Local LLM Hardware Requirements

The five components that matter

For local language-model inference, think about CPU, system RAM, GPU, VRAM and storage separately. They solve different problems, and a strong specification in one area does not erase a hard limit in another.

The exact requirement changes with the model architecture, number of parameters, weight precision or quantization, context length, runtime, backend and whether all layers are placed on the GPU.

CPU: can you run an LLM without a GPU?

Yes. Runtimes in the llama.cpp ecosystem are designed to support CPU execution as well as hardware acceleration. CPU inference can therefore be a useful way to experiment without a discrete GPU.

The trade-off is usually throughput and responsiveness. A model that technically runs is not necessarily a model that feels pleasant for interactive use. CPU architecture, memory bandwidth, core configuration and model size all affect performance, so avoid rules such as “any 8-core CPU is enough.”

For a buying decision, test the specific model + quantization + runtime you intend to use.

System RAM

System RAM matters even when a GPU is present. The runtime and operating system need memory, and configurations that do not keep the entire workload in VRAM may rely more heavily on system memory.

Model file size is a useful first clue, but it is not a complete peak-memory estimate. Runtime structures and the context used during inference add overhead.

GPU and VRAM

A supported GPU can dramatically accelerate local inference by moving computational work onto hardware designed for highly parallel operations. But VRAM capacity is a hard practical constraint: model weights and runtime data must fit somewhere.

Ollama publishes a hardware-support page for its supported GPU backends, while llama.cpp documents multiple backends including CPU and GPU acceleration. Support changes over time, so check the current runtime documentation for your exact GPU rather than assuming that a vendor name alone guarantees support.

Context length also matters. Ollama explicitly documents context length as a setting that affects memory use. Long-context configurations therefore deserve their own memory budget rather than being treated as free.

Storage

Local models can consume substantial disk space, especially if you keep several quantizations or model families. Quantization can reduce the stored weight size considerably. llama.cpp's quantization tooling converts higher-precision GGUF weights into smaller quantized representations.

For a real workstation, budget storage for more than one model file: downloads, alternate quantizations, caches and updates accumulate quickly.

A better way to plan hardware

Instead of asking “What PC do I need for local AI?”, work backwards:

1. Choose the model family and size you actually want to use.
2. Choose a runtime and verify that it supports your operating system and accelerator.
3. Choose a quantization appropriate for your memory budget.
4. Decide the context length you realistically need.
5. Check whether the weights and runtime overhead fit in available RAM/VRAM.
6. Only then compare hardware.

That process also makes E—DOCEO's future VRAM calculator more useful: it can expose the assumptions instead of pretending one GPU-memory number applies to every runtime and workload.

Sources

• Ollama — hardware support
• Ollama — context length
• llama.cpp project documentation
• llama.cpp quantization tool