Technology for emerging-market businesses and institutions.
Explore Deviceterra ↗A practical shortlist, a compatible engine, and a setup you can test.
Answer the simple questions below. If you do not know a detail, MamiLens will use the safer option.
You can correct every answer below. A starter never claims to know your exact hardware.
Enter the free space on the drive where you will keep the model. MamiLens does not read your files or disk automatically.
Enter your computer, graphics, main job and free disk space above. MamiLens does not detect your device automatically.
Use this only when you want to search beyond the three recommendations.
Alibaba Cloud · 9B
Qwen 3.5 9B is a curated dense profile for general, coding, reasoning workloads.
Meta · 8B
Llama 3.1 8B is a curated dense profile for general, writing, coding workloads.
Alibaba Cloud · 8B
Qwen 3 8B is a curated dense profile for general, coding, reasoning workloads.
IBM · 8B
Granite 3.2 8B is a curated dense profile for general, reasoning, tools workloads.
Alibaba Cloud · 7B
Qwen 2.5 7B is a curated dense profile for general, writing, coding workloads.
Alibaba Cloud · 7B
Qwen 2.5 VL 7B is a curated dense profile for vision, general, research workloads.
Mistral AI · 7B
Mistral 7B is a curated dense profile for general, writing, tools workloads.
Alibaba Cloud · 4B
Qwen 3.5 4B is a curated dense profile for general, coding, reasoning workloads.
Alibaba Cloud · 4B
Qwen 3 4B is a curated dense profile for general, coding, reasoning workloads.
Google · E4B QAT
Gemma 4 E4B QAT is a curated dense profile for general, writing, reasoning workloads.
NVIDIA · 4B
Nemotron 3 Nano 4B is a curated dense profile for general, reasoning, tools workloads.
Google · 4B
Gemma 3 4B is a curated dense profile for general, writing, reasoning workloads.
Showing 12 of 98 matches
The engine and installation steps stay locked until you make your own model choice.
Return to the matchesDuring the public launch, Deviceterra can check the selected model, artifact, engine and hardware at no cost.
Estimate memory, compare candidates or plan a first deployment. No sign-up and no hardware information leaves your browser.
Save your result on this device, install MamiLens for quick access, or receive occasional updates when new hardware guides and model profiles are published.
Deviceterra helps small businesses and institutions in emerging countries increase profit using technology today, and build for the global market tomorrow. MamiLens delivers one part of that mission: practical, private and affordable AI adoption.
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 →The planning tools and configuration review are public during this launch phase. Clear paid services may come later, after MamiLens has earned trust and demonstrated consistent value.
Plan what to test first
A written check of your selected setup
Tell Deviceterra where your setup is blocked
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 MamiLens. 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 →MamiLens owns the full guides and internal search journey. Relevant articles link to Deviceterra for the wider technology and deployment perspective.
A profile passes only when weights, runtime and selected context fit conservatively. Responsiveness still requires a benchmark on the actual machine.
MamiLens labels full-VRAM fits, partial offload and CPU/RAM execution separately instead of treating every runnable model as equal.
Combined VRAM is useful only when the runtime supports the model, quantization and sharding topology. Interconnect and offload strategy matter.
MoE models load all weights but activate only some experts per token. Active parameters can improve compute efficiency without reducing storage.
A published maximum is not a promise that your hardware can use it. The engine adds more headroom as the selected context grows.
Official pages establish specifications. Community and creator tests inform practical fit, but MamiLens never copies speed results to untested hardware.