DeepSeek on your phone: choose local or cloud

Using DeepSeek from a phone can mean running a model on the phone, using the phone as an interface to your computer, or calling a cloud service. These architectures have different hardware needs and data flows. Choose the one that fits your task before downloading a model.

Read the complete model name

As of September 27, 2026, the Ollama DeepSeek-V4.1-Flash page lists deepseek-v4.1-flash:cloud. That suffix matters: a command launched on your computer can still use remote inference. An endpoint on localhost is not proof of local computation.

The official DeepSeek model card describes a very large model. With mixture-of-experts architectures, parameters active for a token are not all the weights that must be stored. Do not estimate personal-computer requirements from active parameter count alone.

Choose around your main constraint

• Inference must remain on your machine: choose downloadable weights and a variant compatible with your engine and resources.
• A remote provider is acceptable: inspect its terms, limits and billing before choosing a cloud model.
• You only need phone access: retain inference where it already works and add a mobile interface.
• You need operation without a computer or network: that requires on-device inference; a relay to a remote agent does not meet the need.

A smaller or distilled model has its own tradeoffs. Do not present its results as those of a much larger model sharing part of its name. Compare answers on your actual tasks.

Estimate resources without false precision

For local inference, account for weight storage, context memory, runtime buffers and other applications. Quantization can reduce weights but does not remove every memory cost. The engine and hardware also determine RAM offload and GPU execution options.

Start with a short question and modest context. Measure initial loading, a second request and actual memory consumption, then increase context to your requirement. Without that test, a promised throughput or supposedly sufficient RAM figure would be speculative.

Add Pacerelle after inference works

If the model answers through Ollama, the mobile connection guide adds a Python agent between the conversation and API. Set OLLAMA_MODEL to the exact model name. For another provider API, adapt the request and error handling in your agent.

Encryption protects the Pacerelle path to the agent. If the agent then sends a question to a cloud API, that provider receives the request data. Make that decision explicit, especially for private documents.

Run a decision-oriented trial

Prepare a short question, a reasoning task with a verifiable answer and a representative document excerpt. Compare answer quality, latency, failures, observed cost and data destination. Also test provider unavailability or a stopped local model so that failures remain understandable on mobile.

Choose the setup that meets these criteria on your hardware and data. To begin with a more accessible local option, follow the progressive testing approach in the Gemma and Ollama guide.