Local hardware & model compatibility audit

Running local models and retrieval pipelines requires a careful match between your hardware and your workload. When model parameters outgrow your GPU memory or system RAM, processing speed can collapse as the operating system falls back on disk storage.

Running large language models and retrieval-augmented pipelines locally demands a close match between your chosen model and the hardware you have on hand. When the parameter count outstrips your graphics memory or system RAM, throughput can fall away almost entirely as your operating system starts swapping to disk.

This service gives you a full hardware compatibility check before you commit to any download or install. We look at your actual machine - Apple Silicon, an NVIDIA CUDA-equipped workstation, or a standard Windows or Linux server - and set out how to get the most from it locally.

What we check

1. Graphics memory limits: we map your dedicated VRAM to establish the largest model you can run, whether that is a 7B, 13B, or 34B parameter build.
2. System memory allocation: we work out your CPU-inference thresholds and offloading limits for machines without a dedicated GPU, or where memory is shared.
3. Quantisation guidance: we suggest a suitable quantisation setting - typically Q4_K_M, Q5_K_M, or Q8_0 - to match processing speed against model quality for your setup.
4. Context window ceiling: we calculate the safe maximum token count so heavy document retrieval does not push your system into an out-of-memory failure.

Rather than guessing which open-source weights will run, or wondering why your local setup is sluggish, you receive a hardware-specific plan built by people who work with this equipment directly. To get started, send us the domain the order relates to and a brief describing your setup and goals.

What happens after you buy

Your order opens its own thread here the moment it is paid, and everything about that order - questions, changes and the final report - happens in it.