Compute is the new capital: why AI startups raise GPUs, not just cash
For AI startups, access to GPUs can matter more than money. How cloud deals, credits and chip reservations shape who can build frontier models.
AI Desk1 min read
Developers & Builders
MLOps & Infrastructure
Serving, cloud, GPUs and the cost of inference.
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Local & On-device AIGuide
Quantization explained: how big AI models run on small hardware
Quantization shrinks AI models by storing weights with fewer bits. How 8-bit and 4-bit formats work, GGUF, GPTQ and AWQ, and the quality trade-offs to expect.
AI Desk3 min read
How LLM inference serving works: batching, KV cache and vLLM
What happens when a server answers thousands of LLM requests: prefill and decode, the KV cache, continuous batching, PagedAttention and the metrics that matter.
AI Desk3 min read
How much GPU memory does an AI model need? A sizing guide
Estimate the GPU memory needed to run or fine-tune a language model: weights, KV cache, activations and overhead, with worked examples for 8B and 70B models.
AI Desk3 min read
How AI is used in telecom networks
Self-optimizing networks, energy savings, predictive maintenance, fraud and spam detection, customer care and AI-RAN: how telecom operators use AI, and what 6G may bring.
AI Desk3 min read