SWAN on Apple Silicon: Running 400B Parameter Models on a Single Mac

A 400-billion parameter AI model. A single Mac Studio. 13 minutes of analysis. No GPU cluster required. SWAN makes this possible — and it changes everything about who can build with the world's most powerful AI models.

The Apple Silicon Advantage

Apple Silicon's unified memory architecture is quietly rewriting the rules of AI deployment. While NVIDIA's most powerful data centre GPUs max out at 80–192 GB of memory per card, a Mac Studio with an M3 Ultra ships with up to 512 GB of unified memory — shared seamlessly between the CPU and a 80-core GPU.

This matters enormously for large language models. A 400-billion parameter model in BF16 requires over 800 GB of memory — impossibly large for any single GPU. Traditional deployments spread the model across 4–8 GPUs with complex tensor parallelism, inter-GPU communication overhead, and considerable infrastructure cost.

But if you can compress that model intelligently — keeping precision where it matters and reducing it where it doesn't — it fits in a single Mac's unified memory. That's exactly what SWAN does.

What SWAN Delivers for Apple AI

SWAN (Statistical Weight Analysis for N-bit allocation) is a data-free, per-tensor mixed-precision quantization method built from the ground up to work on Apple Silicon. Here's what makes it uniquely suited to the Apple ecosystem:

13 Minutes from Download to Deployment

SWAN analyses every tensor in a 400B+ parameter model and produces an optimised quantization plan in under 13 minutes on a Mac Studio with an M3 Ultra. Compare this to calibration-based methods like GPTQ or AWQ, which require hours of forward passes through the model with representative data — data you may not have access to.

The pipeline is elegantly simple: load each safetensor shard, compute four sensitivity metrics per tensor, assign bit-widths, move on. Peak memory stays well below total model size because SWAN processes shards sequentially.

Native MLX Integration

SWAN's output is a JSON manifest mapping each tensor to its bit-width decision. This manifest feeds directly into Apple's MLX framework as a quantization predicate during model conversion. No custom kernels. No framework modifications. Just a clean integration with Apple's native AI toolkit.

The quantized model runs through mlx_lm like any other quantized model — but with smarter bit allocation under the hood.

400B Parameters on a Single Machine

Here are the numbers that matter:

A SWAN-quantized Qwen3.5-397B fits entirely within 240 GB of peak memory on a single Mac Studio with 512 GB unified memory. No GPU cluster. No cloud infrastructure. No inter-node communication latency.

Quality That Doesn't Compromise

The natural concern with aggressive quantization is quality loss. SWAN's results on Apple Silicon are remarkable:

These scores come from a model running at just 4.31 average bits per parameter — compressed to less than a quarter of its original size. The 96.0% on ARC-Challenge means near-perfect science reasoning from a model running on a desktop Mac.

In head-to-head perplexity comparison, SWAN outperforms uniform 4-bit quantization (4.283 vs 4.298 PPL) because it's smarter about which tensors need protection. It gives 8-bit precision to the 4.3% of tensors that are genuinely sensitive — attention projections, expert gates, and MTP layers — while safely compressing the remaining 95.2% to 4-bit.

Why This Matters for the Apple AI Ecosystem

Democratising Access to Frontier Models

Until now, running 400B+ parameter models required access to multi-GPU cloud instances costing $10–50+ per hour. SWAN on Apple Silicon puts these models on a machine that sits on your desk, runs silently, and costs a one-time hardware investment. For researchers, independent developers, and small teams, this is transformative.

Data Privacy by Default

Running the model locally means your data never leaves your machine. No API calls to cloud providers. No data residency concerns. No terms-of-service changes that could expose your proprietary data. For regulated industries — healthcare, finance, legal — this is not a nice-to-have; it's a requirement.

Zero Infrastructure Overhead

No Docker containers to manage. No Kubernetes clusters to maintain. No GPU driver compatibility to debug. No NCCL configuration for multi-GPU communication. The model loads through MLX and runs on the unified memory architecture that Apple Silicon was designed around.

Offline-Capable AI

A SWAN-quantized model on your Mac works without an internet connection. On a plane, in a secure facility, or during an internet outage — you still have access to a state-of-the-art 400B parameter model with 96% science reasoning accuracy.

The SWAN Advantage for MLX Developers

If you're building AI applications on Apple Silicon, SWAN slots directly into your existing workflow:

1. Analyse. Point SWAN at any safetensor model. 13 minutes for 400B parameters.
2. Convert. Feed the manifest to mlx_lm.convert with SWAN's bit-width decisions as the quantization predicate.
3. Deploy. Load and serve with mlx_lm. No configuration changes needed.

The entire pipeline requires Python 3.9, MLX, and PyTorch — standard tools in the Apple AI ecosystem. No GPU cluster provisioning. No calibration data procurement. No hour-long quantization runs.

What This Means for Apple's AI Future

Apple Silicon is already the most accessible high-memory platform for AI. SWAN removes the last major barrier: you no longer need specialised quantization infrastructure or calibration datasets to compress frontier models for this hardware.

As Apple continues to scale unified memory — and as the MLX ecosystem matures — the combination of intelligent mixed-precision quantization and Apple's hardware architecture creates a compelling alternative to traditional GPU-cluster AI deployment. For many use cases, it's not just competitive; it's superior.

SWAN is open source. Code and data at github.com/baa-ai/swan-quantization.