● Brand New · Factory Sealed · Ready to Ship
Mac Studio M3 Ultra
512GB Unified Memory
M3 Ultra · 24-Core CPU · 80-Core GPU
512GB Unified Memory · 1TB SSD
Maximum Apple Silicon Configuration
Direct from Apple, Still Sealed in Box
Memory Available vs. What Fits
■ model ■ second model ■ free
RTX 5090 — 32GB VRAM
cannot run any model below
Qwen3.5-397B 241GB · 271GB free
DeepSeek R1 404GB · 108GB free
Qwen3.5 + 70B model ~281GB · 231GB free
Qwen3.5 + Llama 4 Maverick ~461GB · 51GB free
The free memory isn't wasted — it feeds the context window. Every free gigabyte extends how much the model can read, reason across, and remember in a single conversation. It also means a second large model can load simultaneously, routing different tasks to different models without reloading.
New to Local AI?
Running AI locally means the model lives on your hardware — not a company's server. Nothing you type is sent anywhere. No subscription. No per-message cost. No one reading your conversations. Claude, ChatGPT, and similar tools are powerful, but every prompt you send touches a third-party server. This machine eliminates that entirely. You own the AI. It lives on your desk. It works offline. And with 512GB of memory, it runs models that rival the best AI tools available — privately, permanently, at zero marginal cost.
⚡ Published March 24, 2026
Google Research · Peer-Reviewed at ICLR 2026
TurboQuant: 6× Memory Compression With Zero Accuracy Loss
When an AI model thinks, it keeps a running memory of the conversation called the key-value cache. The longer the conversation, the more memory it eats. This is the invisible wall that limits every local AI setup — not the model itself, but how much room is left over for it to think.
Today, Google published TurboQuant — a peer-reviewed algorithm that compresses this conversational memory by 6× with zero loss in accuracy. The model produces identical outputs. No retraining required. No fine-tuning. It's a pure software optimization that makes existing hardware dramatically more capable.
This algorithm was published today. It has not yet been ported to Apple Silicon inference engines like MLX or llama.cpp. When it is — and the open-source community moves fast — here is what changes for this machine:
What TurboQuant Unlocks on 512GB
DeepSeek R1 (671B)
Currently loads at 4-bit with ~108GB free for context
Context capacity
Today: ~16K–32K tokens — enough for a long email thread
With TurboQuant: 108GB stretches to ~648GB effective — enough for 100K+ tokens, well into book-length reasoning
Speed at length
Today: Slows as context grows — attention reads dominate
With TurboQuant: ~5× less data per attention step, keeping the model responsive at longer context
Qwen3.5-397B
Currently runs at 35 tok/s with ~271GB free for context
Context capacity
Today: ~256K tokens (~400 pages) — already exceptional
With TurboQuant: 271GB becomes ~1.6TB effective — potentially 1M+ tokens, enough to ingest an entire codebase or legal filing at once
Multi-model use
With TurboQuant: Load two frontier-class models simultaneously, each with generous context — route coding tasks to one, analysis to another, without reloading
What Changes in Daily Use
→ Paste a 200-page contract into DeepSeek R1 and ask it to find every liability clause — today this requires chunking; with TurboQuant it fits in a single prompt
→ Run multi-hour coding sessions without the model losing earlier context — conversations stay coherent roughly 6× longer
→ Feed an entire codebase to the model and ask architectural questions — it sees everything at once, not fragments
→ Long conversations no longer slow to a crawl — compressed cache means less data moving through the memory bus per token
Why Apple Silicon Benefits Most
TurboQuant's compressed cache must be decompressed on the fly during inference. On discrete GPU systems like NVIDIA, data shuttles across a PCIe bus between CPU and GPU memory. On Apple's unified memory architecture, the GPU reads the compressed cache directly — no bus, no copy, no transfer penalty. The M3 Ultra's 800 GB/s memory bandwidth serves compressed data without any additional overhead. Unified memory was always the right architecture for local AI. TurboQuant makes it even more so.
Why This Matters Right Now
TurboQuant was published today. Most people — including most AI developers — haven't read it yet. When the open-source community ports this to MLX and llama.cpp, every 512GB Mac Studio in the world becomes dramatically more capable overnight. That's when demand for this hardware spikes. That's when remaining sealed units disappear from the market. Right now, you can buy one before any of that happens.
For the Technical Reader
TurboQuant combines two novel algorithms: PolarQuant (converts vectors to polar coordinates, eliminating per-block normalization constants) and QJL (a 1-bit Johnson-Lindenstrauss error corrector with zero memory overhead). Together they quantize the KV cache to 3 bits — provably near the theoretical lower bound — with no accuracy loss. Data-oblivious (no dataset-specific tuning). Negligible runtime overhead. Validated on Gemma and Mistral across LongBench, RULER, Needle-in-a-Haystack, ZeroSCROLLS, and L-Eval. 4-bit TurboQuant achieved up to 8× speedup in attention logit computation vs. FP32 on H100 GPUs. Formal mathematical proofs included in paper.
Verify It Yourself
Search: TurboQuant Google Research
Paper: arXiv 2504.19874
Privacy & Control
— Your source code never touches OpenAI, Anthropic, or Google's servers
— Legal documents and privileged communications stay privileged
— Business strategy, M&A analysis, and financial data never leave your machine
— Your prompts don't train anyone's next model
— No usage logs, no content filtering, no third-party access
— Works completely offline — on a plane, in a SCIF, anywhere
Economics
— One-time cost vs. perpetual API bills
— Unlimited tokens at zero marginal cost — no metering on batch jobs or overnight runs
— Fine-tune any model on your own data without sending it anywhere
— No subscription tiers, no rate limits, no surprise invoices
Beyond AI
— ProRes and ProRes RAW hardware-accelerated encode and decode — four ProRes engines
— Full dev environments, VMs, Docker clusters, and a frontier AI model — all simultaneously
— Multiple Mac Studios link via Thunderbolt 5 for distributed inference
The Alternative
Multi-GPU server configurations with comparable addressable memory require four or more datacenter GPUs. Four H200 cards alone list for $124,000–$128,000, before the server chassis, networking, or cooling infrastructure. They draw 2,000+ watts and require datacenter conditions. This machine draws 215 watts and sits on a desk.
Worldwide, the number of people who can run Qwen3.5-397B locally at interactive speeds is almost certainly under 10,000. Possibly well under. In a world of 8 billion people, that's 0.0001% of the global population.
A year ago this hardware didn't exist. Two years ago the model didn't exist. The 512GB configuration launched in March 2025. Qwen3.5-397B launched in February 2026. The window where this specific machine is the answer to this specific capability is maybe 18 months wide — and Apple already closed the door on buying a new one.
What this machine represents is frontier-class AI reasoning running completely privately on a box that sits on a desk, owned by one person, answerable to no one, accessible to no one else, logging nothing. That's a genuinely new thing in the world, and right now fewer than 10,000 people have it.
