A paper from Google could make local LLMs even easier to run.

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in ...

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

Write-through: all cache memory writes are written to main memory, even if the data is retained in the cache, such as in the example in Figure 4.11. A cache line can be in two states – valid or ...

Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), ...

One of the greatest challenges facing the designers of many-core processors is resource contention. The chart below visually lays out the problem of resource contention, but for most of us the idea is ...

When talking about CPU specifications, in addition to clock speed and number of cores/threads, ' CPU cache memory ' is sometimes mentioned. Developer Gabriel G. Cunha explains what this CPU cache ...

The year so far has been filled with news of Spectre and Meltdown. These exploits take advantage of features like speculative execution, and memory access timing. What they have in common is the fact ...

System-on-a-Chip (SoC) designers have a problem, a big problem in fact, Random Access Memory (RAM) is slow, too slow, it just can’t keep up. So they came up with a workaround and it is called cache ...