Mac Studio M5 Ultra: Run 671B Models Locally and Build Your Own AI Infrastructure with OpenClaw
The first consumer hardware that fits DeepSeek R1's full 671B parameters in memory, and what you can actually do with it.
The Mac Studio M5 Ultra with 512GB unified memory is the first consumer-grade machine that can run DeepSeek R1 671B (the largest open-source model) entirely in RAM. No offloading, no multi-GPU rigs, no water cooling. Just a box that sits on your desk and draws less power than a hair dryer.
This changes the math on local AI. When you can run frontier-class models at home, the question shifts from "can I?" to "should I?" For a growing number of developers, the answer is yes.
Below: what the M5 Ultra delivers for LLM inference, how to pair it with OpenClaw for a 24/7 personal AI assistant, and when it makes financial sense versus cloud APIs.
What the M5 Ultra Brings to the Table
The M5 Ultra is two M5 Max chips fused via Apple's UltraFusion interconnect. Here's what matters for LLM inference:
| Spec | M3 Ultra | M5 Ultra (projected) | Why it matters |
|---|---|---|---|
| Memory bandwidth | 819 GB/s | ~1,100โ1,400 GB/s | Token generation speed is bandwidth-bound |
| Unified memory | Up to 512GB | Up to 512GB+ | Determines max model size |
| GPU cores | 80 | ~80 | Parallel compute for prefill |
| Neural Accelerator | None | Per-GPU-core | 3โ4x faster first-token latency |
| Process node | 3nm | 3nm (N3P) | Better perf/watt |
| TDP | ~200W | ~190W | Runs silent, 24/7 capable |
The single biggest improvement for AI workloads: the M5 embeds a Neural Accelerator inside every GPU core. Apple's own MLX benchmarks show 3.3โ4.1x faster time-to-first-token (TTFT) compared to M4. Token generation improves ~25%, still bandwidth-bound, but the bandwidth ceiling is higher.
For agent workloads that involve frequent context switches and long system prompts, this matters most. An M3 Ultra takes ~2.3 seconds to process a 120K-token context (estimated from prefill benchmarks); the M5 Ultra should do it in under 0.7 seconds.
What Can 512GB of Unified Memory Actually Run?
This is the table that matters. Unified memory means the GPU and CPU share the same RAM, no PCIe bottleneck, no VRAM limits.
| Model | Quantization | Memory needed | M3 Ultra 512GB | M5 Ultra (projected) |
|---|---|---|---|---|
| DeepSeek R1 671B (MoE) | Q4 | ~336 GB | 17โ20 tok/s | ~25โ35 tok/s |
| Llama 3.1 405B | Q4 | ~203 GB | ~2 tok/s | ~3โ5 tok/s |
| Qwen3-VL 235B | Q4 | ~118 GB | ~30 tok/s | ~40โ55 tok/s |
| GLM-4.7 358B | Q3 | ~180 GB | ~15 tok/s | ~20โ28 tok/s |
| Qwen3 30B (MoE) | 4-bit | ~17 GB | ~45 tok/s | ~60+ tok/s |
| Mistral Small 24B | BF16 | ~48 GB | 95 tok/s | ~130+ tok/s |
Sources: geerlingguy/ai-benchmarks, Apple MLX Research, HN community benchmarks
For context: 20โ30 tok/s is comfortable for interactive chat. 15 tok/s is usable. Below 5 tok/s feels sluggish but works for batch tasks.
The 512GB configuration means you can run DeepSeek R1 671B Q4 (~336GB) and still have ~176GB left for KV cache and context. That's enough for multi-turn conversations with 100K+ token contexts.
Why Not Just Use NVIDIA?
| Mac Studio M5 Ultra | NVIDIA RTX 5090 | 4x RTX 5090 | |
|---|---|---|---|
| Memory | 512GB unified | 32GB VRAM | 128GB VRAM |
| Bandwidth | ~1,200 GB/s | 1,792 GB/s | 7,168 GB/s |
| DeepSeek R1 671B | โ Runs in memory | โ Doesn't fit | โ Still doesn't fit |
| Llama 70B speed | ~18 tok/s | ~80 tok/s | ~240 tok/s |
| Power draw | ~190W | ~450W | ~1,800W |
| Noise | Silent | Loud | Data center |
| Price | ~$10,000 | ~$2,000 | ~$8,000 + motherboard |
NVIDIA wins on raw speed when the model fits in VRAM. But the moment a model exceeds 32GB, NVIDIA falls off a cliff: offloading to system RAM drops throughput from 100+ tok/s to ~3 tok/s. The Mac's unified memory architecture means there's no cliff. A 400GB model runs at the same bandwidth as a 40GB model.
For models under 70B, buy a GPU. For models over 200B, the Mac Studio is currently the only practical consumer option.
Enter OpenClaw: Turning Hardware into an AI Assistant
Running a model locally is step one. Making it useful 24/7 is step two.
OpenClaw is an open-source, self-hosted AI agent platform. It turns your Mac into a persistent AI assistant that you interact with through your existing messaging apps โ Telegram, Slack, Discord, WhatsApp, even iMessage.
Why OpenClaw + Mac Studio?
Most people interact with AI through a browser tab. OpenClaw puts it in your messaging app instead: your assistant runs on your hardware, remembers your context across conversations, and works while you sleep.
What OpenClaw Does
- Persistent memory: Markdown-based memory files with semantic search. Your assistant remembers what you discussed last week.
- Multi-channel inbox: Talk to it via Telegram, Slack, Discord, WhatsApp, or any supported platform. Same context, any device.
- Autonomous tasks: Schedule cron jobs, set up webhooks, let it work overnight on research or code tasks.
- Browser automation: CDP-based web browsing for research, data extraction, form filling.
- Skills ecosystem: Install community skills from ClawHub, or write your own.
- MCP server support: Connect to external tools and APIs.
The Local Model Advantage
When you run OpenClaw on a Mac Studio with local models via Ollama or MLX:
- Zero API costs. No per-token billing. Run DeepSeek R1 671B all day, every day, for the cost of electricity (~$3/month).
- Complete privacy. Your prompts, documents, and code never leave your machine. Process sensitive contracts, proprietary code, medical records, no third-party data processing.
- No rate limits. Cloud APIs throttle you at 1,000โ10,000 requests/minute. Local inference has no limits beyond your hardware.
- No downtime dependency. OpenAI goes down? Anthropic has an outage? Your local setup keeps running.
- Latency. No network round-trip. First token appears in milliseconds for small models.
Quick Setup: Mac Studio + Ollama + OpenClaw
# 1. Install Ollama
brew install ollama
# 2. Pull a model (start with something fast)
ollama pull qwen3:30b
# 3. Install OpenClaw
npm install -g openclaw@latest
openclaw onboard --install-daemon
# 4. Configure OpenClaw to use local Ollama
# In ~/.openclaw/openclaw.json, set:
# "defaultModel": "ollama/qwen3:30b"
# "providers": [{ "type": "ollama", "baseUrl": "http://127.0.0.1:11434" }]
OpenClaw runs as a launchd service on macOS. It starts on boot and runs 24/7 in the background. Connect your Telegram or Slack, and you have a persistent AI assistant that's always available.
For the M5 Ultra with 512GB, you can go bigger:
# Pull DeepSeek R1 671B (requires ~336GB RAM)
ollama pull deepseek-r1:671b-q4
# Or the excellent Qwen3-VL 235B for multimodal tasks
ollama pull qwen3-vl:235b-q4
The Economics: When Does Local Beat Cloud?
Let's do the math.
Cloud API costs (heavy user)
| Usage pattern | Monthly cost |
|---|---|
| OpenClaw with Claude Sonnet 4.6 (heavy) | $200โ400/month |
| Development + coding assistant | $50โ100/month |
| Research + document analysis | $50โ100/month |
| Total | $300โ600/month |
Mac Studio M5 Ultra (one-time + running)
| Item | Cost |
|---|---|
| Mac Studio M5 Ultra 512GB (projected) | ~$10,000 |
| Electricity (~200W, 24/7) | ~$3/month |
| Internet (already have it) | $0 |
| Break-even vs $400/month cloud | ~25 months |
After 25 months, you're running frontier-class AI for $3/month. And you still have a $10,000 workstation for everything else.
The Hybrid Approach (Recommended)
You don't have to go all-local or all-cloud. The smartest setup:
- Local models for high-volume, privacy-sensitive, or latency-critical tasks (coding, document analysis, brainstorming)
- Cloud APIs for frontier capabilities you can't run locally (GPT-5, Claude Opus 4.6 with 200K context at full speed)
OpenClaw supports this natively: configure multiple model providers and switch between local Ollama and cloud APIs per conversation or per task.
And for cloud API access, LemonData gives you 300+ models through a single API key with pay-as-you-go pricing, no subscriptions, no minimums. Use it as your cloud fallback when local models aren't enough.
Configuration Guide: Three Tiers
Tier 1: The Starter ($4,000โ5,000)
Mac Studio M3/M5 Ultra 96GB
- Runs: Qwen3 30B, Llama 70B (Q4), DeepSeek R1 14B
- Speed: 30โ50 tok/s on 30B models
- Best for: Personal assistant, coding help, light research
- OpenClaw config:
qwen3:30bas default, cloud fallback for complex tasks
Tier 2: The Power User ($7,000โ9,000)
Mac Studio M5 Ultra 256GB
- Runs: Qwen3-VL 235B, GLM-4.7 358B (Q3), Llama 405B (Q4)
- Speed: 15โ30 tok/s on 200B+ models
- Best for: Professional development, multimodal tasks, team AI server
- OpenClaw config:
qwen3-vl:235bfor vision,deepseek-r1:70bfor reasoning
Tier 3: The AI Workstation ($10,000โ14,000)
Mac Studio M5 Ultra 512GB
- Runs: DeepSeek R1 671B (Q4), everything below
- Speed: 25โ35 tok/s on 671B
- Best for: Running the largest open-source models, multi-user server, research
- OpenClaw config:
deepseek-r1:671bfor deep reasoning, smaller models for quick tasks
Running It as a 24/7 AI Server
The Mac Studio is designed for always-on operation. Here's how to set it up as a headless AI server:
Power & Thermal
- 190W TDP means standard outlet, no special wiring
- Fanless at idle, whisper-quiet under load
- No thermal throttling in sustained workloads (Apple's thermal design handles it)
Remote Access
- SSH for terminal access
- Tailscale for secure remote access from anywhere
- OpenClaw's messaging integration means you don't need direct machine access. Just message your AI through Telegram.
Reliability
- macOS launchd auto-restarts OpenClaw if it crashes
- Ollama runs as a background service
- UPS recommended for power outages (the Mac Studio boots and resumes services automatically)
# Enable SSH
sudo systemsetup -setremotelogin on
# Install Tailscale for remote access
brew install tailscale
sudo tailscale up
# OpenClaw already runs as launchd service after onboarding
# Check status:
launchctl list | grep openclaw
What's Coming: The M5 Ultra Roadmap
The M5 Ultra Mac Studio is expected in the second half of 2026. Here's the timeline:
- March 4, 2026: Apple "Experience" event, M5 Pro/Max MacBook Pro expected
- H2 2026: Mac Studio with M5 Ultra
- Key improvements over M3 Ultra: GPU Neural Accelerators (3โ4x TTFT), higher memory bandwidth (~1.1โ1.4 TB/s), same or higher max memory
Should You Wait or Buy Now?
Buy the M3 Ultra 512GB now if:
- You need local AI inference today
- You're spending $300+/month on cloud APIs
- The 17โ20 tok/s on DeepSeek R1 671B is fast enough for your use case
Wait for M5 Ultra if:
- You can tolerate cloud APIs for 6โ9 more months
- You want the 3โ4x TTFT improvement (critical for agent workloads)
- You want to see actual benchmarks before committing $10K+
Either way, you can start with OpenClaw today using cloud APIs through LemonData. $1 free credit on signup, 300+ models, pay only for what you use. When your Mac Studio arrives, just point OpenClaw at your local Ollama instance and your costs drop to near zero.
TL;DR
| Cloud APIs | Mac Studio M5 Ultra + OpenClaw | |
|---|---|---|
| Max model size | Unlimited (provider handles it) | 671B Q4 (512GB config) |
| Monthly cost | $300โ600 (heavy use) | ~$3 electricity |
| Privacy | Data sent to third parties | Everything stays local |
| Latency | 200โ500ms network + inference | Inference only |
| Rate limits | Yes | No |
| Upfront cost | $0 | ~$10,000 |
| Break-even | โ | ~25 months |
The Mac Studio M5 Ultra is personal AI infrastructure. Pair it with OpenClaw, and you have a 24/7 AI assistant that runs frontier-class models, respects your privacy, and costs $3/month to operate.
The era of "local AI is a toy" is over. 512GB of unified memory at 1.2+ TB/s bandwidth means you can run models that rival cloud offerings. The only question is whether you're ready to own your AI stack.
Ready to start building your AI infrastructure? Try OpenClaw with LemonData: 300+ cloud models with $1 free credit. When your Mac Studio arrives, switch to local models with zero code changes.