Latest posts
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Bare metal vs cloud GPUs: who can see your data?
On a cloud GPU, the cloud layer can in principle read and copy your machine. Bare metal removes that layer, but you still trust the operator. Here is who can see your data, and what to ask before you rent.
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RTX PRO 6000 vs H100: which GPU is right for your business?
The H100 is the faster GPU for training AI and for giving every user fast replies. The RTX PRO 6000 has more memory on one card and suits video, graphics and teams sharing a GPU. Here is how to choose.
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Renting GPUs: why going through FlexiCloud beats going direct
We partner with more than ten GPU providers in India, the EU and the US, so you do not have to. Here is what that gets you: better pricing, priority on scarce GPUs, live inventory across datacentres and regions,…
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Hopper vs Blackwell: NVIDIA’s GPU generations compared
NVIDIA’s Hopper (H100, H200) and Blackwell (B200, B300) generations compared: memory, architecture, interconnect, power and price, and a practical guide to which one actually fits your AI workload.
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Renting a multi-node B300 cluster: what to check before you commit
A B300 cluster is far more than a pile of GPUs. What to check before you commit: whether you need multi-node at all, the fabric and storage that decide performance, how reserving allocation safely works, realistic lead…
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Questions to ask a GPU provider before you sign
More GPU providers than ever, and many resell capacity they do not own. The questions to ask any provider before you sign: who owns the hardware, the config, the fabric, proof it works, the datacentre, and the one…
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NVLink, NVSwitch and InfiniBand: the fabric that makes eight GPUs act as one
Buying GPUs is easy; making eight of them act as one is a question of fabric. How NVLink and NVSwitch scale up inside a node, how InfiniBand scales out between nodes, why a GPU that waits is money burning, and why a…
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Will your model fit? A practical guide to GPU memory for AI
Whether your model fits is arithmetic, not a benchmark. The bytes-per-parameter maths for inference and training, the levers that shrink it (quantization, LoRA, checkpointing), and a back-of-envelope guide to size any…
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Rent GPUs, buy your own, or use an API? A guide for teams building on AI
Pay-per-token API, buy your own GPUs, or rent bare-metal nodes? The right answer is about utilisation and control, not hype. A fair look at all three, and a simple way to decide which your workload actually needs.
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What it costs to fine-tune a model on an 8-GPU B300 node
The cost of a B300 job is GPU-hours × the rate, nothing more. Here is the actual arithmetic, three worked examples from a short LoRA run to a two-week training job, and an honest note on when a node is the wrong tool.