With 97 GB+,
here is what really runs.
Experiences reported by people using their machines every day. The figure is the reported setup capacity; multi-GPU and unified-memory layouts can behave differently.
in this range
A HIPAA-style internal LLM platform, built on a cluster of DGX Sparks
Beyond personal daily use, this person stood up a Spark cluster for a client so their employees get an internal, compliance-friendly LLM platform.
A cardiologist's referral system, fully air-gapped
Referral triage that extracts details, standardizes them, and suggests categories and tests — with the final call always left to a doctor, and nothing ever leaving the network.
Construction estimating work moved onto a 7900XTX and two DGX Sparks
After six months of tinkering, Qwen 3.8 27B justified a 7900XTX plus two DGX Sparks, taking over a job the estimating team was spending dozens of hours a week on.
Running sales work on GLM 5.3 Flash across two DGX Sparks, 200M tokens a week
A sales professional dropped every frontier subscription and runs work agents on two DGX Sparks, averaging about 200 million tokens per week.
Internal company tools on a scavenged 1TB ECC server with mixed GPUs
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.
Eight A100s in tensor-parallel, inside a company with a hundred more
A large enterprise deployment: eight A100 80GBs running Qwen 3.8 under SGLang with tensor parallelism, while the company brings newer B200s online for bigger models.
Four DGX Sparks running agentic work all day
A four-DGX-Spark cluster under vLLM serves several concurrent agentic coding and research sessions, with unquantized weights and honest notes on concurrency limits.
Uncensored Qwen 3.8 Flash Next tops a 19-task hacking benchmark at 80.6% on 2x RTX Pro 6000
On a 19-task cybersecurity benchmark, an uncensored Qwen 3.8 Flash Next solved 80.6% first try, but the run used someone else's 2x RTX Pro 6000 and only 2 runs per task.
A 4x RTX 5090 (128GB) office rig serving a 50-person company: local RAG, chat, diffusion and agentic coding
A custom 4x RTX 5090 build with 128GB of VRAM, an Intel Xeon w5-3423 and 192GB of ECC DDR5 runs DeepSeek V4 Flash, Qwen 3.8 and Gemma 4 32B locally for a 50-person company that wants its data off grid.
Building a two-server local AI ecosystem across 17 GPUs
A self-hosted AI ecosystem spans two Linux servers, assigning different GPUs to long-context coding, inference, speech, RAG and media workloads.
ISP NOC automation on two vLLM servers with 4× RTX 3090 each
A small ISP (~7,600 customers) serves its agentic operations work on two identical machines, each with 4× RTX 3090 (250W-capped) and vLLM v0.30.0; llama-benchy measures ~5993 tok/s prefill and ~199 tok/s generation.
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Every capacity guide, or start from your own machine and a task.