The Rise of On-Device AI: Why Foundry Local Is Poised to Redefine Automation
If you’ve felt the explosion of AI models and tooling, you’ve probably also felt the friction: too many choices, unclear performance on your hardware, unclear licensing, and a constant tug-of-war between privacy, cost, and speed. Foundry Local is tackling that head-on with an opinionated, hardware-accelerated approach to running AI models on your devices—CPU, GPU, and NPU—without sending your data anywhere.
Here’s the big idea: your company already bought machines with capable GPUs and NPUs. Put them to work.
- On-device models deliver privacy by default. Your data never leaves the machine.
- Offline is a feature, not a fallback. Mission-critical workflows keep running even when the network doesn’t.
- Cost control is immediate and obvious. Compute is “free” after the device purchase; Foundry Local itself is free to use.
- Performance is real, not theoretical. Foundry partners directly with AMD, NVIDIA, Qualcomm, and Intel (via OpenVINO) to publish hardware-optimized models.
The result is a practical foundation for automation that’s fast, secure, and composable across heterogeneous fleets—perfect for enterprises that care about data governance and for builders who want predictable performance on real hardware, not on someone’s benchmark lab.
Practical Multimodel Orchestration: CPU, GPU, and NPU Working Together
The most compelling demo isn’t a fancy benchmark—it’s watching three different models run concurrently across CPU, GPU, and NPU, all on a shared-memory device, all accelerated. That concurrency matters for real-world automation:
- Low-latency “quick answer” tasks (e.g., capital-of-France) on compact CPU models.
- Heavier reasoning or longer context on 7B GPU models when you need depth.
- Specialized NPU models for energy-efficient, sustained workloads and future mobile/edge deployments.
What this unlocks:
- Parallel workflows with no queuing headaches.
- Token-per-second that tracks with model size and hardware constraints.
- Flexibility to tune parameters (context window, temperature, quantization) per model to hit your latency/quality targets.
Even on modest machines, you can juggle multiple jobs and still get responsive experiences. That’s the automation developer’s dream: pair the right model with the right job at the right time—locally.
Hugging Face, Licensing, and Bring-Your-Own-Model Reality
Model availability is expanding fast. Foundry Local ships a curated set today, with active work to add more via direct collaboration with providers (e.g., Meta for Llama) and compatibility with Hugging Face ecosystems.
- You can bring your own models now; documentation covers the process.
- Publishing and seamless pulling from Hugging Face is in the works—resource constraints are real, but the intent is clear.
- License restrictions remain a practical constraint; curation ensures compliance.
Translation for automation teams: you won’t be blocked by provider lock-in. Your preferred model—and your domain-specific fine-tunes—can run on-device with acceleration, when licensing allows.
Dev Experience: GitHub Copilot, VS Code, and SDKs
Foundry Local isn’t just a CLI. It plugs into your workflow.
- VS Code AI Toolkit integration lets you manage local models and use them within GitHub Copilot for code edits and chat. It’s a fun (and sometimes free) way to offload routine tasks to local compute.
- REST endpoints and SDKs mean you can hit Foundry Local like any service, but without leaving the machine.
- Model management UX (download, retain flags, concurrent sessions) is designed for builder velocity.
Caveat: Copilot pipes a lot of context, and smaller local models may misinterpret domain specifics. For production automation, pick models intentionally and test with your real prompts and datasets.
Foundry Local vs. Cloud-Hosted: Choosing the Right Mode
Why choose Foundry Local over other hosted solutions? It boils down to data, downtime, and dollars.
- Privacy: Banks, tax firms, and regulated industries can keep sensitive data fully local. No data egress means simpler compliance and fewer attack surfaces.
- Offline-first: Field operations, edge sites, or remote scenarios (hikes, labs, disaster recovery) keep working when the network doesn’t. This is resilience as a feature.
- Cost and experimentation: Fine-tune locally, validate utility, and scale hosted only when needed. Foundry Local is free; the “cost” is your device and electricity.
Hosted still has a place—especially for centralized scaling, multi-tenant needs, or managed SLAs. The thought-leader takeaway: treat local and hosted as complementary modes. Build automation that flows across both.
Performance, Tokens, and Reality Checks
Benchmarks are fickle; your workload is what matters. Still, teams are seeing:
- Faster tokens-per-second and load times on larger models and wider context windows when using Foundry’s hardware-optimized builds.
- Noticeably better experience when pairing model size to task complexity (e.g., 0.5–1.5B for utility; 7B for depth).
- NPU load times that are slower to start but excellent for sustained tasks.
The strategic angle: adopt a “portfolio of models” mindset. Measure real throughput and accuracy on your data. Optimize by hardware. Automate with intention.
Roadmap Signals: Linux, LoRA, and Community
- Linux support: Actively advocated and being worked on. Initial priority has been Windows and macOS due to user base distribution; WSL or native Linux are in sight post major events.
- LoRA adapters: Not supported today for local application pipelines; recognition that it’s valuable and likely to land later as product maturity grows.
- Community-first: The team is responsive on Discord and open to model requests. If the community cares, it gets prioritized.
This matters for automation leaders: the platform is evolving with real-world input. If your workflow needs something, raise it early.
The Automation Playbook: How FlowDevs Can Leverage Foundry Local
For automation companies and dev teams at flowdevs.io, here’s a practical strategy to turn on-device AI into real ROI:
- Audit your tasks by sensitivity and latency.
- Keep PII, financial, and compliance-heavy flows fully local.
- Push non-sensitive, scale-heavy jobs to hosted when throughput matters.
- Build a multimodel strategy.
- Small CPU models for instant answers and routing.
- Mid-to-large GPU models for reasoning, synthesis, and code assist.
- NPU for sustained, power-efficient edge workflows.
- Design for offline-first.
- Critical flows (document classification, form extraction, basic vision checks) should run with no network dependency.
- Cache prompts and responses; degrade gracefully when models aren’t loaded.
- Adopt “model ops” for local.
- Version models, track parameters (temperature, context size), and log local performance.
- Use Foundry’s SDK to encapsulate model calls behind adapters, so swapping models is easy.
- Prioritize privacy UX.
- Clearly communicate to end-users when data never leaves their device.
- Offer toggles for local vs. hosted execution, with transparent cost/perf notes.
- Iterate with community feedback.
- Request models your users need (domain-specific, license-compliant).
- Share reproducible benchmarks that reflect your real tasks, not synthetic tests.
Outcome: A resilient, privacy-first automation stack that harnesses hardware you already own, keeps costs under control, and delights users with fast, trustworthy AI.
Slogan Spotlight
“Your employer bought the devices with these NPUs. Put them to work.”
It captures the ethos perfectly: modern machines have untapped AI acceleration. Foundry Local turns that buried capability into real automation leverage—securely, locally, and at zero software cost.
Call to Action
- Install Foundry Local on Windows or macOS and run your first CPU/GPU/NPU model today.
- Plug into VS Code AI Toolkit for local Copilot-style assistance.
- Check out our other blog post on Foundry Local: What Is Microsoft Foundry Local?
On-device AI isn’t a niche—it’s the new foundation for privacy-first, cost-aware, and offline-capable automation. FlowDevs and automation leaders who embrace it now will set the pace for the next era of intelligent systems.
The Rise of On-Device AI: Why Foundry Local Is Poised to Redefine Automation
If you’ve felt the explosion of AI models and tooling, you’ve probably also felt the friction: too many choices, unclear performance on your hardware, unclear licensing, and a constant tug-of-war between privacy, cost, and speed. Foundry Local is tackling that head-on with an opinionated, hardware-accelerated approach to running AI models on your devices—CPU, GPU, and NPU—without sending your data anywhere.
Here’s the big idea: your company already bought machines with capable GPUs and NPUs. Put them to work.
- On-device models deliver privacy by default. Your data never leaves the machine.
- Offline is a feature, not a fallback. Mission-critical workflows keep running even when the network doesn’t.
- Cost control is immediate and obvious. Compute is “free” after the device purchase; Foundry Local itself is free to use.
- Performance is real, not theoretical. Foundry partners directly with AMD, NVIDIA, Qualcomm, and Intel (via OpenVINO) to publish hardware-optimized models.
The result is a practical foundation for automation that’s fast, secure, and composable across heterogeneous fleets—perfect for enterprises that care about data governance and for builders who want predictable performance on real hardware, not on someone’s benchmark lab.
Practical Multimodel Orchestration: CPU, GPU, and NPU Working Together
The most compelling demo isn’t a fancy benchmark—it’s watching three different models run concurrently across CPU, GPU, and NPU, all on a shared-memory device, all accelerated. That concurrency matters for real-world automation:
- Low-latency “quick answer” tasks (e.g., capital-of-France) on compact CPU models.
- Heavier reasoning or longer context on 7B GPU models when you need depth.
- Specialized NPU models for energy-efficient, sustained workloads and future mobile/edge deployments.
What this unlocks:
- Parallel workflows with no queuing headaches.
- Token-per-second that tracks with model size and hardware constraints.
- Flexibility to tune parameters (context window, temperature, quantization) per model to hit your latency/quality targets.
Even on modest machines, you can juggle multiple jobs and still get responsive experiences. That’s the automation developer’s dream: pair the right model with the right job at the right time—locally.
Hugging Face, Licensing, and Bring-Your-Own-Model Reality
Model availability is expanding fast. Foundry Local ships a curated set today, with active work to add more via direct collaboration with providers (e.g., Meta for Llama) and compatibility with Hugging Face ecosystems.
- You can bring your own models now; documentation covers the process.
- Publishing and seamless pulling from Hugging Face is in the works—resource constraints are real, but the intent is clear.
- License restrictions remain a practical constraint; curation ensures compliance.
Translation for automation teams: you won’t be blocked by provider lock-in. Your preferred model—and your domain-specific fine-tunes—can run on-device with acceleration, when licensing allows.
Dev Experience: GitHub Copilot, VS Code, and SDKs
Foundry Local isn’t just a CLI. It plugs into your workflow.
- VS Code AI Toolkit integration lets you manage local models and use them within GitHub Copilot for code edits and chat. It’s a fun (and sometimes free) way to offload routine tasks to local compute.
- REST endpoints and SDKs mean you can hit Foundry Local like any service, but without leaving the machine.
- Model management UX (download, retain flags, concurrent sessions) is designed for builder velocity.
Caveat: Copilot pipes a lot of context, and smaller local models may misinterpret domain specifics. For production automation, pick models intentionally and test with your real prompts and datasets.
Foundry Local vs. Cloud-Hosted: Choosing the Right Mode
Why choose Foundry Local over other hosted solutions? It boils down to data, downtime, and dollars.
- Privacy: Banks, tax firms, and regulated industries can keep sensitive data fully local. No data egress means simpler compliance and fewer attack surfaces.
- Offline-first: Field operations, edge sites, or remote scenarios (hikes, labs, disaster recovery) keep working when the network doesn’t. This is resilience as a feature.
- Cost and experimentation: Fine-tune locally, validate utility, and scale hosted only when needed. Foundry Local is free; the “cost” is your device and electricity.
Hosted still has a place—especially for centralized scaling, multi-tenant needs, or managed SLAs. The thought-leader takeaway: treat local and hosted as complementary modes. Build automation that flows across both.
Performance, Tokens, and Reality Checks
Benchmarks are fickle; your workload is what matters. Still, teams are seeing:
- Faster tokens-per-second and load times on larger models and wider context windows when using Foundry’s hardware-optimized builds.
- Noticeably better experience when pairing model size to task complexity (e.g., 0.5–1.5B for utility; 7B for depth).
- NPU load times that are slower to start but excellent for sustained tasks.
The strategic angle: adopt a “portfolio of models” mindset. Measure real throughput and accuracy on your data. Optimize by hardware. Automate with intention.
Roadmap Signals: Linux, LoRA, and Community
- Linux support: Actively advocated and being worked on. Initial priority has been Windows and macOS due to user base distribution; WSL or native Linux are in sight post major events.
- LoRA adapters: Not supported today for local application pipelines; recognition that it’s valuable and likely to land later as product maturity grows.
- Community-first: The team is responsive on Discord and open to model requests. If the community cares, it gets prioritized.
This matters for automation leaders: the platform is evolving with real-world input. If your workflow needs something, raise it early.
The Automation Playbook: How FlowDevs Can Leverage Foundry Local
For automation companies and dev teams at flowdevs.io, here’s a practical strategy to turn on-device AI into real ROI:
- Audit your tasks by sensitivity and latency.
- Keep PII, financial, and compliance-heavy flows fully local.
- Push non-sensitive, scale-heavy jobs to hosted when throughput matters.
- Build a multimodel strategy.
- Small CPU models for instant answers and routing.
- Mid-to-large GPU models for reasoning, synthesis, and code assist.
- NPU for sustained, power-efficient edge workflows.
- Design for offline-first.
- Critical flows (document classification, form extraction, basic vision checks) should run with no network dependency.
- Cache prompts and responses; degrade gracefully when models aren’t loaded.
- Adopt “model ops” for local.
- Version models, track parameters (temperature, context size), and log local performance.
- Use Foundry’s SDK to encapsulate model calls behind adapters, so swapping models is easy.
- Prioritize privacy UX.
- Clearly communicate to end-users when data never leaves their device.
- Offer toggles for local vs. hosted execution, with transparent cost/perf notes.
- Iterate with community feedback.
- Request models your users need (domain-specific, license-compliant).
- Share reproducible benchmarks that reflect your real tasks, not synthetic tests.
Outcome: A resilient, privacy-first automation stack that harnesses hardware you already own, keeps costs under control, and delights users with fast, trustworthy AI.
Slogan Spotlight
“Your employer bought the devices with these NPUs. Put them to work.”
It captures the ethos perfectly: modern machines have untapped AI acceleration. Foundry Local turns that buried capability into real automation leverage—securely, locally, and at zero software cost.
Call to Action
- Install Foundry Local on Windows or macOS and run your first CPU/GPU/NPU model today.
- Plug into VS Code AI Toolkit for local Copilot-style assistance.
- Check out our other blog post on Foundry Local: What Is Microsoft Foundry Local?
On-device AI isn’t a niche—it’s the new foundation for privacy-first, cost-aware, and offline-capable automation. FlowDevs and automation leaders who embrace it now will set the pace for the next era of intelligent systems.