Minimum / Entry
- System RAM
- 8GB RAM as a very limited starting point
- GPU / storage
- CPU-only possible; storage for downloaded models required
- Best suited for
- Mainly smaller quantized models, short prompts, and cautious expectations.
Ollama hardware guide
Ollama System Requirements
Ollama can run on different hardware levels, from CPU-only systems to high-memory GPU workstations. There is no single universal RAM or VRAM number for every model because fit depends on model size, quantization, context length, runtime overhead, and available system or GPU memory.
Quick answer
Ollama requirements depend on the model you run.
Small quantized models can run on modest hardware, but the useful model choices are narrower.
More RAM or VRAM gives more model choices, context headroom, and room for runtime overhead.
A discrete GPU is helpful for responsiveness, but Ollama can run CPU-only for some workflows.
There is no single universal RAM or VRAM number because model size, quantization, context, and offloading all matter.
Baseline planning
Minimum and Recommended Ollama System Requirements
These tiers are practical planning guidance, not official guarantees. RAM or VRAM alone does not prove that a specific model will fit; model size, quantization, context length, runtime overhead, GPU offload, and storage all still matter.
Practical
- System RAM
- 16GB RAM
- GPU / storage
- 8GB+ VRAM when using a supported NVIDIA or AMD GPU
- Best suited for
- Better flexibility for small-to-mid local models and everyday experimentation.
Comfortable
- System RAM
- 32GB RAM
- GPU / storage
- 12GB-16GB+ VRAM
- Best suited for
- More headroom for mid-size models, longer context, and local development workflows.
High-memory
- System RAM
- 64GB+ RAM
- GPU / storage
- 24GB+ VRAM or substantial unified memory
- Best suited for
- Broader experimentation with larger local models and heavier context settings.
System memory
How Much RAM Does Ollama Need?
Ollama RAM use changes with model size, quantization, context length, operating-system overhead, runtime behavior, and CPU offloading. If the model is not fully handled in GPU memory, system RAM becomes especially important. On Apple Silicon, CPU and GPU share unified memory, so total memory is part of the model-fit question.
8GB RAM
A constrained starting point. Smaller quantized models may be possible, but the operating system, browser, Ollama runtime, and model context can quickly consume available memory.
16GB RAM
A more practical baseline for many small local LLM experiments. Model size, quantization, context length, and CPU offloading still decide whether a specific model is comfortable.
32GB RAM
A stronger tier for local LLM use, especially when system RAM participates in CPU inference, partial offload, or larger context windows.
64GB+ RAM
Useful for larger models, multiple local AI tools, and CPU-heavy or offload-heavy setups. On Apple Silicon, unified memory is shared by the CPU and GPU, so the total memory pool matters.
GPU acceleration
Ollama GPU and VRAM Requirements
A GPU is optional in some Ollama setups, but GPU acceleration can improve responsiveness when the selected model and quantization fit available VRAM. VRAM requirements depend on the model, context length, quantization, runtime overhead, and whether layers are offloaded to system memory.
8GB VRAM
Best treated as a careful planning tier for smaller quantized models, tighter context windows, and conservative runtime settings.
12GB VRAM
Adds more practical headroom for common small-to-mid local models, especially when quantization and context length are chosen carefully.
16GB VRAM
Gives stronger flexibility for mid-size models, larger quantizations, and more comfortable local development experiments.
24GB+ VRAM
Supports broader experimentation with larger models, more context, and extra runtime headroom, while still depending on the exact model and settings.
CPU-only use
Can Ollama Run Without a GPU?
Yes. Ollama can run on CPU-only systems for some local LLM workflows. It is generally slower than GPU-accelerated execution, so smaller models, lower quantizations, and conservative context settings are more practical.
When there is no discrete GPU, system RAM becomes the main memory pool for model loading and runtime overhead. Storage also matters because downloaded model files can be large.
Check what local LLMs your PC can runPlatform notes
Ollama Requirements by Operating System
Windows
Ollama can run as a native Windows application on modern 64-bit Windows systems. CPU support, available RAM, model storage, and supported GPU drivers matter more than the OS label alone, especially when you want acceleration.
macOS
Apple Silicon is especially relevant because Ollama can use the shared CPU/GPU memory pool. Unified memory affects model capacity, so compare total memory and model size together rather than treating VRAM as a separate number.
Compare Apple Silicon memory tiersLinux
Linux is commonly used for local AI setups. CPU execution can work, while NVIDIA or AMD acceleration depends on supported drivers, runtime support, and whether Ollama can see usable GPU memory.
Hardware tiers
Recommended Hardware Tiers for Ollama
Lightweight local chat
Start with smaller quantized models, 16GB RAM when possible, and modest context settings. CPU-only can be acceptable for experiments if slower responses are fine.
General local LLM use
A practical target is 16GB-32GB RAM plus a supported GPU with 8GB-12GB+ VRAM, then choose model size and quantization to match the available memory.
Coding / development
Coding prompts often involve longer files and tool context. More RAM, 12GB-16GB+ VRAM, and careful context settings give more room for local code generation and debugging.
Larger-model experimentation
Plan around 64GB+ system RAM, 24GB+ VRAM, or high unified memory. Larger models still depend on quantization, context length, and runtime overhead.
PC fit checker
Can My PC Run Ollama?
The practical answer depends on GPU VRAM, system RAM, model size, quantization, context length, and runtime/software behavior. Start from your hardware profile when you want a fit result for local LLMs rather than a generic hardware tier.
Common questions
Ollama System Requirements FAQ
How much RAM does Ollama need?
Ollama RAM needs depend on the model, quantization, context length, and whether the workload runs on CPU, GPU, or partial offload. 8GB is a very limited starting point, 16GB is more practical, and 32GB or more gives better headroom.
Does Ollama require a GPU?
No. Ollama can run without a discrete GPU in some setups, but GPU acceleration can make local LLM use more responsive when the model and runtime fit available VRAM.
Can Ollama run on CPU?
Yes. CPU-only use is possible, usually with slower responses than GPU-accelerated execution. Smaller quantized models and enough system RAM are the most practical CPU-only starting point.
Can I run Ollama on my PC?
It depends on your CPU, system RAM, GPU VRAM, operating system, model size, quantization, context length, and runtime settings. Use the AIPCFit PC checker to compare your hardware against local model data.
How much VRAM does Ollama need?
There is no universal Ollama VRAM requirement. 8GB can be useful for smaller quantized models, 12GB-16GB gives more flexibility, and 24GB+ opens more room for larger models and context.
Is 8GB RAM enough for Ollama?
8GB RAM can be enough only for very limited use with smaller quantized models and conservative settings. It is not a comfortable general recommendation.
Is 16GB RAM enough for Ollama?
16GB RAM is a more practical starting point for small local LLM experiments, but model size, quantization, context, GPU offload, and other applications still matter.
Is Apple Silicon good for Ollama?
Apple Silicon can be a good Ollama platform because CPU and GPU share unified memory. The useful model range depends heavily on total unified memory and the model configuration.
Does Ollama work on Windows?
Yes. Ollama supports Windows, but GPU acceleration depends on supported hardware, drivers, and runtime support. Available RAM, VRAM, and storage still determine practical model fit.
Does Ollama work on Linux?
Yes. Linux is commonly used for Ollama and local AI. CPU execution and GPU acceleration depend on the hardware, driver stack, runtime support, and available memory.