Technology for emerging-market businesses and institutions.
Explore Deviceterra ↗LocalLens is Deviceterra's local AI decision tool. It filters proven models against your real hardware so businesses and institutions can adopt useful technology with less cost, risk and guesswork.
Deviceterra helps small businesses and institutions in emerging countries increase profit using technology today, and build for the global market tomorrow. LocalLens delivers one part of that mission: practical, private and affordable AI adoption.
Use practical calculators before downloading a large model. No sign-up and no hardware information leaves your browser.
Define the job first. LocalLens turns it into a small, testable and safer first deployment.
Begin with 10 approved documents and five questions with known answers.
Keep the first setup single-user and record the exact model, runtime and settings.
No hardware data leaves this page until you choose to request help.
It fits our memory rule, but performance must be confirmed on your device. This is a compatibility decision - not a speed guarantee.
Not a mirror of Hugging Face. A shortlist people can actually use.
Alibaba Cloud · 8B
A widely used hybrid reasoning family with tool support and multilingual strength.
DeepSeek · 8B
Distilled reasoning variants for step-by-step analysis and mathematics.
Meta · 8B
A broadly supported general model with a mature ecosystem and tool use.
DeepSeek · 7B
Distilled reasoning variants for step-by-step analysis and mathematics.
Alibaba Cloud · 7B
Code-specific models for generation, fixing, explanation and code reasoning.
Mistral AI · 7B
A long-standing compact instruction model with a permissive commercial license.
Google · E4B
A stronger efficient multimodal option for laptops and edge devices.
Google · 4B
Mature multimodal variants for question answering, summarization and image understanding.
Meta · 3B
Mature small models for summarization, rewriting and personal information tasks.
Alibaba Cloud · 4B
A widely used hybrid reasoning family with tool support and multilingual strength.
Microsoft · 3.8B
Compact multilingual reasoning and mathematics with function calling.
Google · E2B
Designed for efficient execution on everyday and edge devices.
Google · 12B
Mature multimodal variants for question answering, summarization and image understanding.
Meta · 11B
Image reasoning, captioning and visual question answering.
Alibaba Cloud · 3B
Code-specific models for generation, fixing, explanation and code reasoning.
Alibaba Cloud · 14B
A widely used hybrid reasoning family with tool support and multilingual strength.
DeepSeek · 14B
Distilled reasoning variants for step-by-step analysis and mathematics.
Alibaba Cloud · 14B
Code-specific models for generation, fixing, explanation and code reasoning.
Meta · 1B
Mature small models for summarization, rewriting and personal information tasks.
Google · 1B
The text-only compact Gemma 3 variant for lightweight language tasks.
DeepSeek · 1.5B
Distilled reasoning variants for step-by-step analysis and mathematics.
Alibaba Cloud · 1.5B
Code-specific models for generation, fixing, explanation and code reasoning.
OpenAI · 20B
Open-weight reasoning and agentic model for capable local systems.
Mistral AI · 24B
Higher-quality instruction following, vision and robust function calling.
Google · 27B
Mature multimodal variants for question answering, summarization and image understanding.
Alibaba Cloud · 30B-A3B
A widely used hybrid reasoning family with tool support and multilingual strength.
DeepSeek · 32B
Distilled reasoning variants for step-by-step analysis and mathematics.
Alibaba Cloud · 32B
Code-specific models for generation, fixing, explanation and code reasoning.
The engine decides how weights use your CPU, GPU and memory, how requests are batched, and how applications connect. LocalLens recommends a starting engine without claiming a universal speed winner.
Fastest route from model choice to a local API
Simple installation, repeatable model tags and broad app integrations. Benchmark against alternatives on your device.
Visual model testing and configuration
A graphical workflow. Apple Silicon users can test both llama.cpp and MLX-based paths where supported.
Portable control across consumer hardware
A strong baseline for CPU, Metal, CUDA, HIP, Vulkan and hybrid offload. Speed depends on backend and settings.
Portable multimodal and embedding workloads
Flexible text, vision, audio and embedding support. Verify exact model and backend maturity before production.
Apple Silicon generation and fine-tuning
Purpose-built for Apple Silicon unified memory. Compare it with a Metal llama.cpp path using equivalent models.
High-throughput concurrent GPU serving
Continuous batching, parallelism and production APIs. Usually unnecessary for one person chatting on a laptop.
Low-latency structured and agent workloads
Optimized production serving when you can operate and benchmark GPU infrastructure.
Shortlist the engine by hardware and scale, then benchmark the same model, quantization, context and prompt on your machine.
Read the engine guide →Short, practical guidance for the questions people ask when starting with local AI. Each answer links to a complete, evidence-reviewed guide.
Yes. An 8GB computer can test small, quantized local models, usually in the 1B to 4B range. Close memory-heavy apps, keep context short and expect CPU generation to be slower than GPU inference.
Read the complete guide →No. A dedicated GPU improves speed, but compact models can run on a CPU with system RAM. The useful test is whether the model completes your real task at an acceptable quality and waiting time.
Read the complete guide →LM Studio is a strong visual starting point. Ollama is often better for command-line workflows, automation and applications that need a local API. The right choice depends on how you plan to use the model.
Read the complete guide →Only when the complete workflow stays local. A local model can still leak data through cloud transcription, web search, plugins, remote storage or exposed APIs. Audit every component, not only the model.
Read the complete guide →Choose the smallest proven model that fits your available memory and passes a test for your exact job. Do not choose by parameter count or popularity alone.
Read the complete guide →The model file, context cache, runtime, operating system and other applications all consume memory. Leave headroom above the download size and benchmark the exact quantization on your machine.
Read the complete guide →Launch pricing for worldwide customers. Pro access is fulfilled manually until automated billing is connected, so no payment is taken on this page today.
Reliable model discovery
For people running local AI seriously
Private AI that performs a business job
The model is only one component. Deviceterra installs the runtime, secures the data path, connects private knowledge and validates the workflow.
We first confirm that your hardware and use case are suitable. If local AI is the wrong answer, we say so before proposing a deployment.
Complete, evidence-first guides live here on LocalLens. Each one turns a technical decision into a practical next step.
Understand privacy, offline use, hardware requirements and where cloud AI still makes more sense.
Read complete guide →HARDWARE · 10 MINLearn why a model file fitting on disk does not automatically mean it will run well in memory.
Read complete guide →SETUP · 12 MINChoose the right local AI runtime for beginners, developers and business deployments.
Read complete guide →MODELS · 11 MINA practical family-level comparison for coding, reasoning, writing, vision and general work.
Read complete guide →BUSINESS · 10 MINPlan a private document assistant without handing sensitive files to an unknown cloud service.
Read complete guide →SAFETY · 9 MINTest output quality, speed and workflow fit before trusting a local model with important work.
Read complete guide →LocalLens owns the full guides and internal search journey. Relevant articles link to Deviceterra for the wider technology and deployment perspective.
A model passes only when system RAM exceeds the Ollama file size with conservative runtime headroom. Speed still requires testing.
If GPU memory is unknown, the engine never claims acceleration. CPU fallback may be slow but remains clearly labelled.
Every command maps to an Ollama library tag and every profile links back to its official listing.
When nothing satisfies the safety rule, LocalLens returns no recommendation rather than inventing one.
Published maximum context is displayed as a model specification, not a promise that full context fits the minimum RAM floor.
Medical, legal, financial and operational deployments require task-specific evaluation before production use.