A self-built server with 1TB of ECC RAM and a mix of used 3090s and P100s produced internal tools whose economic impact is reported at ten times the hardware cost.
Start with your machine.
Capacity is the reported total for this setup. Multi-GPU and unified-memory layouts can behave differently.
A freelance worker keeps restricted client work local on a large multi-machine setup that also doubles as a Blender and data-processing workstation.
A two-3090 local setup runs Qwen 27B for coding and infrastructure work, including bug fixing and quality-of-life automation.
A self-hosted AI ecosystem spans two Linux servers, assigning different GPUs to long-context coding, inference, speech, RAG and media workloads.
A 3090 owner runs Qwen 3.8 27B daily in Cline at a 96K context by quantising the KV cache to q8_0, reporting about 43 tok/s once the model stops thinking.
A custom llama.cpp fork for Ampere cards pushes Qwen 3.8 27B to 90+ tok/s through a 100K context, aimed at caching-heavy agentic work.
Three GPUs spread across machines run Qwen 3.8 27B through a custom stateful agent layer to build a personal finance and portfolio tracker without shipping documents to the cloud.
A contractor whose clients forbid commercial providers from reading their source code runs unit tests and code reviews locally on a 3090, with a long-context llama.cpp setup.
A developer uses Qwen 3.8 27B for primary coding on a 3090-plus-5060 setup and pays for frontier inference only when speed or model depth matters.
A teacher runs an entire exam-correction pipeline on a single 3090, from generating answer sheets to scoring hundreds of students automatically.
Client confidentiality rules out cloud APIs for this freelancer, who spreads different model sizes across three separate machines.
A four-bit Qwen 3.8 quant at 200K context handles complete app builds through OpenCode, left running until it hits a decision point.