Waizkhan·3d agoA Deep Dive into Two Philosophies of AI Inference OptimizationA few weeks ago, I thought I had a fairly simple picture of AI inference optimization: quantize models, write faster kernels, and use the…
InOpen IntelligencebyDr. Fadi Shaar·Oct 24Uzu vs. llama.cpp: How One Inference Engine Reached 92 Tokens per Second on Apple SiliconA reported benchmark on a base M5 MacBook Pro puts Uzu far ahead of MLX and llama.cpp on Qwen3.5–9B, raising questions about what optimized…
Shivank Chaudhary·Oct 178x Faster LLM Cold Starts on Kubernetes With NVIDIA Dynamo Snapshot, your guide to scale to 0 and…Testing NVIDIA Dynamo Snapshot on H200s: the numbers, what broke, and where it stops helping.
Gnana Prasath·Oct 16685.9 Tokens per Second: Is TokenSpeed a vLLM Alternative?Inside the Qwen3.8-Flash-Next benchmark, and the engineering that determines how fast an AI application feels.
InToward Next AIbyAnna Jey·Oct 7Private AI Inference Architecture: How to Protect Prompts, Outputs, and Tool DataA model call is not just text sent to an API. It can carry customer records, retrieved documents, tool arguments, hidden instructions, and…
InOpen IntelligencebyDr. Fadi Shaar·Oct 4What If a Gaming PC Could Run a Trillion-Parameter AI Model?Inside Colibri, a pure-C inference engine that streams giant Mixture-of-Experts models across VRAM, RAM, and disk
Inazhar labsbyazhar·Oct 3GPU Kernel Engineering From First PrinciplesWhat really happens between one line of Python and thousands of GPU threads
InLevel Up CodingbyAnkit Goel·Oct 1How vLLM Stops Wasting GPU MemoryWhen you serve an LLM, the model weights aren’t what limits how many users fit on a GPU. The KV cache is.
Vinay Badhan·Sep 21Inference Engineering: The Part of AI Systems Nobody Talks About EnoughMost discussions about generative AI focus on models.
