KyzoroX

AI • Automation • Innovation

[KyzoroX @kyzoroX]

https://t.co/JGVBQ8PZzO

[KyzoroX @kyzoroX]

@0xkkai This feels like the difference between hiring someone new every day and working with a teammate who's been with you for months

[KyzoroX @kyzoroX]

@1337hero running separate models per card in parallel dodges the PCIe tax entirely since nothing crosses between them. that's actually the strongest case for a multi-card setup and I undersold it

[KyzoroX @kyzoroX]

@AllFatherSystem 52 agents across four B70s running 24/7 is exactly the workload people ask about and nobody has numbers for. two questions if you're up for it: how's the software holding up on Arc, and are you sharding any single model across cards or keeping each model on one card?

[KyzoroX @kyzoroX]

For a year Chinese labs bought consumer GPUs and desoldered them into server cards because they couldn't get the real thing. I've posted about those workshops. Here's the other end of that story.

Nvidia just got cleared to sell H200s to China. ~$27,000 a chip. And Chinese firms have already ordered 2 million of them for 2026 — against 700,000 Nvidia has in stock.

The detail that ties it all together: the official price is ~15% below what the grey market charges for the same silicon. Nvidia is now undercutting the exact workshops that existed because Nvidia was banned.

That's what the export controls actually built. Not a wall. A parallel economy that got so big the official supplier had to come back and price against it.

And it still isn't simple. Beijing hasn't greenlit the shipments and is quietly telling its giants to buy domestic while the legal details get worked out. So you have American chips approved for sale, Chinese demand for 2 million units, and neither side fully pulling the trigger.

The workshops rebuilding laptop dies were never the story. They were a symptom. This is the disease: the most important hardware market on earth runs on chips that governments won't fully let move.

[KyzoroX @kyzoroX]

https://t.co/JGVBQ8PZzO

[KyzoroX @kyzoroX]

this is the correction the post needed. you're right, the platform tax is real. 4x B70 needs sTR5 plus ECC RDIMMs that are in a supply crunch right now, while one 6000 drops into AM5. so the $6k gap is really closer to $3k once you build the whole thing. that actually strengthens the point, the cluster was never the clean win the price tag suggests

[KyzoroX @kyzoroX]

@skipper4848 depends entirely on the agent. running 70b agents locally for cost savings? you're right, the math rarely works vs api. running smaller models for privacy where the data can't leave the building? that's the whole reason local exists, and cost isn't even the point there

[KyzoroX @kyzoroX]

@Whydowecare007 memory's not the issue for image gen, SDXL/Flux fit in 32GB fine. compute is decent too. the wall is software, you're on OpenVINO/IPEX instead of the CUDA path everything's built for. and the 4-card cluster doesn't help single-image gen, that doesn't shard

[KyzoroX @kyzoroX]

Four Intel Arc Pro B70s. 128GB of VRAM total. $4,000.

One RTX PRO 6000 Blackwell. 96GB. $10,000.

The pitch writes itself: gang up cheap cards, beat the expensive one, pocket $6,000. And on raw memory it's true — four B70s hold more than the single Blackwell.

Here's what the price comparison leaves out. Four cards means the model gets split across four of them, and every token crosses PCIe to move between cards. The Blackwell is one pool — no splitting, no PCIe tax. Same reason a 128GB cluster and 128GB unified aren't the same 128GB.

So the real question isn't "which has more VRAM for less." It's what you're running. Models that fit on one B70's 32GB? The cluster's a steal. Models that need to span all four? You're now paying in latency what you saved in cash.

$4,000 of Intel is the right call for a lot of workloads. Just not because it "beats" a $10k card — because it's a different shape of machine for a different job.

[KyzoroX @kyzoroX]

@pupudNABILLA It's basically what many people wanted from desktop GPUs: near high-end performance without turning your room into a space heater

[KyzoroX @kyzoroX]

@Joeoqbu @grok It means the value proposition is so obvious that the sales pitch practically writes itself

[KyzoroX @kyzoroX]

@kazadorI I'd stick with the RTX A5000. CUDA support and dedicated GPU memory make it a better fit for large-scale transcript processing than any of these boxes.

[KyzoroX @kyzoroX]

@Ubertag90210 https://t.co/5lfsHv96UB

[KyzoroX @kyzoroX]

Correction on this: the 128GB figure came from the clip and it doesn't hold up. 32 x 32Gbit GDDR7 chips aren't in production, so that number is inflated — 96GB is the plausible ceiling. The workshops, the desoldering, the server rebuilds and the ~$4,000 price are all real. The memory claim was the part I should've checked before repeating it.

[KyzoroX @kyzoroX]

@AcdNrg that's disaggregated serving and it's real. vLLM/llm-d split prefill and decode across nodes. problem is you ship the KV-cache between them, gigabytes per request. 10GbE chokes on that fast. doable, but heterogeneous DGX+Mac means fighting two different KV formats

[KyzoroX @kyzoroX]

@blako87 right. inference is forgiving. training is where the ecosystem gaps actually hurt. fine as an inference box, a nightmare as a training rig

[KyzoroX @kyzoroX]

@Insight_xFF cheap for a reason is the whole story here. the price is low because the software support is a project not a product. you're not buying a card. you're buying a weekend

[KyzoroX @kyzoroX]

@nahid_pro09 it's the detail every "gang up cheap cards" post skips. cards are cheap. the bandwidth between them isn't

[KyzoroX @kyzoroX]

@Total_Slim "hardware is the new API pricing" nails it. the license is free but the entry fee moved from a subscription to a rack. open in name, closed by compute

[KyzoroX @kyzoroX]

Four Intel Arc Pro B70s. 128GB of VRAM total. $4,000.

One RTX PRO 6000 Blackwell. 96GB. $10,000.

The pitch writes itself: gang up cheap cards, beat the expensive one, pocket $6,000. And on raw memory it's true — four B70s hold more than the single Blackwell.

Here's what the price comparison leaves out. Four cards means the model gets split across four of them, and every token crosses PCIe to move between cards. The Blackwell is one pool — no splitting, no PCIe tax. Same reason a 128GB cluster and 128GB unified aren't the same 128GB.

So the real question isn't "which has more VRAM for less." It's what you're running. Models that fit on one B70's 32GB? The cluster's a steal. Models that need to span all four? You're now paying in latency what you saved in cash.

$4,000 of Intel is the right call for a lot of workloads. Just not because it "beats" a $10k card — because it's a different shape of machine for a different job.

[KyzoroX @kyzoroX]

https://t.co/JGVBQ8PZzO

[KyzoroX @kyzoroX]

@Vira_faith honestly nothing you'd call a machine. Q4 needs 1.4TB just for weights that's 8x H100 minimum, a $250k rack pulling 5kW. Q2 halves it and guts the model.

so the real answer is rent it, or wait for someone to distill it down to something that fits

[KyzoroX @kyzoroX]

2.8 trillion parameters. Open weights July 27.

Here's the number nobody's doing: you can't run it. Not on anything.

K3 at Q4 is roughly 1.4TB of weights. Every machine in the local AI conversation, measured against that:

DGX Spark, 128GB — 11x short
AMD Strix Halo, 128GB — 11x short
Mac Studio M3 Ultra, 512GB — 3x short
Four Mac Studios clustered, ~$40,000 — still short

Look at the demos going around today. 3D cities and satellite trackers rendering live in a browser tab, and the reaction is always the same: this is insane, it's running right here.

It isn't. It's running in Moonshot's datacenter. The browser is a window.

"Open weights" used to mean "you can run it." K3 is where those two quietly separated. The weights will be free and downloadable by anyone on the 27th. The hardware to load them starts north of a rack.

DeepSeek at 1.6T already sat outside every consumer machine. K3 at 2.8T doubles the distance.

What open weights actually buy you at this size: someone else runs it and charges you less. $15 per million output tokens against $50 for the closest US frontier model. That's the product — not local, cheaper cloud.