GPU & accelerator
The GPU determines which acceleration paths are available. NVIDIA systems commonly use CUDA, while other platforms can use different backends. Software support matters as much as the GPU name itself.
Local AI hardware guide
Local AI PC requirements
There is no single PC requirement for local AI. The hardware you need depends on what you want to run, the software stack, the model or workflow, memory pressure, and how much work can be accelerated by your GPU.
Quick answer
Start with the workload, not a universal VRAM number.
Local LLM inference, image generation, and PyTorch workloads use hardware differently. Even within one category, model size, quantization, context, resolution, precision, offloading, and software configuration can change practical requirements.
The six things that matter
What determines local AI compatibility?
VRAM
VRAM limits how much model data and runtime state can remain on the GPU. More VRAM can enable larger configurations, but there is no universal VRAM requirement for local AI.
System RAM
System RAM becomes especially important when model data is not fully resident in GPU memory, when CPU execution is used, or when applications offload work outside the GPU.
CPU
The CPU can be part of inference and offloading, and some applications also have instruction-set requirements. For example, LM Studio requires AVX2 on Windows x64.
Model or workflow
A small quantized language model, a large-context LLM, an image-generation workflow, and a training workload can have completely different hardware demands.
Software stack
The runtime, driver, accelerator backend, application version, and load settings can change whether the same hardware works well, works partially, or does not match a verified path.
Verified application guidance
Requirements are application-specific.
AIPCFit keeps application compatibility separate from workload fit. A software requirement does not tell you that every model or workflow will fit the same hardware.
Ollama
Local LLM fit depends on the specific model, quantization, context length, GPU VRAM, system RAM, and how much work stays on the GPU.
NVIDIA families currently verified in AIPCFit
RTX-30RTX-40RTX-50
LM Studio
LM Studio publishes application-level system guidance, but the model you load can require substantially different memory depending on its size, quantization, context, and offload configuration.
Check LM Studio model fitComfyUI
Image-generation requirements vary by model, resolution, precision, workflow, and nodes. A single VRAM number cannot describe every ComfyUI workflow.
NVIDIA families currently verified in AIPCFit
RTX-30RTX-40RTX-50RTX-20
PyTorch
Hardware support is only one part of PyTorch compatibility. Python, the PyTorch build, CUDA availability, drivers, and the workload itself also matter.
NVIDIA families currently verified in AIPCFit
RTX-20RTX-30RTX-40RTX-50
A concrete example
LM Studio on Windows x64
Unlike a universal local-AI requirement, these are application-level recommendations recorded from LM Studio's system requirements. They still do not establish that every LLM will fit.
- CPU
- AVX2 required
- System RAM
- 16 GB+ recommended
- Dedicated VRAM
- 4 GB+ recommended
These numbers are LM Studio application guidance, not universal minimums for local LLMs or other AI applications.
Workload matters
The same PC can behave very differently across AI tasks.
Local LLM inference
Model size, quantization, context length, KV cache, GPU offload, system RAM, and runtime all influence practical fit.
Check what LLM your PC can runImage generation
ComfyUI workloads can change dramatically with the selected model, image resolution, precision, batch behavior, and graph nodes.
Check GPUs for ComfyUIPyTorch workloads
A GPU being CUDA-capable does not establish a universal memory requirement. Training, inference, fine-tuning, tensor sizes, and the installed environment all matter.
Check PyTorch hardware pathsPopular hardware
See how common GPUs fit into local AI.
Learn the details
Common questions
How much RAM do I need for local AI?
There is no single RAM requirement for every local AI workload. The application, model or workflow, GPU offloading, context size, and other runtime settings all matter. LM Studio specifically recommends at least 16 GB of system RAM, but that is application guidance rather than a universal requirement for every local AI tool.
How much VRAM do I need for local AI?
There is no universal VRAM number. LM Studio recommends at least 4 GB of dedicated VRAM at the application level, but individual models and other AI workloads can require very different amounts of memory.
Can I run local AI without a GPU?
Some local AI software can use CPU execution, but practical speed and memory use depend on the workload and runtime. AIPCFit does not treat a dedicated GPU as a universal requirement for every local AI task.
Is more VRAM always better?
More VRAM can make larger configurations possible and reduce the need for offloading, but it does not by itself guarantee faster performance. GPU architecture, compute performance, memory behavior, software support, and the workload also matter.
Is model file size the same as required VRAM?
No. The model file is only one part of runtime memory use. Context length, KV cache, offloading, runtime implementation, and other load settings can change actual memory use.
AIPCFit methodology
Verified requirements stay separate from estimates.
AIPCFit distinguishes documented application requirements, hardware compatibility, model metadata, planning heuristics, and unknowns instead of turning all of them into one artificial minimum specification.
Read the methodology