packet.ai

🔥 Thursday's GPU Take Before buying a bigger GPU for inference, test whether quantization could get the job done on your current hardware. Lower memory requirements can change the economics, but model quality and serving performance still need to hold up. Would you test quantization before upgrading?

Got H100s? Good. Now, what kind? SXM, PCIe and NVL are not interchangeable. Neither are the systems they're built into. Know before you list. packet.ai/blog/got-h100s-wha…

Your Black Friday GPU deal just got bigger. Run NVIDIA B200 at $3.75/GPU-hr and get an extra $50 in credits in your wallet. Here's the deal: 💰 Top up $100 → Get $50 FREE in your wallet 🚀 Deploy B200 or any other gpu by the hour based on your workload. Scale when you need it. No long-term commitment. Just powerful Blackwell GPUs at a compelling rate. Don't let this Black Friday opportunity pass you by. 👉 Get started: dash.packet.ai/ Limited-time offer. Minimum $100 top-up (One-time only). #BlackFriday #NVIDIAB200 #GPUCloud #AIInfrastructure #PacketAI #Blackwell

💸 GPU Bill Rescue You move a workload from one GPU to two. Runtime drops by just 10%, but GPU spend nearly doubles. What would you investigate before deciding whether the second GPU is worth it?

Buying the GPU is the straightforward part. Pricing it right, keeping it online, meeting verification requirements and making the economics work? That's where new GPU hosts get caught out. 7 operational mistakes worth avoiding: packet.ai/blog/gpu-hosting-m…

🧭 Tuesday's GPU Choice Your model needs more than 48GB of VRAM but fits on one 96GB GPU. Would you use one larger GPU or split the workload across two 48GB GPUs? Which would you test first, and why?

Before connecting your GPUs to a hosting platform, ask what you're giving it access to. Your machines, your network, your data, your liability. We've put together 12 questions GPU providers should ask before listing their capacity. packet.ai/blog/gpu-provider-…

38 providers were already listing B300 capacity on one tracker by October 28. The early-mover window is getting smaller. Buyers can compare providers, prices and availability. Owning the GPUs is one thing. Having the infrastructure to put them to work is another. What B300 providers need to know: packet.ai/blog/got-b300-why-…

🔥 Thursday's GPU Take A GPU that wins your benchmark can still lose you money in production. A benchmark might reward peak throughput. Your production workload might be constrained by latency, concurrency, memory pressure or the cost of completing each job. Winning the benchmark and winning in production aren't necessarily the same thing.

Got H200s? The real question is who needs that memory, and what they'll pay for it. H200 vs H100, rental prices, and where inference demand is heading: packet.ai/blog/got-h200s-whe…

💸 GPU Bill Rescue Your expensive GPU spends a significant amount of time waiting for data from the CPU or storage. The team wants to upgrade to a faster GPU. Would you make the switch, or investigate the bottleneck first? What's your take?

Installed the GPUs. Collected the payment. End of story? Not necessarily. MSPs and integrators have several ways to build a revenue stream beyond the initial sale. The right model depends on what they're willing to own. packet.ai/blog/how-msps-and-…

🧭 Tuesday's GPU Choice Two GPUs look suitable for your workload. One has slightly stronger specs. The other has mature support for your framework and libraries. Which would you test first, and why? Hint: Hardware is only part of the decision.

A GPU can be good collateral and still be a repayment risk. For lenders, the key isn't just whether a GPU is busy. It's whether that activity generates billable revenue, and whether the contracts can sustain repayment. What to monitor, what to ask, and where idle capacity matters: packet.ai/blog/gpu-financing…

NVIDIA A100 PCIe at $0.89/hr. NVIDIA B200 at $3.75/hr. Both live on packet•ai. Dynamic or Dedicated. No contracts, billed by the hour. dash.packet.ai/compute?utm_s…

🧠 Monday's GPU Note Your model fits in VRAM. Increase concurrency, and suddenly you're running out of memory. Same model. Same precision. Different serving load. How do you account for concurrency when sizing VRAM for inference?

Your workload needs fewer GPUs. Your commitment still stands. Some GPU commitments can't be cancelled. Others have narrow resale or refund routes. Six ways to deal with excess capacity, including what AWS, Azure and Google Cloud actually allow: packet.ai/blog/oversubscribe…

🔥 The A100 just landed on Packet.ai. And we're starting at $0.89/GPU-hour. One of the most proven GPUs in AI is ready for your next: → Training run → Fine-tuning job → Inference workload → Research project → HPC workload No waiting for hardware. No infrastructure to build. Just serious compute, ready when you are. A100 is available now. Deploy Now -> dash.packet.ai/compute

💸 GPU Bill Rescue Your overnight GPU job ran for 8 hours. The GPU was available throughout, but far fewer jobs completed successfully than expected. What do you think could be going wrong? How would you read this situation?

Rack rent or GPU revenue? One gives you a lease. The other gives you a GPU fleet to finance, operate and keep billable. Our model compares fixed rent, revenue share and running GPUs yourself, with the assumptions laid out. packet.ai/blog/colo-owners-m…