Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

the flashback on the new Cache

NAVI

836,898 subscribers

285,756 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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

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

Razed Esports

81,942 views • 4 months ago

FLASHBACK: Hulk Hogan jumps in on the Trump dance.

FLASHBACK: Hulk Hogan jumps in on the Trump dance.

Fox News

47,418 views • 1 year ago

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

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

Inside Politics

11,968 views • 1 month ago

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

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

MARIA

160,665 views • 3 years ago

New way to Cache self boost

New way to Cache self boost

psp1g

247,895 views • 3 months ago

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!

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!

Call of Duty: Mobile

39,826 views • 1 month ago

The FlashBack

Sensitive content

The FlashBack

Rᴀᴠɪ ᴠᴀ₹ᴍᴀ™

304,201 views • 1 year ago

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

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

Ozzny

452,016 views • 4 months ago

need new cache for this potential clip

need new cache for this potential clip

psp1g

174,760 views • 1 year ago

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:

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:

Rockstar Games

2,279,070 views • 3 years ago

There’s a way to wallbang on Cache from the B site to enemies on A, found by u/PlanktonLatter6762

There’s a way to wallbang on Cache from the B site to enemies on A, found by u/PlanktonLatter6762

Counter-Strike News

322,833 views • 1 year ago

the flashback 😢😢

the flashback 😢😢

angry momonga

108,483 views • 1 year ago

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

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

Kami🧢

870,027 views • 3 years ago

Flashback to the best TV moment of the 2010s

Flashback to the best TV moment of the 2010s

Adam Feldman

618,548 views • 3 years ago

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

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

Groyper Clips

16,673 views • 12 days ago

XSE hosting the first pro cache game since 2019

XSE hosting the first pro cache game since 2019

psp1g

125,547 views • 2 months ago

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.

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.

max fu

70,797 views • 1 month ago