What they did
The poster, u/TumbleweedNew6515, is a lawyer who runs a 2-bit quant of DeepSeek-V4.1-Flash on eight Tesla V100 cards across two NVLink boards, served through a ported vLLM build with their own speed tuning. Because Claude Code can talk to any server that speaks Anthropic's API, they point it at this machine and have it read folders of their own case records, extract facts, dates and numbers, summarise, and take a first pass at routine drafting, with tool calls working. Claude Code also ran the benchmarks and crunched the numbers for the post.
How it went
They say the setup is genuinely useful for real work and estimate 50 to 60% of their LLM workflows can move off frontier models. Measured speeds were about 200 tokens a second on code and 110 on legal writing, with 524K tokens of context. They found and fixed a parser bug that sent tool call arguments twice, after which Claude Code's tool call tests passed 144 of 144. It still serves one request at a time, so Claude Code cannot run subagents against it yet.
Worth knowing
Nothing the model drafts goes out without their own review. The cards cost about $3,200 plus $800 per board, with roughly $80 a month in power; they put the payback at about five months measured against API prices. A long 256K prompt takes about two minutes before the first token, fine for reading a file, slow for chat.