the flashback on the new Cache

no way wabii-sabi just hit the shroud play on... the new Cache 🤯show more

FLASHBACK: Hulk Hogan jumps in on the Trump dance.

❗️Flashback to Putin's words on the 24th of June... during the Wagner rebellion.show more

FLASHBACK: As the Iran war moves on, President Trump... keeps shifting the goalposts. #InsidePoliticsshow more

New way to Cache self boost

The FlashBack

An explosive Legendary comes to the Secret Cache. Collect... shards to unlock the new MG42 - High Octane and take your gameplay to the next level!show more

You can solo boost to shroud in the new... Cache ‼️ They made it very easy too, no need to use the doorshow more

need new cache for this potential clip

Take on a new entrepreneurial side hustle that's vital... to the city's lifeblood. Purchase your own Taxi from Warstock Cache & Carry, and visit the Downtown Cab Co. depot in East Vinewood to get cracking:show more

There’s a way to wallbang on Cache from the... B site to enemies on A, found by u/PlanktonLatter6762show more

the flashback 😢😢

on joue pas à cache-cache quand je prends le métro

Flashback to the best TV moment of the 2010s

Day 11/90 of Inference Engineering How does vLLM work... and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.show more

FLASHBACK: Nick Fuentes name was mentioned in front of... Charlie Kirk on the Whatever podcast😭show more

XSE hosting the first pro cache game since 2019