generation | ~4ms/token | ~2ms/token |
| HNSW search (256 vectors) | ~1ms | ~0.5ms |
| Embedding (64-dim) | <1ms | <0.5ms |
| Anomaly check | <1ms | <0.5ms |
| Binary quant inference | ~1.5ms | ~0.8ms |
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- Standard: ~200-250 tokens/sec (simulated)
- With speculative: ~400-500 tokens/sec (simulated)
- Actual ESP32: ~200-500 tokens/sec depending on model
---
Project Structure
esp32-flash/
├── Cargo.toml # Rust config with feature flags
├── src/
│ ├── lib.rs # Library exports
│ ├── main.rs # Full-featured ESP32 binary
│ ├── optimizations/
│ │ ├── binary_quant.rs # 32x compression
│ │ ├── product_quant.rs # 8-32x compression
│ │ ├── lookup_tables.rs # Pre-computed LUTs
│ │ ├── micro_lora.rs # On-device adaptation
│ │ ├── sparse_attention.rs # Memory-efficient attention
│ │ └── pruning.rs # Weight pruning
│ ├── federation/
│ │ ├── protocol.rs # Multi-chip communication
│ │ ├── pipeline.rs # Pipeline parallelism
│ │ └── speculative.rs # Draft-verify decoding
│ └── ruvector/
│ ├── micro_hnsw.rs # Vector index
│ ├── semantic_memory.rs # Context-aware memory
│ ├── rag.rs # Retrieval-augmented gen
│ └── anomaly.rs # Outlier detection
├── npm/ # npx package
│ ├── package.json
│ └── bin/
│ ├── cli.js # CLI implementation
│ └── postinstall.js # Setup script
├── .github/workflows/
│ └── release.yml # Automated builds
├── install.sh # Linux/macOS installer
├── install.ps1 # Windows installer
├── Makefile # Make targets
└── Dockerfile # Docker build
---
Troubleshooting
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Linux:
bash
sudo usermod -a -G dialout $USER
Log out and back in
`Windows: Run PowerShell as Administrator.
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1. Hold BOOT button while clicking flash
2. Check correct COM port in Device Manager
3. Use a data USB cable (not charge-only)
4. Close other serial monitors
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bash
Re-run toolchain setup
espup install
source ~/export-esp.sh # Linux/macOS
Restart terminal on Windows
`$3
Edit
:
`toml
ESP32 (default)
target = "xtensa-esp32-espidf"ESP32-S3 (recommended)
target = "xtensa-esp32s3-espidf"ESP32-C3/C6 (RISC-V)
target = "riscv32imc-esp-espidf"
`---
Feature Flags
Build with specific features:
bash
Default (ESP32)
cargo build --releaseESP32-S3 with federation
cargo build --release --features federationAll features
cargo build --release --features fullHost testing (no hardware needed)
cargo build --features host-test --no-default-featuresWebAssembly
cargo build --target wasm32-unknown-unknown --features wasm --no-default-features
`---
API Usage (Library)
Use as a Rust library:
rust
use ruvllm_esp32::prelude::*;// Vector search
let config = HNSWConfig::default();
let mut index: MicroHNSW<64, 256> = MicroHNSW::new(config);
index.insert(&vector)?;
let results = index.search(&query, 5);
// RAG
let mut rag: MicroRAG<64, 64> = MicroRAG::new(RAGConfig::default());
rag.add_knowledge("The sky is blue", &embedding)?;
let results = rag.retrieve(&query_embedding, 3);
// Semantic memory
let mut memory: SemanticMemory<64, 32> = SemanticMemory::new();
memory.add_memory(&embedding, &tokens, MemoryType::Factual)?;
// Anomaly detection
let mut detector = AnomalyDetector::new(AnomalyConfig::default());
let result = detector.check(&embedding);
if result.is_anomaly {
println!("Anomaly detected!");
}
// Binary quantization
let binary = BinaryVector::from_f32(&float_vector);
let distance = hamming_distance(&a, &b);
// Product quantization
let pq = ProductQuantizer::new(PQConfig { dim: 64, num_subspaces: 8, num_centroids: 16 });
let code = pq.encode(&vector)?;
`---
Installation Options
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bash
Use directly with npx (no install needed)
npx ruvllm-esp32 install
npx ruvllm-esp32 build --target esp32s3
npx ruvllm-esp32 flashOr install globally
npm install -g ruvllm-esp32
ruvllm-esp32 --help
`$3
Add to your
:`toml
[dependencies]
ruvllm-esp32 = "0.2"
`The library crate is available at crates.io/crates/ruvllm-esp32.
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This directory contains a complete, ready-to-flash project with all features:
bash
git clone https://github.com/ruvnet/ruvector
cd ruvector/examples/ruvLLM/esp32-flash
cargo build --release
``---
MIT
---
- Main Repository
- Rust Library (crates.io)
- npm CLI Tool
- Documentation
- Issue Tracker
---
ESP32 LLM, Tiny LLM, Embedded AI, Microcontroller AI, Edge AI, ESP32 Machine Learning, ESP32 Neural Network, INT8 Quantization, Binary Quantization, Product Quantization, HNSW Vector Search, RAG Embedded, Retrieval Augmented Generation ESP32, Semantic Memory, Anomaly Detection, Speculative Decoding, Multi-chip AI, Pipeline Parallelism, MicroLoRA, On-device Learning, IoT AI, ESP32-S3 SIMD, Xtensa AI, RISC-V AI, Offline AI, Privacy-preserving AI