Edge0-145B: Frontier AI Capabilities on Your Own Computer

Today, we're open-sourcing Edge0-145B and Edge0-120B , together with our unified inference framework and deployment tools. Edge0-145B is the world's first open-source on-device AI model with 145 billion parameters. It brings coding, reasoning, and agent capabilities that rival frontier cloud models to your own computer. On-device AI has long forced a tradeoff between model capability and what the hardware can run. Larger models demand more memory, bandwidth, and power; smaller models can struggle with complex work. Moving that work to the cloud adds model-provider fees, data-handling constraints, and a dependence on connectivity. This release addresses those constraints in four ways: The constraint What Edge0-145B makes possible Large models are difficult to run on personal hardware. A 145B model runs locally on a compatible laptop, with reported peak memory of 29GB and decoding above 80 tokens/s. Small on-device models can fall short on demanding tasks. Coding, reasoning, and instruction-following results are competitive with specific frontier cloud models, as the comparisons below show. A useful assistant needs to finish work, not just answer questions. The model plans, writes and runs code, debugs errors, and checks results visually for projects such as 3D scenes, games, hardware programs, and PCB designs. Cloud workflows bring recurring fees, sensitive-data restrictions, and network dependence. Local inference incurs no corresponding cloud-model token or API fees; data can stay on the computer, and core capabilities keep working offline. Hardware and electricity still cost money. We're releasing the full stack so developers can reproduce the results, evaluate or fine-tune the models, and integrate them into their own products. Get the model weights , explore the inference framework , or try the Edge0 App . How We Make 100B+ Parameter AI Run on a Personal Computer Edge0-145B Laptop Performance: Memory, Speed, and Power Edge0-145B makes a 145-billion-parameter model usable on a personal laptop. A useful local model has to fit in memory, process prompts promptly, generate fast enough to use, and keep power consumption under control. It also needs to sustain longer tasks. Requirement Reported Edge0-145B result What it enables Memory 29GB peak memory Runs on a compatible personal laptop, such as a MacBook Pro, without a server. Prompt processing 1,100+ tokens/s prefill Processes long prompts, including dozens of pages of code, before generation begins. Generation 80+ tokens/s decode Generates faster than a person reads. Power Approximately 120W typical power Sustains operation without throttling in the reported setup. Long tasks 128K–256K context Maintains essentially the same speed on long inputs, supporting extended work. Our earlier Edge0-35B phone release reported peak memory below 2.5GB. On an iPhone 18 Pro Max, Edge0-35B delivers 37+ tokens/s using approximately 2.5GB of memory. Edge0-145B has roughly four times as many parameters; its laptop comparison reports 80+ tokens/s and 28GB peak memory. These measurements apply to different models and hardware. On-device model scale over time. The chart covers 67 open models of at least 1B parameters from 14 labs, intended for phones, laptops, and AI PCs. Same-size base, instruct, and reasoning variants count once; each line tracks a lab's largest model, including releases before July 2025. Filled points mark new highs and hollow points other releases. The scale is logarithmic below 40B and linear above it. Apple is excluded because its weights are not released. Sources: vendor announcements and model cards. Why On-Device Models Have Stayed Small Memory limits kept most on-device models below 10B parameters before September 2026. Apple Intelligence's on-device foundation model had approximately 3B parameters, while Google's Gemini Nano came in 1.8B and 3.25B versions. Even a 7B model could consume approximately 4GB of memory. Larger models faced a memory wall. Model weights occupy storage and must be read during generation, competing for limited memory and bandwidth with the operating system and other apps. Even an aggressively compressed 35B model could need approximately 20GB of memory, far more than a phone could make available. Compute, bandwidth, and power created competing constraints. Larger models offered more capability, but strained memory capacity, generation speed, and battery life. Making models smaller helped them fit, often at the cost of capability. Privacy alone could not close that gap. Apple Intelligence protected user data, but Apple acknowledged that its on-device model was not designed for agentic applications requiring general world knowledge. The prevailing assumption was that on-device intelligence could not match the cloud. Under the Hood of Edge0: Four Layers of Model and Inference Design We designed Edge0-145B and its inference engine together to reduce the memory and data movement that make large models difficult to run locally. The design spans architecture, training, loading, and scheduling. Architecture: share large matrices across sparse experts. Sharing matrices substantially reduces the weights that must be stored and moved while keeping the expert count unchanged. Each group of 64 experts shares two large matrices, while each expert retains a small set of parameters that captures its differences. Previously, every expert stored a complete set of matrices. Of the model's 145B total parameters, only approximately 10B participate in computation on each inference step. Training: build for local execution from day one. We train the model in its on-device architecture from the outset, with deployment constraints built into its design. This avoids treating local deployment as a compression or pruning step after training a cloud model. Loading: pipeline GPU memory, system memory, and SSD access. Reading and computation overlap across three storage tiers, avoiding serial waits. Frequently used “hot experts” remain in GPU memory. An expert library of approximately 40GB sits in system memory, with experts brought into GPU memory as needed. Approximately 48GB of vocabulary data resides on the SSD and is read on demand. Scheduling: use the CPU and GPU together. The GPU computes cached experts while the CPU handles the remaining work in parallel. Speculative decoding verifies multiple candidate tokens at once, spreading the cost of reading weights across more output tokens. These choices support the reported 80+ tokens/s decode rate. Model and inference-engine design work together to make 100B+ parameter inference practical on personal computers. Source: internal testing. Together, these changes let parameter counts grow from tens of billions to more than a hundred billion without proportional increases in memory use, power consumption, and latency. What Edge0 Can Do: Anything From Chat to Complex Work on Your Computer Edge0-145B can carry a complex task through to a usable result. Its coding and reasoning capabilities support local workflows that combine planning, tool use, execution, debugging, and visual checks. The benchmarks establish its capabilities; the demonstrations show how it puts them to work. Coding and reasoning that rival frontier cloud models Edge0-145B is competitive with specific frontier cloud models across coding, reasoning, knowledge, and instruction following: Coding: It scores 64.6 on DeepSWE 1.1 , ahead of Qwen3.8-Flash-Next at 58.7 and DeepSeek-V4-Flash-0731 at 54.4. Reasoning and knowledge: It scores 90.4 on GPQA-Diamond , close to DeepSeek-V4-Flash-0731 at 90.8 and Claude-Opus-4.6 \(Max\) at 91.3. Instruction following: It scores 80.6 on IFBench , ahead of DeepSeek-V4-Flash-0731 at 79.2 and Claude-Opus-4.6 \(Max\) at 62.5. Its 86.52 on Terminal-Bench 2.1 also exceeds Qwen3.8-Flash-Next's 83.3, DeepSeek-V4-Flash-0731's 82.7, and Claude's 78.2. On AA-LCR, Edge0-145B scores 7