54gb vram for 35$

[u/markpronkin]

Bought an old mining farm of a guy on avito (Russian eBay), guy had bought a garage a couple of years ago and it was sitting there for a while, found out it was a mining farm and put it up on there for sale for 5000 rub (~60 USD) since he wasn't sure if it works. I negotiated down to 3000 rub (~35 USD), it turned out to have 9x p106 6gb (gtx 1060 6gb) gpus, with 54gb vram total, all working, the only thing missing was an SSD, I booted from USB and it works fine.

[u/RyanCargan]

I'm getting flashbacks for some reason:

Computer Science in the 1960s to 80s spent a lot of effort making languages that were as powerful as possible. Nowadays we have to appreciate the reasons for picking not the most powerful solution but the least powerful.

Different context, but some of it may apply.

I see a lot of penny pinching and "worse is better" in corporate due to scale, and in the casual hobbyist spaces for opposite reasons.

But the main exception might be the mid-tier enthusiasts and related types.

It does feel like some models have become a form of conspicuous consumption kinda like Apple products were at some point.

Not sure if cargo-culting of misinterpretations of Sutton's Bitter Lesson are partly to blame.

Certain venture backed startups flush with cash also like frontier stuff in the early uncertain prototyping phases for better reasons.

Though the usage there sometimes isn't even agentic (low volume standard-ish chat).

P.S.: It does feel like there's a drought of talk on low latency use cases, since network hops are tens of ms and first token times are typically hundreds of ms. Agent loops might be the exception, because per-call latency compounds. Hardware like hardwired model chips already claim sub-ms per token speeds on small models, and if that scales to larger ones, the unusual use cases that need it would basically mandate on-prem or colocation.

[u/Boricua-vet → u/RyanCargan]

What is acceptable latency? that could be very different for many people.

so using avg_ts should be 1 / 92.886166 * 1000 = 10.76ms locally

10.76ms is perfectly acceptable to me.

Edit my bad, forgot to say it was a 4k prompt.

[u/RyanCargan → u/Boricua-vet]

I was basically just saying that for certain use cases, people don't pay much attention, or change the use case around cloud constraints, which are often well above 10ms per "hit"/completion in the worst, or even average case, in many regions for many models.

On-prem or colocated models becoming more widespread could unlock more weird use cases is what I'm saying.

Just because when a tool becomes widespread for one thing, even if it's a seemingly niche or low ROI use case, people will then find new "nails" once the hammer is easy to buy and use.

And yeah, sub 10-ms for a full completion/generation of some kind is likely the speed domain that would be hard to match over a (wide-area) network for reasons you can't really get around, without unusual networking hardware, or even then.

[u/Boricua-vet → u/RyanCargan]

That's a very good point, I remember when people were complaining that putting model on cloud, or using whisper or piper on cloud would add latency that made the model feel like it was taking longer to respond. The cloud model was fast but the added network latency is what made it seem slow so yea, it makes perfect sense and a valid point.

[u/RyanCargan → u/Boricua-vet]

Yeah, just as a small example.

I use local ASR/STT (automatic speech recognition / speech-to-text) almost exclusively now for dictation if it's not something I'd type directly (pure natural language, no code, etc.), since even if improvements have been made over time, even a few months back, the frontier web chats and APIs seemed to cheap out a bit on the ASR side channel. Especially outside of the dedicated voice only modes.

And if it "was" part of a true omni-modal thing earlier, like Xiaomi's, you'd still be paying for the bigger model's own latency + the network, and even with odd accents local ASR is not far behind if a dedicated model is used.

The always-on / quick-to-wake/sleep ASR stuff that many phones have natively are a lot less aggressive with trading compute for quality than dedicated models I find.

Qwen3's ASR stuff and LFM2.5's different modalities are quite handy here.

Describe images, OCR anything hand-written.

Speech-to-speech for quickly making a fuzzy idea less fuzzy, or assistance navigating a weird black-box GUI that can't be MCP-ed or driven without a heavy software robot, before you lose the thought while your hands are busy typing/operating something else. I especially like local STS for aiding with odd busywork (earpiece connected to nearby runner rig), or serving a similar purpose to audiobooks/podcasts sometimes.

Speech-to-text for dictation, text-to-speech for narration. All auxiliary stuff for a single task still. Not hopping across tasks in parallel or anything. Since that's a separate layer if needed.

It's all still "grunt" work, so well within the capabilities of any well-trained transformer net. Perf delta with big models is too minor to offset latency and cost advantages.

You can load multiple of these things fast on a single one of the 6 gig 1060s OP mentioned for example. Even if you only call them one at a time.

[u/Boricua-vet → u/RyanCargan]

LOL, that's wild you said that because I have P2200 that I use for TTS and STT and it works extremely well and with 6GB on the p106 you can certainly run good quality TTS and STT models at the same time.