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The compute layer, explained from first principles, with the arithmetic shown, so you can check us.
12 min
The A100 in 2026: the best value in AI inference
Six years old and unbeatable on bandwidth-per-dollar, the arithmetic, what it serves, and when to skip it.
7 min
A100 vs H100 for inference
When the newer card earns its 2-3× price, and when it doesn’t, with the one-question shortcut.
9 min
A100 40GB vs 80GB: when half the price wins
Same silicon, half the cost, the workloads where the 40GB is the sharper buy, and the two-for-one math.
9 min
Renting vs buying GPUs: the decision framework
Break-even utilization, payback math on used A100s at live rates, the costs owners forget, and the dedicated-capacity middle path.
9 min
The second life of datacenter GPUs
Where retired hyperscaler fleets go, redeployment economics, and the diligence checklist for pulled hardware.
9 min
Gaudi 3: the accelerator nobody is fighting over
Intel lost the accelerator war, which is exactly what makes Gaudi 3 a bargain. The honest case and the earned risks.
10 min
Cutting inference cost per token
The seven levers that move $/token, ranked by impact, and the latency-vs-throughput split that decides your hardware.
8 min
CUDA cores vs Tensor Cores
The spec-sheet decoy and the units doing the real work, and how Tensor Core generations decide which models a GPU can serve.
8 min
The VRAM math every AI engineer should know
Weights, KV cache, overhead, the three numbers that decide whether a model fits, worked by hand.
10 min
Why your tokens/sec is 10× lower than the benchmark
Prefill vs decode, the memory-bandwidth wall, continuous batching, PagedAttention.
9 min
How one GPU becomes seven
MIG, vGPU, time-slicing, what isolation you are actually renting, and how to check.
9 min
VMs, containers, microVMs
The isolation spectrum AI clouds run on, from namespaces to Firecracker.
11 min
The AI compute stack, from sand to serverless
Eight layers between a transistor and your API call, and who takes margin at each.