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Beaverd (@beaverd) posted a celebratory tweet

2026-08-04 · 19:02 UTC ·@beaverd ·0 min read

Brief

Beaverd tweeted a boast about doubling from $1M to $2M and credited “the trenches” for keeping a coin free. Separately, Leyten ran DeepSeek-V4-Flash (284B parameters) on six RTX 5090s across Europe over the open internet, reporting ~30 tok/s for short code contexts and ~14–23 tok/s for 2k-context math, prose and agentic tasks.

Why it matters

Beaverd (@beaverd) posted a celebratory tweet: “if you liked it when I made $1 million you're gonna love it when I make $2 million,” thanking God and claiming “the trenches are still retarded and are keeping this coin free.”

Key details

Leyten (@leyten) reported running DeepSeek-V4-Flash (a 284 billion parameter model from @deepseek_ai) across 6 distinct RTX 5090 GPUs distributed around Europe over the open internet, measuring throughput by workload: short-context code at ~30 tokens/sec and math/prose/agentic workloads at 2k context yielding ~14–23 tokens/sec.

Source evidence

if you liked it when I made $1 million you're gonna love it when I make $2 million

thanking God the trenches are still retarded and are keeping this coin free

leyten (@leyten)

1/ We ran DeepSeek-V4-Flash (@deepseek_ai), a 284 billion parameter model, on 6 distinct RTX 5090s spread across Europe over the open internet.

One ring, tested across workloads:
• short-context code: 30 tok/s
• math, prose and agentic work out to 2k context: 14 to 23 tok/s

— https://nitter.net/leyten/status/2084011202007408772#m