programmerChilli

I'm planning on going with some additional people and need to find a different section. My current tickets are lower bowl, resale price would probably in the 10-20k range each.

Message me for more details!

There seems to be a lot of confusion about how Nvidia could be selling their 5090 with 32GB of VRAM, but their Project Digits desktop has 128 GB of VRAM.

Typical desktop GPUs have GDDR which is faster, and server GPUs have HBM which is even faster than that, but the Grace CPUs use LPDDR (https://www.nvidia.com/en-us/data-center/grace-cpu/), which is generally cheaper but slower.

For example, the H200 GPU by itself only has 96/144GB of HBM, but the Grace-Hopper Superchip (GH200) adds in an additional 480 GB of LPDDR.

The memory bandwidth to this LPDDR from the GPU is also quite fast! For example, the GH200 HBM bandwidth is 4.9 TB/s, but the memory bandwidth from the CPU to the GPU and from

TL;DR: Perhaps surprisingly, Batter's Eye is the most important stat for batters

After running some experiments for pitchers (and coming to the conclusion that Control was the best stat for pitchers), I wanted to do some controlled experiments for batters as well.

My methodology is pretty similar to the other post. i would sim several seasons with the same team, but with a different stat allocation on the batters.

Here, the main question I wanted to know is: Is Contact, Power, or Batter's Eye the best stat to focus on for batters?

Stat Allocation Strategies Tested

1. Contact Only
2. Power Only
3. Batter's Eye Only
4. Contact/Power Split
5. Contact/B. Eye Split
6. B. Eye/Power Spl

I see wrestling in general starts at 2am pst but the Kennedy Blades match is last.

The 4x100 W was not so clean either, but Shacarri pulled through. The men’s handoff though…

TL;DR: Control is the most important stat for pitchers.

I've always been curious what the right stat allocation is - there's a lot of anecdotal opinions on this subreddit - but I haven't seen any controlled experiments being done. So I thought I would do some.

I wanted to see several simmed seasons with nearly the same team, but with some stat reallocated in some manner.

The first one I decided to test is pitching. In particular, how do the various stats (Control vs. the Pitch specific stats) affect the performance of the pitcher?

Setup

I have a team playing in Champion 3 with 5 pitchers, generally around 60-70 overall rating. Each pitcher has the same 3 pitches: Fastball, Forkball,

Hey folks, we released gpt-fast last December as a hackable "tutorial" implementation of sorts that achieves SOTA decoding performance for text generation.

Since then, we also recently added a Mixtral implementation to gpt-fast as well. Check it out here: https://github.com/pytorch-labs/gpt-fast/tree/main/mixtral-moe

Featuring

• (!) no custom kernels
• int8 and tensor-parallelism support
• still very simple (<150 LOC to support)
• faster decoding than any (non-Groq) API endpoint, at up to 220 tok/s/user.

I also wrote a longer-form explanation of the challenges involved here: https://th

Hey folks, we released gpt-fast last December as a hackable "tutorial" implementation of sorts that achieves SOTA decoding performance for text generation.

Since then, we also recently added a Mixtral implementation to gpt-fast as well. Check it out here: https://github.com/pytorch-labs/gpt-fast/tree/main/mixtral-moe

Featuring

• (!) no custom kernels
• int8 and tensor-parallelism support
• still very simple (<150 LOC to support)
• faster decoding than any (non-Groq) API endpoint, at up to 220 tok/s/user.

I also wrote a longer-form explanation of the challenges involved here: https://ch

We're happy to release GPT-Fast, a fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!

Check out the blog post describing the techniques here: https://pytorch.org/blog/accelerating-generative-ai-2/

And check out the code here: https://github.com/pytorch-labs/gpt-fast

To be clear, this is intended more as a minimal "tutorial" of how you get really good inference performance rather than a library. Hopefully y'all find it useful!

Happy to answer any questions.

Ever since 2020, we've had these (east) asian led movies nominated for best picture every year, along with an asian director.

• 2020: Parasite (best picture winner)
• 2021: Minari, Nomadland (non-asian cast)
• 2022: Drive My Car
• 2023: Everything Everywhere All at Once (best picture winner)
• 2024: Past Lives (likely)

Prior to 2020, we have only...

• 2001: Crouching Tiger, Hidden Dragon

Relaxing one of the constraints, we have

• 2005: Brokeback Mountain (non-asian cast)
• 2006: Letters from Iwo Jima (non-asian director)
• 1987: The Last Emperor (non-asian director)

Expanding to south asian, that adds 2012: Life of Pi (asian director and cast) and 2009: Slumdog Millionaire (non-asian

Any particularly shocking divergences folks remember?