Exploring the bleeding edge of AI Agents, Gemini 3.0, and the hardware that powers them.
How can you make an 8B or 14B model run up to 2.5x faster without changing precision or losing accuracy? We break down the draft-and-verify mechanics of speculative decoding.
We benchmarked CPU-only local LLM inference across Intel Core Ultra 5, 7, and 9 systems. The results reveal where modern x86 chips excel—and where memory bandwidth hits a wall.
We tested an asymmetric dual-GPU setup (RTX 5070 + 5060) on the Acer Predator Orion. The results reveal a massive bottleneck hiding inside your PC.
A deep dive into the hidden memory hog of local AI: the KV Cache. See why pushing context windows causes high-end consumer GPUs to fall off a performance cliff.
We crunch the numbers on Claude, ChatGPT, and Gemini's basic tiers. How long does an RTX 4060 take to pay for itself?
We tested the Mac Mini M4 against the RTX 5070. The results change how we think about 'Always-On' AI.
Why the RTX 4060 and 5060 have become the only viable entry points for modern LLMs.
Reasoning models were supposed to be slow and cloud-bound. We tested the 8B distilled version on a laptop, and the results are shocking.
Conventional wisdom says larger contexts slow you down. Our lab data proves that for modern silicon, the opposite is true.
We tested a $300 laptop GPU and a card from 2016. The results prove that the barrier to entry for local AI has purely collapsed.
We are launching a dedicated hardware lab to benchmark everything from Apple Silicon to RTX 50-series laptops. Our first finding? The M4 Max defies the laws of scaling.
The era of floating-point arithmetic is ending. Enter BitNet b1.58 and the ternary weight revolution that turns multiplication into addition.
DRAM prices are up 50% and consumer GPU supply is tightening. We explore the "zero-sum game" between hyperscalers and local LLM builders.
Why the shift from training to inference is the defining moment of 2026, and what it means for hardware enthusiast.