Local Inference

Limited by the computing power and memory of consumer-grade devices, the scale of models that can be run locally is typically far smaller than cloud-based flagship models, with gaps in complex reasoning, long-context processing, and other tasks.

Running large models locally means all data never leaves the user's device, offering a privacy advantage.

The two core advantages of local inference are privacy and zero marginal cost, at the price of certain hardware requirements and model parameter scale and knowledge coverage that are often inferior to cloud-based large models.

Command execution latency on a local area network can be as low as under 5 milliseconds, far superior to the 50–300 millisecond round-trip latency of cloud-based solutions.

For the 4-bit quantized version of a 7B parameter model, at least 8GB of memory and a modern multi-core CPU are typically required for smooth operation, with inference speeds of approximately 10–30 tokens per second

Local Inference means an AI model runs directly on the user's device to perform computation, rather than sending requests to a remote server.

The core appeal of local AI lies in privacy, low latency, and offline availability.

The trade-off of local processing is that processing speed is limited by the CPU/GPU performance of the user's local device, and complex or high-resolution projects may face performance bottlenecks.

The essential difference between local AI inference and cloud inference lies in where the computation occurs: cloud inference requires sending prompts and context data to a remote server for computation by the provider's GPUs, while local inference downloads the model weights to the user's device and performs the forward pass on-device.

Local deployment can compress AI response latency from hundreds of milliseconds to single-digit milliseconds, while preventing user data from leaving the device.

The bottleneck in running large models locally lies in VRAM capacity and bandwidth; the CPU is not the critical factor

Locally deployed models download AI model weights to the user's hardware for execution, with inference completed locally so that data does not need to pass through external servers.

模型压缩涵盖量化、剪枝、知识蒸馏三条主要技术路径

Local inference means the model or algorithm runs directly on the user's device without sending requests to the cloud.

Reality is more likely to be a hybrid model: for scenarios that are latency-sensitive, have high privacy requirements, and involve fixed tasks, local inference has irreplaceable value; for scenarios that require frontier capabilities, elastic scaling, and large-scale concurrency, the cloud remains the more economical choice.

OpenAI CEO Sam Altman said: 'Local inference is a terrible allocation of scarce resources.'

If Proton chooses the local path, it must weigh functionality against device compatibility; if it chooses a trusted cloud environment, it must prove to users that it cannot peek.

Pure local inference offers the best privacy but places high demands on a Mac's compute power and memory.

The speed bottleneck of local LLM inference is often memory bandwidth rather than raw compute, because generating each token requires passing all weights through once.

Local inference latency mainly consists of three parts: prompt preprocessing (prefill), token-by-token generation (decode), and model loading; as output length increases, the time rises linearly.