pulmencr

18 • Dropping what's actually working in AI • Tech • Crypto

[pulmencr @pulmencr]

A guy who runs AI models locally just benchmarked three machines against each other

The results broke the internet
Nobody is talking about this device yet

M4 Pro Mac Mini - PP512: 563
Framework Desktop Strix Halo - PP512: 342
DJX Spark - PP512: 2107

That's not a typo. 2107.

Everyone was sleeping on the DJX Spark because they were only testing token generation speed. That's the wrong metric. The part that actually matters for heavy AI workloads is prompt processing - the computationally expensive phase that determines how fast your model thinks before it even starts responding.

Mac Mini handles it at 563. Strix Halo drops to 342. DJX Spark hits 2107.

That's 3.7x faster than the Mac Mini on the metric that actually counts.

The people who tested it early missed this completely. They ran basic chat sessions, saw average token generation, moved on. Nobody stress tested the pre-fill phase until now.

This changes the math on running large local AI models entirely. 30 billion parameter models that felt slow suddenly feel instant. The bottleneck most people blamed on their model was actually their hardware all along.

The DJX Spark isn't just another AI PC. It's the first consumer device that handles prompt processing at a level nobody expected at this price point.

Most people are still arguing about cloud vs local AI in 2026. This guy just showed why local won

[pulmencr @pulmencr]

@kingwilliam_ this is the part most people still miss

[pulmencr @pulmencr]

@Nekt_0 last minute fires turning into easiest money, that’s the whole shift

[pulmencr @pulmencr]

@defileo your notes talking back to you, that changes everything

[pulmencr @pulmencr]

@AnatoliKopadze 2027 is the inflection, 2026 is the prep window, the people who get this are already moving

[pulmencr @pulmencr]

@Asteri_eth agents running in the cupboard while you order coffee, this is the setup

[pulmencr @pulmencr]

@0xDezo typed the prompt once, Claude wrote every shader, studio work in 4 days

[pulmencr @pulmencr]

@Blum_OG research first, patterns second, writing third, most people skip the first two

[pulmencr @pulmencr]

@antpalkin $5,280 a year to rent vs $4,000 once to own, the math finally clicked

[pulmencr @pulmencr]

@doublenickk AI raised the floor and raised the ceiling at the same time

[pulmencr @pulmencr]

@0xKiyoro 2.5M views and he was asleep when it happened

[pulmencr @pulmencr]

@0xCodez they build Claude with Claude, that's the most meta thing in tech right now

[pulmencr @pulmencr]

@ridark_eth one evening vs weeks of approvals, the gap is getting impossible to justify

[pulmencr @pulmencr]

@cyrilXBT this is insane result

[pulmencr @pulmencr]

@CoinSh0t give Claude the slice not the whole vault, that one line explains everything

[pulmencr @pulmencr]

@Kyodo686 in practice it means faster responses and better reasoning on complex tasks, you feel it immediately

[pulmencr @pulmencr]

A guy who runs AI models locally just benchmarked three machines against each other

The results broke the internet
Nobody is talking about this device yet

M4 Pro Mac Mini - PP512: 563
Framework Desktop Strix Halo - PP512: 342
DJX Spark - PP512: 2107

That's not a typo. 2107.

Everyone was sleeping on the DJX Spark because they were only testing token generation speed. That's the wrong metric. The part that actually matters for heavy AI workloads is prompt processing - the computationally expensive phase that determines how fast your model thinks before it even starts responding.

Mac Mini handles it at 563. Strix Halo drops to 342. DJX Spark hits 2107.

That's 3.7x faster than the Mac Mini on the metric that actually counts.

The people who tested it early missed this completely. They ran basic chat sessions, saw average token generation, moved on. Nobody stress tested the pre-fill phase until now.

This changes the math on running large local AI models entirely. 30 billion parameter models that felt slow suddenly feel instant. The bottleneck most people blamed on their model was actually their hardware all along.

The DJX Spark isn't just another AI PC. It's the first consumer device that handles prompt processing at a level nobody expected at this price point.

Most people are still arguing about cloud vs local AI in 2026. This guy just showed why local won

[pulmencr @pulmencr]

@I_say_GFY this setup actually makes sense, stealing this workflow

[pulmencr @pulmencr]

@Kyodo686 the benchmark gap is real, hard to argue with numbers like that

[pulmencr @pulmencr]

@ryuseihustleup that energy hits different, local AI + leftover hustle is the real meta

[pulmencr @pulmencr]

@Xbushh0 FLUX doing the heavy lifting and nobody giving it credit

[pulmencr @pulmencr]

A 21-year-old from China is charging businesses $1,500 for videos that cost him 15 minutes to make

He linked Claude to Higgsfield through MCP and started generating cinematic ad videos for local businesses

Over $13,000 last month. This is now his only income.

No film crew. No director. No budget. Just a prompt that tells Claude exactly what kind of shot to create - camera angle, lighting, motion, mood - and Higgsfield renders it into something that looks like it cost $50,000 to produce.

His pitch to clients is simple. He finds luxury brands, car dealerships, restaurants that are still paying agencies $5,000-10,000 for video ads. He shows them a sample. Charges $800-1,500 per clip. His actual cost - 15 minutes and a few API credits.

The gap between what agencies charge and what this costs is where the $13,000 a month comes from.

He calls it the new meta and looking at the numbers - it's hard to argue
Claude handles the creative direction. Higgsfield handles the visuals. He handles the invoice.

Most businesses have no idea this is even possible yet. That window doesn't stay open forever.

The ones who move first will eat. Everyone else will wonder what happened.

[pulmencr @pulmencr]

@Xbushh0 agencies charging $10k for the same output, the gap is just free money right now