Best Local AI Models for 16GB RAM and 8GB VRAM
Check local AI models for a PC with 16GB system RAM and an NVIDIA GPU with 8GB VRAM.
Yes. A computer with 16GB RAM can run small local AI models. A safe first choice from the current MamiLens list is Qwen 3 4B. This is a memory-fit estimate, not a speed promise.
The device we checked
MamiLens keeps 5GB for the operating system and background work. That leaves a working model budget of about 11GB.
Best models to start with
Qwen 3 4B
Qwen 3 4B is a curated dense profile for general, coding, reasoning workloads.
- Calculated weights
- 2.5 GB
- Estimated total RAM
- 9 GB
- RAM left
- 7 GB
- Expected path
- CPU / RAM
- Speed
- Benchmark required
- Evidence
- Calculated estimate
ollama run qwen3:4bOpen evidence-labelled model profile →Nemotron 3 Nano 4B
Nemotron 3 Nano 4B is a curated dense profile for general, reasoning, tools workloads.
- Calculated weights
- 2.8 GB
- Estimated total RAM
- 9 GB
- RAM left
- 7 GB
- Expected path
- CPU / RAM
- Speed
- Benchmark required
- Evidence
- Calculated estimate
ollama run nemotron-3-nano:4bOpen evidence-labelled model profile →Phi-4 Mini 3.8B
Compact multilingual reasoning and mathematics model for modest systems.
- Calculated weights
- 2.5 GB
- Estimated total RAM
- 9 GB
- RAM left
- 7 GB
- Expected path
- CPU / RAM
- Speed
- Benchmark required
- Evidence
- Calculated estimate
ollama run phi4-miniOpen evidence-labelled model profile →Qwen 3.5 4B
Qwen 3.5 4B is a curated dense profile for general, coding, reasoning workloads.
- Calculated weights
- 3.4 GB
- Estimated total RAM
- 11 GB
- RAM left
- 5 GB
- Expected path
- CPU / RAM
- Speed
- Benchmark required
- Evidence
- Calculated estimate
ollama run qwen3.5:4bOpen evidence-labelled model profile →Gemma 3 4B
Gemma 3 4B is a curated dense profile for general, writing, reasoning workloads.
- Calculated weights
- 3.3 GB
- Estimated total RAM
- 11 GB
- RAM left
- 5 GB
- Expected path
- CPU / RAM
- Speed
- Benchmark required
- Evidence
- Calculated estimate
ollama run gemma3:4bOpen evidence-labelled model profile →Recommended inference engine
Ollama or llama.cpp with CUDA
This is the simplest supported starting path for NVIDIA acceleration.
CPU use is practical for small models, but speed must be tested.How accurate is this result?
This page uses the same calculator rules as the live MamiLens tool. It includes model weights, runtime overhead, operating-system reserve, context reserve and simultaneous-user cache when selected. A displayed speed range is calculated from published memory bandwidth. It is not a benchmark from this exact computer.
Start with the first model. Test a normal task, record response speed and watch memory use before using it for important work.