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Triple T Roblox Model Pack Price: $24.99/5,000 Robux a/t Includes 7 Models including 1 regular (ON SALE) DM —> mystery_0001

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TheMysteryPl4ys

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121,033 Aufrufe • vor 4 Monaten •via X (Twitter)

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DeepSeek R1 671B is ridiculously good, but running at 158 tokens per second is something out of this world. A model *this good* running *this fast* is 3 years ahead of this time. The secret: DeepSeek in this short video isn't running on GPUs. It's running on custom-made chips! This is running on SambaNova and their SN40L chips, which are designed and optimized for running AI workflows. SambaNova is sponsoring my work. We designed GPUs to run games and later realized we could use them for AI. This is very different from creating a chip specifically for AI from the ground up. Just think about this for a second: One single SNL40 chip can simultaneously hold 100+ models (trillions of parameters) in memory! An agentic workflow that uses multiple models simultaneously won't need to swap models from memory even once! By the way, the Llama family of models *screams* on the SambaNova Cloud. Here are some numbers from my latest tests: • Llama 3.3 70B: 367.32 t/s • Llama 3.2 1B: 2381.82 t/s • Llama 3.2 3B: 1335.71 t/s • Llama 3.1 8B: 929.58 t/s • Llama 3.1 70B: 369.16 t/s • Llama 3.1 405B: 103.37 t/s You can access all of these models for free!
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DeepSeek R1 671B is ridiculously good, but running at 158 tokens per second is something out of this world. A model *this good* running *this fast* is 3 years ahead of this time. The secret: DeepSeek in this short video isn't running on GPUs. It's running on custom-made chips! This is running on SambaNova and their SN40L chips, which are designed and optimized for running AI workflows. SambaNova is sponsoring my work. We designed GPUs to run games and later realized we could use them for AI. This is very different from creating a chip specifically for AI from the ground up. Just think about this for a second: One single SNL40 chip can simultaneously hold 100+ models (trillions of parameters) in memory! An agentic workflow that uses multiple models simultaneously won't need to swap models from memory even once! By the way, the Llama family of models *screams* on the SambaNova Cloud. Here are some numbers from my latest tests: • Llama 3.3 70B: 367.32 t/s • Llama 3.2 1B: 2381.82 t/s • Llama 3.2 3B: 1335.71 t/s • Llama 3.1 8B: 929.58 t/s • Llama 3.1 70B: 369.16 t/s • Llama 3.1 405B: 103.37 t/s You can access all of these models for free!

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market makers aren't quoting price they're running an inventory optimization formula, and every bid/ask you've ever traded against was its output formula is free, been public since 2008, and retail trading has never once mentioned it NYU professor Marco Avellaneda published the model - 14 pages, zero paywall, on SSRN reservation price: r = mid - inventory × γ × σ² × (T-t) when a desk holds too much of one side, they shift their entire spread to push flow the other direction. you were always a variable in that calculation 4 resources to run this yourself: > avellaneda & stoikov 2008 - "high-frequency trading in a limit order book" - free on SSRN > github: search "avellaneda stoikov python" - clean implementations under 200 lines > tardis - historical level 2 order book data, exact inputs the model runs on > lobsterdata - NASDAQ order book reconstructions, free academic access Bookmark this before placing another trade optimal spread: δ = γσ²(T-t) + (2/γ)ln(1 + γ/κ) γ = risk aversion, σ = volatility, T-t = time horizon, κ = arrival rate that second term - (2/γ)ln(1 + γ/κ) - is the profit extraction piece retail never sees you've never been on the right side of a spread - you've been the inventory problem they were solving What used to require manual calculations now takes just a few seconds with AI
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market makers aren't quoting price they're running an inventory optimization formula, and every bid/ask you've ever traded against was its output formula is free, been public since 2008, and retail trading has never once mentioned it NYU professor Marco Avellaneda published the model - 14 pages, zero paywall, on SSRN reservation price: r = mid - inventory × γ × σ² × (T-t) when a desk holds too much of one side, they shift their entire spread to push flow the other direction. you were always a variable in that calculation 4 resources to run this yourself: > avellaneda & stoikov 2008 - "high-frequency trading in a limit order book" - free on SSRN > github: search "avellaneda stoikov python" - clean implementations under 200 lines > tardis - historical level 2 order book data, exact inputs the model runs on > lobsterdata - NASDAQ order book reconstructions, free academic access Bookmark this before placing another trade optimal spread: δ = γσ²(T-t) + (2/γ)ln(1 + γ/κ) γ = risk aversion, σ = volatility, T-t = time horizon, κ = arrival rate that second term - (2/γ)ln(1 + γ/κ) - is the profit extraction piece retail never sees you've never been on the right side of a spread - you've been the inventory problem they were solving What used to require manual calculations now takes just a few seconds with AI

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Created to celebrate the record breaking run CeeDee Lamb is on….the 88s are coming together to launch an exclusive t-shirt collection that includes a chance to receive a 1-of-1 SIGNED CEEDEE LAMB 88 JERSEY. 👀🔥💙⭐️Link to purchase in the bio 👉🏿👉🏿

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