8bit.tr Journal
On-Device LLM Deployment: Quantization, Latency, and Privacy
A practical guide to deploying LLMs on-device with quantization, memory limits, and privacy trade-offs.
Why On-Device Matters
On-device models reduce latency and protect privacy.
They also reduce cloud costs for high-volume applications.
Quantization and Memory
Use 4-bit or 8-bit quantization to fit models in device memory.
Quantization must be tested for quality regressions.
Latency and Power Trade-Offs
Mobile hardware introduces power constraints.
Batching and caching can help, but battery impact must be measured.
Privacy and Security
On-device inference keeps sensitive data local.
Still enforce secure storage and model integrity checks.
Deployment Strategies
Use hybrid routing: on-device for simple tasks, cloud for complex ones.
Provide a fallback when device resources are insufficient.
Device Constraints
Profile memory usage per model layer to avoid crashes.
Measure cold start times on low-end devices.
Tune batch sizes for thermal limits and sustained performance.
Use model pruning to fit strict RAM budgets.
Store models in compressed formats to reduce download size.
Detect device capabilities to select the right model variant.
Throttle inference under high temperature conditions.
Test across OS versions to ensure consistent performance.
Benchmark token throughput on common hardware tiers.
Limit background activity so inference stays responsive.
Track battery drain per request to set usage limits.
Optimize caching to avoid storage bloat on low-end devices.
Use lazy loading to reduce initial startup time.
Validate performance after OS updates to catch regressions.
Test offline modes to ensure critical paths remain functional.
Monitor memory fragmentation during long sessions.
Provide lightweight models for entry-level devices.
Tune thread counts to balance performance and power usage.
Privacy and Updates
Sign model binaries to prevent tampering.
Use secure storage for on-device caches and memory.
Provide opt-out controls for local logging.
Ship updates with delta patches to reduce bandwidth.
Validate model integrity after download and before execution.
Rotate model versions gradually to avoid sudden regressions.
Log crash reports without exposing sensitive content.
Offer offline modes with clear capability limits.
FAQ: On-Device LLMs
Is on-device always cheaper? Not if you need large models.
What is the biggest risk? Performance variability across devices.
What is the quickest win? Start with small, task-specific models.
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