…The result is remarkably better utilization. Instead of prematurely spilling data into slower system memory, the GPU holds onto critical assets longer, allowing it to operate closer to its full potential. In…
…describes as "perfect downstream results across all benchmarks" in needle-in-haystack tests. The blog also claims up to eight-times speedup in attention computation on H100 GPUs. At the lossless 3…
…That's the full-fat model, which would normally need a $10,000 GPU to run on locally. Related Your old GPU can still run big LLMs – you just need the right…
…the proprietary userspace and the kernel module source for the GPU. For the GPU, Radxa ships the kernel module as portable source with Linux version code checks up to 6.8, so…
…can actually go for anyone with a smaller GPU. Related I ran the same prompts through Claude and my local LLM, and the results weren't what I expected I got my…
…However, attempting to use file-sharing protocols for low-latency tasks like gaming just doesn’t deliver proper results. Besides massively inflating the load times, file-sharing protocols would cause certain titles…
…Related I let the NVIDIA App overclock my GPU, and it left me impressed For a "conservative" OC tool, the NVIDIA App gave me remarkable results PCIe lane limitations became annoying faster…
…variant for your specific GPU and workload, only to have ten different people tell you two (or three, or five) different things. That's because most benchmarks and first-hand write-ups…
…When purpose-built coding models started appearing online with benchmark scores that piqued my interest, I decided to try some out. The model I've stuck with is Qwen2.5-Coder 32B…
…My Bachelor’s thesis was conducted on the viability of benchmarking the non-functional elements of Android apps and smartphones such as performance, and I’ve been working in the tech industry…
