Practical hardware examples
What can a complete rig actually run?
Start with representative local AI computers, understand their practical limits, and open a preselected compatibility check. These are educational examples, not sponsored build recommendations.
Current guide set4 representative rigs
Choose a starting pointRig capability guides
Results depend on system RAM, operating system, quantization, context length, and runtime. Open each checker to adjust those details.
Accessible desktop12 GB VRAM
RTX 3060 rig
A useful entry point for local chat, coding, embeddings, speech, and selected image-generation workloads.
- Strong fit for small and mid-size quantized text models
- Good ecosystem support on Windows and Linux
- Larger models may require CPU offload
Check this rig
Modern balanced desktop12 GB VRAM
RTX 4070 rig
A faster, efficient desktop configuration for local assistants, coding models, and creative AI workflows.
- Faster inference than entry-level 12 GB cards
- Well suited to common 7B–8B model families
- VRAM remains the limit for larger workloads
Check this rig
High-end desktop24 GB VRAM
RTX 4090 rig
A capable single-GPU workstation for larger quantized models, heavier image workflows, and experimentation.
- More room for larger models and context
- High memory bandwidth for responsive generation
- Power, cooling, and system balance matter
Check this rig
Large unified memory128 GB unified
Apple M3 Max rig
A compact workstation able to load unusually large models through Apple unified memory, with different speed tradeoffs from a discrete GPU.
- Large model capacity in one memory pool
- Strong local workflow and energy efficiency
- Capacity does not automatically mean faster inference
Check this rig
Video evidenceDemonstrations will be added carefully.
Future guides can include relevant YouTube demonstrations when the creator, tested hardware, model, runtime, and result are clearly identified. This avoids presenting unrelated promotional videos as evidence.
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