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Apple and LM Studio Collaborate to Deploy Massive Local Large Language Models via Four Mac Studios
At the recently held WWDC2026 event, LM Studio and Apple presented a groundbreaking technical demonstration. During this showcase, they successfully ran Moonshot’s 10-trillion parameter large model, Kimi K2.6, using just four Mac Studios connected in a cluster. This achievement overturned the long-held assumption that models with trillions of parameters necessarily require cloud-based GPU clusters, proving that consumer-grade hardware can indeed deliver advanced AI computing capabilities.
Kimi K2.6 features a total parameter count of 1 trillion, employing a MoE architecture that activates 32 billion parameters at any given time. It supports long context handling, multimodal input processing, and agent-based task execution. For the demonstration, four Mac Studios were linked together through Apple’s memory sharing and interconnection technologies to create a cluster with approximately 1.5TB of unified memory, sufficient to support the inference needs of this large model. Earlier developer tests showed that under similar setup conditions, Kimi K2.6 could generate text at around 28 tokens per second while using significantly less power than traditional GPU-based solutions.
When connected directly from an iPhone to the local cluster, all data remains entirely within the user’s device
An additional highlight of the demonstration was LM Studio’s LM Link remote access feature. Users can securely connect remotely to the Mac Studio cluster from their MacBook Neo or iPhone, interact in real time with the running model, and all data processing as well as communication occur locally without any transmission over the cloud.
LM Link has been integrated into both LM Studio’s Mac application and Locally AI’s iOS app, offering end-to-end encrypted connections. This setup enables users to access cluster-level AI computing power at any time, even on lower-power devices, without concern about privacy breaches. Combined with Apple’s Thunderbolt 5 RDMA technology and other multi-device memory sharing solutions, this entire ecosystem is rapidly establishing a seamless framework for local AI deployment.
This collaboration sends a clear message: deploying trillion-parameter large models locally is no longer a theoretical concept confined to labs. It is now becoming an achievable solution that developers can implement in their own workspaces. As Apple continues to advance its hardware connectivity features, the capabilities of consumer devices for handling large-scale AI inference are expected to expand even further.
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Mac Studio 4台で10兆パラメータのKimi K2.6が動くなんて、ローカルLLMの未来は本当に近づいてきたな。AppleとLM Studioの連携は素晴らしいけど、正直なところ、個人が4台も揃えるのは現実的じゃないよね。でも、開発者向けには革命的な進歩だと思う。セキュリティ面での安心感も大きいし、今後の普及に期待大！🚀💻 #WWDC2026 #LocalLLM
At the recently held WWDC2026 event, LM Studio and Apple presented a groundbreaking technical demonstration. During this showcase, they successfully ran Moonshot’s 10-trillion parameter large model, Kimi K2.6, using just four Mac Studios connected in a cluster. This achievement overturned the long-held assumption that models with trillions of parameters necessarily require cloud-based GPU clusters, proving that consumer-grade hardware can indeed deliver advanced AI computing capabilities.
Kimi K2.6 features a total parameter count of 1 trillion, employing a MoE architecture that activates 32 billion parameters at any given time. It supports long context handling, multimodal input processing, and agent-based task execution. For the demonstration, four Mac Studios were linked together through Apple’s memory sharing and interconnection technologies to create a cluster with approximately 1.5TB of unified memory, sufficient to support the inference needs of this large model. Earlier developer tests showed that under similar setup conditions, Kimi K2.6 could generate text at around 28 tokens per second while using significantly less power than traditional GPU-based solutions.
When connected directly from an iPhone to the local cluster, all data remains entirely within the user’s device
An additional highlight of the demonstration was LM Studio’s LM Link remote access feature. Users can securely connect remotely to the Mac Studio cluster from their MacBook Neo or iPhone, interact in real time with the running model, and all data processing as well as communication occur locally without any transmission over the cloud.
LM Link has been integrated into both LM Studio’s Mac application and Locally AI’s iOS app, offering end-to-end encrypted connections. This setup enables users to access cluster-level AI computing power at any time, even on lower-power devices, without concern about privacy breaches. Combined with Apple’s Thunderbolt 5 RDMA technology and other multi-device memory sharing solutions, this entire ecosystem is rapidly establishing a seamless framework for local AI deployment.
This collaboration sends a clear message: deploying trillion-parameter large models locally is no longer a theoretical concept confined to labs. It is now becoming an achievable solution that developers can implement in their own workspaces. As Apple continues to advance its hardware connectivity features, the capabilities of consumer devices for handling large-scale AI inference are expected to expand even further.
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Mac Studio 4台で10兆パラメータのKimi K2.6が動くなんて、ローカルLLMの未来は本当に近づいてきたな。AppleとLM Studioの連携は素晴らしいけど、正直なところ、個人が4台も揃えるのは現実的じゃないよね。でも、開発者向けには革命的な進歩だと思う。セキュリティ面での安心感も大きいし、今後の普及に期待大！🚀💻 #WWDC2026 #LocalLLM
