Wholesale GPU clusters. ~30% lower cost than hyperscaler.
B200, H200, H100, A100 — available now. Quotes usually within a few hours.
DM us or click the link → https://t.co/Vif65nWKSS
B200 is coming to packet•ai - our sister platform on hosted·ai.
For startups, ML engineers & small teams who need B200 without minimums or procurement cycles:
Dynamic: $3.75/GPU/hr
Dedicated: $5.90/GPU/hr
Waitlist open. Going live soon - https://t.co/QE3FmJqcRg
H100s that sold for $40,000 in 2023 now go for $6,000 to $12,000.
Average enterprise GPU utilization: 5%.
Depreciation runs whether the GPU is working or not.
Here is what enterprises with idle clusters are doing.
https://t.co/SMG64bsTRt
Right-sized GPUs, cut the bill 35%. Then someone asked why they still paid $6.88/hr for an H100 when the same chip cost $2.50 elsewhere.
Right-sizing and provider switching stack. $14,500/mo became $3,200.
https://t.co/Q0rPrwnNj1
"Wholesale GPU isn't enterprise-grade."
Every provider in the GPUaaS•com network is vetted: uptime, SLA, performance, support. Before appearing in a single result.
DGX-class hardware. Same NVIDIA silicon. ~30% below hyperscalers rates.
Browse clusters:https://t.co/dbkkLlhvEW
First invoice was 38% above the negotiated H100 rate. Nothing hidden. Egress, storage, regional multipliers. All in the contract.
Same H100: $1.49/hr spot to $6.98/hr Azure. Most inventory pre-sold before reaching on-demand pools.
https://t.co/NmyhEjZQoG
Half the providers SemiAnalysis called in March were sold out. Most had nothing coming off contract.
H200 lead times: 36 to 52 weeks. H100 contracts up 15 to 20% month on month.
What exists right now won't be there next month.
https://t.co/OsydoBEjSa
95% of enterprise GPU capacity is sitting idle.
Cast AI: 23,000 clusters. Average GPU utilization: 5%.
At 5% utilization, $6.88/GPU/hr becomes $137.60 per hour of actual work.
H100s that sold for $40K in 2023 now trade for $6K to $15K .
https://t.co/fU78ZxaeEv
Your GPU quote is not your GPU price.
AWS quotes you $6.88/GPU/hr. That number includes 192 vCPUs, 2 TB RAM, and 30 TB NVMe you are not using.
Add egress, storage, and region premiums. Real bill lands 20 to 40% higher.
Full breakdown: https://t.co/XaLXF0lhM7
For two years, winning meant securing GPUs.
In 2026, it means squeezing them. The moment usage-based billing hits an idle stack, cost-per-token becomes an emergency.
Match workload to capacity. Stop hoarding.
https://t.co/qEENH4ksVa
Still hunting for H100s?
We have 12 nodes available , ready to deploy today.
Location : APAC
Specs
Price: Under $2/hr (12-month term)
GPU: 8x NVIDIA H100 SXM5 80GB
CPU: Dual AMD EPYC 9654 (64 cores each)
RAM: 1.5TB DDR5 ECC
Boot storage: 2x 1TB NVMe SSD
Local data storage: 2x 2TB NVMe SSD
GPU interconnect: 8x 400Gbps ports
Storage / cluster network: 4x 100Gbps ports
Internet: 2x 10Gbps ports
Cooling: Advanced air cooling
DM us or click on the link below to get a quote.
Get quote - https://t.co/Kjsqedg2hP
"If it's cheaper than a hyperscaler, what's the catch?"
No catch. The savings come from higher utilization, not worse hardware. Same GPU. Same performance. Less waste in the bill.
Get your Quote here -> https://t.co/sYTs08r93t
Same NVIDIA silicon as the hyperscalers. Same performance. A lower bill.
The difference isn't the hardware. It's how efficiently it's run - and that efficiency gets passed to you.
Get your Quote here -
https://t.co/nn6uSAsvTw
The gap between "we need GPUs" and "we have GPUs" is where AI roadmaps stall.
One requirement in, competing quotes back - usually in a few hours. Not days of vendor ping-pong.
Get Quotes here -> https://t.co/GQRsZEO5qj
Two H100 SXM clusters open. Two different planning horizons.
→ 3 nodes in London. Ready to deploy today.
→ 64 nodes in Portugal. Open for September commits, deploys Sep 1.
Identical spec across both:
8x H100 80GB SXM per node, 3.2 Tbit/s InfiniBand, 2TB RAM, multi-tier NVMe.
London for the team that needs capacity now. Portugal for the 90-day procurement cycle most teams actually operate on.
DM us or click the link → https://t.co/yH40elhWa8
The 80-GPU H100 cluster you're scoping doesn't get cheaper while you wait two weeks for a hyperscaler quote.
It just gets two weeks later to deploy.
10+ H100 SXM nodes live in APAC right now. ~30% below hyperscaler reserved rates. Quotes from us in hours.
If you're mid-cycle on a hyperscaler quote, run a parallel one. Worst case you confirm their number.
DM us or click the link → https://t.co/HqbBCc3Rwr
Most AI teams pick a GPU billing model the same way they pick a SaaS plan: "monthly or annual?"
That framing breaks at scale.
PAYG, reserved, and spot aren't pricing tiers. They're risk transfer mechanisms.
→ PAYG: you absorb pricing risk, provider absorbs utilization risk
→ Reserved: you absorb utilization risk, provider gives you pricing certainty
→ Spot: you absorb availability risk, provider gives you the floor price
The mistake isn't picking the wrong tier. It's not knowing which risk you're trading away.
Finance teams scoping H100 or H200 — worth a parallel quote from us before locking in.
DM us or click the link → https://t.co/TB39Q7ozb7