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Decision guide

Local LLM vs Cloud LLM for Personal Assistant

Local LLMs keep data on-device and cost nothing to run, but require hardware and lack capability. Cloud LLMs offer top performance but send data off-device and charge per token. The right choice depends on your threat model, hardware, and budget.

Install guidePrivacy checklist

Local LLM vs Cloud LLM for Personal Assistant

Practical notes for evaluating a fast-moving open-source AI assistant.

Practical, source-linked OpenHuman guidance

At a Glance

Use local LLMs for embeddings, summaries, and lightweight chat when privacy is paramount. Use cloud LLMs for reasoning, coding, vision, and voice when capability matters more than absolute privacy. Most users benefit from a hybrid setup.

  • Local wins on: privacy (data never leaves your machine), cost (no per-token charges), and latency (no network round-trip for small models).
  • Cloud wins on: capability (frontier models outperform local equivalents), convenience (no hardware setup), and features (vision, voice, tool use).
  • Best setup for most users: hybrid routing. Lightweight tasks local, heavy tasks cloud. OpenHuman does this automatically.
  • Minimum hardware for local: 8 GB RAM for small models (Gemma 3 1B, Llama 3 8B). 16 GB for medium models (Llama 3 70B quantized). 32 GB+ for large models or heavy concurrent use.
  • Minimum cost for cloud: varies by provider. OpenHuman's bundled subscription covers 30+ providers. Expect $0.50–5/month for light personal use.

Cost Comparison

Real numbers for 2026. Costs assume daily personal assistant use (moderate chat, occasional document summarization).

  • Local LLM — hardware cost: $0/month ongoing. One-time: $0 if you already have a machine with 8+ GB RAM. $800–2,000 for a dedicated mini-PC or GPU setup. Apple Silicon Macs (M1+) are excellent local LLM hosts with unified memory.
  • Local LLM — electricity: ~$2–5/month for a machine running 8 hours/day at 100W. Negligible for laptops.
  • Cloud LLM — OpenHuman bundled: subscription unlocks 30+ providers. Light use is effectively included. Heavy use scales with token consumption.
  • Cloud LLM — direct API: GPT-4o ~$0.005/1K input tokens, ~$0.015/1K output tokens. Claude 3.5 Sonnet ~$0.003/1K input, ~0.015/1K output. For daily personal use: $1–10/month depending on verbosity.
  • Hybrid — OpenHuman model routing: embeddings and summaries run locally (free). Heavy reasoning routes to cloud (~$2–5/month for typical use). This is the most cost-effective setup for most users.

Privacy Comparison

How your data flows in each setup.

  • Local LLM: your prompts, documents, and responses never leave your machine. The only network traffic is the initial model download. Even that can be avoided by manual download.
  • Cloud LLM: every prompt and response travels to the provider's servers. Most providers do not train on API data, but they do process and temporarily store it. Read each provider's privacy policy.
  • Hybrid (OpenHuman routing): embeddings and summary-tree building run locally. Chat and reasoning route to cloud. You control which workloads stay local via the local AI preset.
  • Local LLM with caveats: if your assistant integrates with cloud services (email, calendar), those services still see the API calls. Local inference does not make your Gmail queries private from Google.
  • Cloud LLM with caveats: even if the LLM provider does not train on your data, the fact that you queried about specific topics creates a usage pattern. This is metadata leakage, not content leakage.

Speed and Quality Comparison

Measured on real hardware in 2026.

  • Local small models (Gemma 3 1B, Llama 3 8B): ~20–50 tokens/second on Apple M2, ~10–20 tokens/second on Intel i7. Quality: adequate for summarization, classification, and simple Q&A. Struggles with complex reasoning and coding.
  • Local medium models (Llama 3 70B Q4): ~5–15 tokens/second on Apple M3 Max (36 GB), ~2–5 tokens/second on RTX 4090. Quality: comparable to GPT-3.5 for many tasks. Good for personal use.
  • Cloud frontier models (GPT-4o, Claude 3.5 Sonnet): ~50–100 tokens/second. Quality: state-of-the-art reasoning, coding, and instruction following. No local model matches this as of mid-2026.
  • Vision tasks: local vision models (LLaVA, BakLLaVA) work but are significantly slower and less capable than cloud vision APIs. If vision is important, cloud is strongly recommended.
  • Voice tasks: Whisper STT runs well locally (~real-time on CPU). ElevenLabs TTS requires cloud and offers superior quality.

Common Mistakes

Avoid these pitfalls when choosing between local and cloud.

  • Assuming local = completely private. If your assistant fetches Gmail, Google still sees those API calls. Local inference only protects the AI processing step.
  • Ignoring RAM requirements. A 70B model needs ~40 GB RAM for full precision, ~20 GB for Q4 quantization. Running out of RAM causes extreme slowdown or crashes.
  • Choosing cloud-only to avoid setup. Modern local LLM tools (Ollama, LM Studio) are one-command installs. The setup gap has closed significantly.
  • Expecting local models to match frontier quality. As of June 2026, no local model matches GPT-4o or Claude 3.5 Sonnet on reasoning and coding. Set expectations accordingly.
  • Not testing hybrid routing. Many users default to all-cloud or all-local without trying the optimal split. Test for a week and measure what works.
Can I switch between local and cloud later?

Yes. With OpenHuman, changing the local AI preset or model routing rules takes under a minute. Ollama models can be added or removed anytime. Cloud API keys can be swapped between providers. Start with one approach and evolve as your needs change.

Will local models catch up to cloud models?

The gap is narrowing for specific tasks but frontier cloud models still lead on reasoning, coding, and multi-modal capabilities. Local models are improving rapidly — Llama 3 70B approaches GPT-3.5 quality for many tasks. For absolute best performance, cloud remains the choice.

Do I need an NVIDIA GPU?

No. Ollama runs excellently on Apple Silicon (M1+) and modern CPUs. An NVIDIA GPU accelerates inference significantly for large models but is not required. For small models (8B parameters), CPU performance is adequate.

Which cloud provider is cheapest?

For light use, OpenHuman's bundled subscription is cost-effective because it covers 30+ providers. For direct API access, Groq offers the fastest inference at competitive prices. Local inference is free after hardware costs. The cheapest overall is a hybrid setup with local for common tasks and cloud for occasional complex queries.