kandiga 0.3.1

Run frontier MoE models on consumer hardware. 35B in 1.5GB RAM.

Kandiga

Giant models. Tiny memory.

Kandiga is an open-source MoE inference engine that uses Selective Expert Materialization (SEM) to run massive models on any Apple Silicon Mac. A 397B model that normally needs 224GB of RAM runs in 8GB. A 35B model runs in 2GB. No cloud, no API keys.

Supported Models

Model	Parameters	Active	Experts	Disk	Kandiga RAM	Min. Mac
Qwen3.5-35B-A3B	35B	3B	256	20 GB	~2 GB	8 GB
Qwen3.5-122B-A10B	122B	10B	256	70 GB	~4 GB	16 GB
Qwen3.5-397B-A17B	397B	17B	512	224 GB	~8 GB	24 GB

Without Kandiga, these models require their full disk size in RAM. With SEM, only the shared layers load to memory — expert weights stay on disk and are read on demand.

How it works

MoE models have hundreds of expert sub-networks per layer, but only activate a few per token. Kandiga exploits this sparsity:

1. Shared layers (attention, norms, embeddings) load to GPU memory
2. Expert weights stay on disk in packed binary files
3. Per token: the router selects which experts to activate (8 of 256)
4. CPU computes expert MLP with NEON-vectorized 4-bit dequant + GCD parallelism
5. GPU computes attention simultaneously via MLX (unified memory, zero copy)

Install

pip install kandiga

Requirements: macOS with Apple Silicon (M1/M2/M3/M4), Python 3.10+

Quick start

# One-time setup: download model + prepare expert files
kandiga setup

# Choose a different model
kandiga setup --model mlx-community/Qwen3.5-122B-A10B-4bit

# Interactive chat
kandiga chat

# Fast mode (K=4 experts instead of 8, ~2x speed)
kandiga chat --fast

# One-shot prompt
kandiga "What is the capital of France?"

# Start an OpenAI-compatible API server
kandiga serve

# Run benchmarks
kandiga bench

# Update to latest version
kandiga update

Performance

Measured on M4 Mac Mini (16GB), Qwen3.5-35B-A3B-4bit:

Mode	Experts	Speed	RAM	Quality
Quality (K=8)	8/256 per layer	~3.5 tok/s	~2 GB	Full
Fast (K=4)	4/256 per layer	~6.5 tok/s	~2 GB	Near-equal

KV Cache Compression (TurboQuant)

Every token generated grows a memory buffer called the KV cache. On a 16GB Mac, this normally limits conversations to ~4K tokens before RAM runs out.

Kandiga implements TurboQuant (based on Google Research) — a compression algorithm that shrinks the KV cache from 16-bit to 3-bit per element with only 4% quality loss:

1. PolarQuant — randomly rotates vectors to spread information, then quantizes to 3 bits
2. QJL — 1-bit error correction using the Johnson-Lindenstrauss transform

Context Length	Standard (float16)	Kandiga (3-bit)	Compression
4K tokens	16.8 MB	4.5 MB	3.8x
8K tokens	33.6 MB	8.9 MB	3.8x
16K tokens	67.1 MB	17.8 MB	3.8x
32K tokens	134 MB	35.3 MB	3.8x

• 96% cosine similarity — attention scores stay accurate
• Zero speed overhead — rotation cost is negligible vs expert I/O
• No configuration needed — compression is automatic

Architecture

User prompt
    |
    v
[Tokenizer + Chat Template]
    |
    v
[MLX Forward Pass]
    |
    +---> GPU: Attention + Norms + Router + Shared Expert + Blending
    |
    +---> CPU: Routed Expert MLP (NEON 4-bit dequant + GCD parallel)
    |         |
    |         +-- pread expert weights from SSD (OS page cache)
    |         +-- SwiGLU activation + down projection
    |
    v
[Token Output]

Both CPU and GPU operate on the same physical DRAM (Apple Silicon unified memory), so there is zero data transfer overhead between them.

API Server

Kandiga includes an OpenAI-compatible HTTP API:

kandiga serve --port 8340

import openai

client = openai.OpenAI(base_url="http://localhost:8340/v1", api_key="unused")
response = client.chat.completions.create(
    model="mlx-community/Qwen3.5-35B-A3B-4bit",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Development

git clone https://github.com/kantheon/kandiga.git
cd kandiga
pip install -e ".[serve]"
cd kandiga/metal && make && cd ../..
pytest tests/ -v

License

MIT — Built by Kantheon