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M4 Mac Mini AI Cluster Uses EXO Labs with Thunderbolt 5 interconnect (80Gbps) to run LLMs distributed across 4 M4 Pro Mac Minis. The cluster is small (iPhone for reference). It’s running Nemotron 70B at 8 tok/sec and scales to Llama 405B (benchmarks soon).

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Alex Cheema

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3,518,551 görüntüleme • 1 yıl önce •via X (Twitter)

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M4 Mac Mini AI cluster running Llama 3.3 70B Preliminary results show Gigabit ethernet switch (4 tok/sec) is slower than Thunderbolt-5 with EXO Labs Benchmarks including Thunderbolt-5 coming soonTM.
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M4 Mac Mini AI cluster running Llama 3.3 70B Preliminary results show Gigabit ethernet switch (4 tok/sec) is slower than Thunderbolt-5 with EXO Labs Benchmarks including Thunderbolt-5 coming soonTM.

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Running DeepSeek-V3 on M4 Mac Mini AI Cluster 671B MoE model distributed across 8 M4 Pro 64GB Mac Minis. Apple Silicon with unified memory is a great fit for MoE.
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Running DeepSeek-V3 on M4 Mac Mini AI Cluster 671B MoE model distributed across 8 M4 Pro 64GB Mac Minis. Apple Silicon with unified memory is a great fit for MoE.

EXO Labs

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Preparing to run the new Llama 3.3 70B on my mac cluster. Downloading model shards onto 3 x M4 Pro Mac Mini and 1 x M3 Max MacBook Pro. AI cluster is connected by Gigabit ethernet switch with EXO Labs
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Preparing to run the new Llama 3.3 70B on my mac cluster. Downloading model shards onto 3 x M4 Pro Mac Mini and 1 x M3 Max MacBook Pro. AI cluster is connected by Gigabit ethernet switch with EXO Labs

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Running DeepSeek R1 on my desk Uses EXO Labs with Thunderbolt 5 interconnect (80Gbps) to run the full (671B, 8-bit) DeepSeek R1 distributed across 2 M3 Ultra 512GB Mac Studios (1TB total Unified Memory). Runs at 11 tok/sec. Theoretical max is ~20 tok/sec.
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Running DeepSeek R1 on my desk Uses EXO Labs with Thunderbolt 5 interconnect (80Gbps) to run the full (671B, 8-bit) DeepSeek R1 distributed across 2 M3 Ultra 512GB Mac Studios (1TB total Unified Memory). Runs at 11 tok/sec. Theoretical max is ~20 tok/sec.

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AGI at home Running DeepSeek R1 across my 7 M4 Pro Mac Minis and 1 M4 Max MacBook Pro. Total unified memory = 496GB. Uses EXO Labs distributed inference with 4-bit quantization. Next goal is fp8 (requires >700GB)
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AGI at home Running DeepSeek R1 across my 7 M4 Pro Mac Minis and 1 M4 Max MacBook Pro. Total unified memory = 496GB. Uses EXO Labs distributed inference with 4-bit quantization. Next goal is fp8 (requires >700GB)

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How much faster is the new MacBook Pro for AI inference? M4 Max is 27% faster with 72 tok/sec compared to 56 tok/sec of the M3 Max with MLX running Gemma 2 9B (4bit). The 27% speedup is the same with Llama-3.2-1b, Llama-3.2-3b and others. Next up: EXO Labs M4 cluster.
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How much faster is the new MacBook Pro for AI inference? M4 Max is 27% faster with 72 tok/sec compared to 56 tok/sec of the M3 Max with MLX running Gemma 2 9B (4bit). The 27% speedup is the same with Llama-3.2-1b, Llama-3.2-3b and others. Next up: EXO Labs M4 cluster.

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Day 1: Benchmarks We ran 1000+ LLM benchmarks on real consumer devices. Data includes single-device and multi-device clusters with Tokens-Per-Second (TPS) and Time-To-First-Token (TTFT). Setups tested: 3x M4 Mac Mini cluster, iPhone 15 + S24, RTX4090 & more.
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Day 1: Benchmarks We ran 1000+ LLM benchmarks on real consumer devices. Data includes single-device and multi-device clusters with Tokens-Per-Second (TPS) and Time-To-First-Token (TTFT). Setups tested: 3x M4 Mac Mini cluster, iPhone 15 + S24, RTX4090 & more.

EXO Labs

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"It's on my todo to build my own stack of apple minis and run Llama on my own little cluster on my desk." - Garry Tan Quick AI homelab tutorial: - Install EXO Labs on each mini (open source, steps in README) - Make sure all the minis are on the same WiFi network or for faster/more reliable results, connected over Thunderbolt/Ethernet. Devices will auto-discover each other and auto-shard the LLMs. - Now you can chat to your cluster with your preferred LLM GUI (the tiny corp tinychat ships with exo)
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"It's on my todo to build my own stack of apple minis and run Llama on my own little cluster on my desk." - Garry Tan Quick AI homelab tutorial: - Install EXO Labs on each mini (open source, steps in README) - Make sure all the minis are on the same WiFi network or for faster/more reliable results, connected over Thunderbolt/Ethernet. Devices will auto-discover each other and auto-shard the LLMs. - Now you can chat to your cluster with your preferred LLM GUI (the tiny corp tinychat ships with exo)

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Distributed training on M4 Mac Mini cluster We implemented Google DeepMind DiLoCo on Apple Silicon to train large models with 100-1000x less bandwidth compared to DDP baseline. AI is entering a new era where a distributed network of consumer devices can train large models.
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Distributed training on M4 Mac Mini cluster We implemented Google DeepMind DiLoCo on Apple Silicon to train large models with 100-1000x less bandwidth compared to DDP baseline. AI is entering a new era where a distributed network of consumer devices can train large models.

EXO Labs

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EXO 1.0 at Apple NeurIPS booth. 4 x 512GB M3 Ultra Mac Studios clustered all-to-all with RDMA over Thunderbolt 5. Runs DeepSeek v3.2 (8-bit) at 25 tok/sec with tensor parallelism.
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EXO 1.0 at Apple NeurIPS booth. 4 x 512GB M3 Ultra Mac Studios clustered all-to-all with RDMA over Thunderbolt 5. Runs DeepSeek v3.2 (8-bit) at 25 tok/sec with tensor parallelism.

EXO Labs

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MARCUS CHEN STACKED 30 MAC MINIS INTO AN AI SERVER FARM. ONE $599 MAC MINI REPLACES YOUR $200/MONTH CLAUDE CODE BILL WITH $3 IN ELECTRICITY two months ago a developer posted his claude code bill on reddit. $170 in 10 days. someone replied "i bought a mac mini m4. haven't paid anthropic since." apple stores ran out of mac minis the same week the m4 chip has 120 gb/s memory bandwidth and unified memory architecture. cpu and gpu share one pool so the model loads once and both read from it. a $599 mac mini runs ai faster than a $1,500 windows pc with a discrete gpu since january 2026 ollama supports the anthropic messages api format. claude code connects directly to your local mac mini with one environment variable. same interface, zero api costs, $0 per request a heavy developer pays $459 a month across claude code max, chatgpt pro, gemini, cursor and copilot. that's $5,508 a year. the mac mini pays off in 3 months and runs on $3 in electricity after that uber rolled out claude code to 5,000 engineers and burned through their $3.4 billion 2026 ai budget in 4 months. the people who own the hardware in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
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MARCUS CHEN STACKED 30 MAC MINIS INTO AN AI SERVER FARM. ONE $599 MAC MINI REPLACES YOUR $200/MONTH CLAUDE CODE BILL WITH $3 IN ELECTRICITY two months ago a developer posted his claude code bill on reddit. $170 in 10 days. someone replied "i bought a mac mini m4. haven't paid anthropic since." apple stores ran out of mac minis the same week the m4 chip has 120 gb/s memory bandwidth and unified memory architecture. cpu and gpu share one pool so the model loads once and both read from it. a $599 mac mini runs ai faster than a $1,500 windows pc with a discrete gpu since january 2026 ollama supports the anthropic messages api format. claude code connects directly to your local mac mini with one environment variable. same interface, zero api costs, $0 per request a heavy developer pays $459 a month across claude code max, chatgpt pro, gemini, cursor and copilot. that's $5,508 a year. the mac mini pays off in 3 months and runs on $3 in electricity after that uber rolled out claude code to 5,000 engineers and burned through their $3.4 billion 2026 ai budget in 4 months. the people who own the hardware in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

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THIS $599 MAC MINI CAN DRIVE THREE DISPLAYS NATIVELY - THIS SETUP TOOK "MORE SCREEN SPACE" SO FAR IT ENDED WITH BINOCULARS. the M4 Mac mini shrinks a full desktop into a 5 x 5-inch box. the base model launched with: 10-core CPU 10-core GPU 16GB unified memory support for two 6K displays plus one 5K display the M4 Pro version can drive three 6K displays at 60Hz, pushing more than 60 million pixels at once. no DisplayLink software is required for the supported three-screen configuration. the video turns that spec into a monitor wall, then delivers the punchline when the creator reaches for binoculars. honest line: Apple guarantees up to three native displays. the clip does not prove every screen in the room is connected to this one Mac mini. the tiny computer solved the screen limit. the next bottleneck is finding the window you opened. bookmark & watch today ↓
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THIS $599 MAC MINI CAN DRIVE THREE DISPLAYS NATIVELY - THIS SETUP TOOK "MORE SCREEN SPACE" SO FAR IT ENDED WITH BINOCULARS. the M4 Mac mini shrinks a full desktop into a 5 x 5-inch box. the base model launched with: 10-core CPU 10-core GPU 16GB unified memory support for two 6K displays plus one 5K display the M4 Pro version can drive three 6K displays at 60Hz, pushing more than 60 million pixels at once. no DisplayLink software is required for the supported three-screen configuration. the video turns that spec into a monitor wall, then delivers the punchline when the creator reaches for binoculars. honest line: Apple guarantees up to three native displays. the clip does not prove every screen in the room is connected to this one Mac mini. the tiny computer solved the screen limit. the next bottleneck is finding the window you opened. bookmark & watch today ↓

Grimmer

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THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf
0:10

THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf

Fokki

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Apple just showcased the ultimate setup for running AI models locally. These are 4 M5 Ultra Mac Studios running Kimi K2.7 Code locally to fix a bug in a 3D rendered scene on XCode. Each has 256GB RAM, so thats 1TB combined memory. Enough to run the biggest open AI models locally like Llama 4, Deepseek v3 and Qwen 3. Perfect for agentic coding when the project codebase is sensitive and you wan to keep it private. This is easily a $50K setup!
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Apple just showcased the ultimate setup for running AI models locally. These are 4 M5 Ultra Mac Studios running Kimi K2.7 Code locally to fix a bug in a 3D rendered scene on XCode. Each has 256GB RAM, so thats 1TB combined memory. Enough to run the biggest open AI models locally like Llama 4, Deepseek v3 and Qwen 3. Perfect for agentic coding when the project codebase is sensitive and you wan to keep it private. This is easily a $50K setup!

Ishan Sharma

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A 27-year-old guy from China bought 100 Mac minis and turned his apartment into a server hub Instead of furniture, the space is filled with neat racks where a hundred Mac minis hum like a living organism, processing gigabytes of data in a continuous stream Many small AI startups in Asia cannot afford to train models on NVIDIA H100 servers: it is insanely expensive He provides his Mac minis for running and fine-tuning small, highly specialized models. He takes on the dirty work of data labeling and quality assurance by using his network of agents that check each other's work > His initial investment was $59,900 > He was able to recoup his costs in 3 months Right now, his average income is $25,000–$30,000 per month He is not a genius: he simply found a niche with high demand and created an offer
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A 27-year-old guy from China bought 100 Mac minis and turned his apartment into a server hub Instead of furniture, the space is filled with neat racks where a hundred Mac minis hum like a living organism, processing gigabytes of data in a continuous stream Many small AI startups in Asia cannot afford to train models on NVIDIA H100 servers: it is insanely expensive He provides his Mac minis for running and fine-tuning small, highly specialized models. He takes on the dirty work of data labeling and quality assurance by using his network of agents that check each other's work > His initial investment was $59,900 > He was able to recoup his costs in 3 months Right now, his average income is $25,000–$30,000 per month He is not a genius: he simply found a niche with high demand and created an offer

Bober_smart

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Inkling-small is out today! With SGLang, you can get 648 tok/s decode with DSpark (simulated acc len=4) and 288 tok/s w/o DSpark, under the same setup (8x NVIDIA AI B200, TP 8, NVFP4, bs=1). What makes this model different is the size. 276B total with 12B active is a sweet spot for RL, and both LoRA and full-parameter training become well within reach. Miles is ready and verified for multimodal RL on Inkling-small, so you can turn your multimodal data into real capability gains. At ~1/4 the size, Inkling-small matches the bigger version in capability and even wins on some benchmarks. Run Inkling-small with SGLang, and customize it with Miles.
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Inkling-small is out today! With SGLang, you can get 648 tok/s decode with DSpark (simulated acc len=4) and 288 tok/s w/o DSpark, under the same setup (8x NVIDIA AI B200, TP 8, NVFP4, bs=1). What makes this model different is the size. 276B total with 12B active is a sweet spot for RL, and both LoRA and full-parameter training become well within reach. Miles is ready and verified for multimodal RL on Inkling-small, so you can turn your multimodal data into real capability gains. At ~1/4 the size, Inkling-small matches the bigger version in capability and even wins on some benchmarks. Run Inkling-small with SGLang, and customize it with Miles.

LMSYS Org

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Exploring diff visuals with Gen2. I'm amazed at how it takes a single block image to build neon streets, structures, and entire cityscapes. Anybody else toying with AI tonight⁉️ AI Art Workflow: 1. Crafted starting image in #Midjourney 2. Generated a 4-sec video with #Gen2 (no text prompt) 3. Extended first 4 secs to 18 secs 4. Repeated steps 2-3 twice, each time using the final frame from the previous 18-sec video to start the next 18-secs run 5. Merged all three 18-sec videos in #FinalCut Pro 6. Adjusted speed for fluidity 7. Final version published here is a 54-sec journey distilled into a 16-sec video with 🎶 #AIart #AIArtCommunity
0:16

Exploring diff visuals with Gen2. I'm amazed at how it takes a single block image to build neon streets, structures, and entire cityscapes. Anybody else toying with AI tonight⁉️ AI Art Workflow: 1. Crafted starting image in #Midjourney 2. Generated a 4-sec video with #Gen2 (no text prompt) 3. Extended first 4 secs to 18 secs 4. Repeated steps 2-3 twice, each time using the final frame from the previous 18-sec video to start the next 18-secs run 5. Merged all three 18-sec videos in #FinalCut Pro 6. Adjusted speed for fluidity 7. Final version published here is a 54-sec journey distilled into a 16-sec video with 🎶 #AIart #AIArtCommunity

Dave Villalva

47,946 görüntüleme • 3 yıl önce

We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.
0:14

We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.

Victor M

55,281 görüntüleme • 2 ay önce