#Inference

VKORE-325M was pretrained from random init on a Rebellions ATOM-Max NPU. On unseen text it scores 0.7993 vs 0.7994 for the same model on a B200 GPU.

Edge AI view of VKORE-325M: pretrained from zero on a Rebellions ATOM-Max inference NPU, matching a B200 GPU twin on unseen text (0.7993 vs 0.7994).

AI-assisted disclosure: Codex assisted with research, code and writing. Measurements, failure cases...

The right GPU cloud depends on four things: the workload (steady inference, bursty inference, or...

AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns…

DigitalOcean gives new customers a free \$5 credit, with no credit card required, through its Managed...

While investigating why quantized `llama.cpp` inference pods behind a FastAPI gateway on a 3-node `kind` cluster were experiencing sudden P95 latency spikes (500ms jumping to 30s) and HTTP 502/504 timeouts, I ran a controlled benchmark across 64…

Learn how LLM inference works, the differences between prefill and decoding stages, and how to optimize model performance for faster response times.

Cut inference spend by 98%: Learn how to bottle frontier reasoning trajectories into low-VRAM 14B models with rejection sampling and… Continue reading on Predict »

Your AI cost model stops at the token price, but the bill doesn't. Discover why almost 80% of production AI spend sits in inference and how to optimize your setup.

The cloud giants are selling an architectural illusion. Discover why centralized clouds can’t scale real-time AI agents and why inference must move to the edge.

While 75% of enterprises run GenAI, 42% of API incidents target AI. Discover why Akamai’s 2026 research proves infrastructure and security must converge.

Discover why modern applications are all part of the AI ecosystem, how real-time inference drives performance, and how to avoid hidden data transfer costs.

Enterprises need economically scalable capacity to keep pace with the data created by AI inference.

Google open-sourced TorchTPU, its native PyTorch stack for TPUs, and released llm-d 1.0, a Kubernetes-native distributed inference stack co-founded with Red Hat, IBM and others, at PyTorch Conference 2026.

Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads.

Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads.

Inference is reshaping AI economics, infrastructure architectures, and investment priorities across the semiconductor ecosystem.

Illustrative photograph by İsmail Enes Ayhan on Unsplash. On 12 October 2026, Microsoft announced...