Mixture of Experts architecture delivers larger-model capability while activating only a fraction of the model\u2019s parameters per token. That makes it a strong fit for local AI, but the full model still has to remain accessible in memory. Here\u2019s why that matters and how to run larger MoE models without moving to a higher memory system.<\/p>\n<\/blockquote>\n
Not long ago, running an AI model on your own hardware meant settling for a capable but modest chatbot. It could answer questions and summarize text, but the serious work-reasoning, coding, agents-stayed in the cloud. <\/span>\u00a0<\/span><\/p>\n That\u2019s changing rapidly. Today\u2019s local models can think through multi-step problems, write and debug code, call external tools, understand images and documents, and drive autonomous agents that work on your behalf. The gap between what runs in a data center and what runs on a workstation is closing fast.<\/span>\u00a0<\/span><\/p>\n A big part of that shift is thanks to an architecture called Mixture of Experts, or MoE, which enables these more capable models to run on desktops and workstations with limited GPU resources by reducing the burden on compute.<\/span><\/p>\n <\/p>\n <\/p>\n Traditional models are dense, meaning every one of their parameters participates in generating every output token. Because the whole model works on every request, compute cost scales directly with model size. That\u2019s a tough equation for desktop systems, which rarely have data-center levels of GPU horsepower at their disposal.<\/span>\u00a0<\/span><\/p>\n MoE models take a different approach. Here, a larger model is divided into specialized sub-networks called experts. Each expert develops its own area of focus during training, learning different internal patterns and representations based on the data routed to it. A small routing network decides which experts should handle each token and only that subset activates. The rest of the model sits idle for that step.<\/span>\u00a0<\/span><\/p>\n The result is larger model capability without requiring every parameter to be computed every time. Put another way, an MoE model can carry the knowledge and skills of a big model while activating only a fraction of its parameters for each token. This is ideal for local AI, where compute power may be limited.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n <\/p>\n New MoE models show where this is heading. Take Google\u2019s Gemma 4 26B A4B, for instance. According to <\/span>Google\u2019s documentation<\/span><\/a>, the model carries 26 billion total parameters but activates only 4 billion per token, giving it a much lower per-token compute burden than a dense model of similar total size. Google positions it for consumer GPUs and workstations, with a built-in reasoning mode, a context window up to 256K tokens, image understanding, stronger coding performance, and native function calling (the ability to invoke outside tools and apps) to power autonomous agents.<\/span>\u00a0<\/span><\/p>\n In other words, capabilities that used to require a cloud API are now packaged for the desktop. And they map directly to what people actually want local AI to do: personal AI agents like OpenClaw and Hermes, coding assistants, private RAG over your own files, document analysis, research and planning tasks, and workflow automation.<\/span>\u00a0<\/span><\/p>\n There are many advantages to running these types of workloads locally. Your data stays on your hardware, which matters for privacy and compliance. Latency is predictable because nothing crosses the internet. There are no per-token API bills. And you stay in control of the model, the versions, and the stack.<\/span>\u00a0<\/span><\/p>\n <\/p>\n It would be easy to assume that a model activating only 4 billion parameters needs only 4 billion parameters\u2019 worth of memory. It doesn\u2019t.<\/span>\u00a0<\/span><\/p>\n The model\u2019s routing network chooses different experts for different tokens, so the full set of experts must be available at all times. Google\u2019s own <\/span>documentation<\/span><\/a> makes this plain: while Gemma 4 26B A4B activates just 4 billion parameters per token, all 26 billion must be loaded into memory to keep routing and inference fast.<\/span>\u00a0<\/span><\/p>\n So, while MoE reduces active compute, it doesn\u2019t shrink the memory footprint. In Phison\u2019s tested configuration, running Gemma 4 26B A4B without any memory offload required roughly 23.5GB of system memory-easily beyond the capabilities of a 16GB system. As MoE models keep growing in capability, they will keep growing in size, exceeding the GPU and system memory of the local machines they were meant to empower.<\/span>\u00a0<\/span><\/p>\n With MoE, the new bottleneck for local AI is memory.\u00a0<\/span><\/p>\n <\/p>\n <\/p>\n Pascari aiDAPTIV\u2122<\/a> is Phison\u2019s AI memory management technology. It extends usable AI memory across three tiers: GPU memory, system DRAM, and NVMe SSD-based aiDAPTIV Cache Memory.<\/span>\u00a0<\/span><\/p>\n Its Dynamic MoE capability applies that architecture to Mixture of Experts models directly. Instead of requiring every expert to live permanently in GPU or system memory, aiDAPTIV treats experts as dynamically cached units. The experts the routing network needs are kept in GPU memory for computation, while the rest can be cached in system DRAM or offloaded to aiDAPTIV Cache Memory and loaded on demand as the model\u2019s routing decisions change.<\/span>\u00a0<\/span><\/p>\n For example, running Gemma 4 26B A4B normally demands 23.GB of memory-15GB for model weights and 8.5GB for the KV cache, runtime, OS, and applications. With aiDAPTIV offloading a portion of the model to cache memory, system-memory use drops to 15.9GB. The offloaded experts now live on the SSD and are loaded only when the routing network calls for them. This brings a workload that wouldn\u2019t fit on a 16GB system now within reach.<\/span>\u00a0<\/span><\/p>\n There\u2019s an honest tradeoff to acknowledge, however. Moving experts between memory tiers can\u00a0 increase latency and reduce throughput. How much depends on the workload. Repetitive queries that reuse the same experts generally perform better, while tasks that jump between domains lean harder on the cache. However, for the workloads driving local AI today-agentic workflows, coding assistants, private RAG, document analysis, and research and planning tasks-what the model can do matters more than peak token speed. Phison\u2019s aiDAPTIV technology makes the more capable model possible on the hardware you already have.<\/span>\u00a0<\/span><\/p>\n <\/p>\n <\/p>\n <\/p>\n This article covers what Dynamic MoE does. Our tech team developed a whitepaper<\/a> that explains how it works and what to expect when you run it, including:<\/span>\u00a0<\/span><\/p>\n![\"\"](\"https:\/\/phisonblog.com\/wp-content\/uploads\/2026\/07\/964_1594174902.png\")
<\/a>Why MoE models are a natural fit for local AI<\/h3>\n
Built for the way AI is used now<\/h3>\n
The catch: MoE models aren\u2019t smaller<\/h3>\n
![\"\"](\"https:\/\/phisonblog.com\/wp-content\/uploads\/2026\/07\/964_2815185868.png\")
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<\/span><\/p>\nHow Pascari aiDAPTIV\u2122 helps MoE<\/h3>\n
![\"\"](\"https:\/\/phisonblog.com\/wp-content\/uploads\/2026\/07\/aiDAPTIVDynamicMoE_86ahj0641-1024x712.png\")
<\/a><\/p>\nGo deeper: how Dynamic MoE actually works<\/h3>\n