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@BebulgaBounty @patty_fi soon candles will break from screens 😎

@patty_fi $MONKE is the cook u looking for bro 🐒

Any cooks?

@BebulgaBounty @_Shadow36 monke is all u need <3

@_Shadow36 Ye better invest in tokens like $MONKE 🫡

Niggas will do all this dumb shit then blame a launchpad😭😭😭😭😭 I dont like everything bonk, pump, moonshot, fomo etc do and think they should emphasize more community shit But to blame them SOLELY for how WE behave is fucking asinine. Them niggas aint tell u to jeet and farm 10-30k coins all day

@BebulgaBounty @miragemunny

@miragemunny Exactly that’s why u should get urself some Monke 🐒

the price-action of the day is not the price-action of tomorrow, so stop trading like it is

@Solana_Monk3 @toly I guess $MONKE inflates this time up :)

@toly @toly ?????

MetaTimer: Using Large Language Models for Precise, Prompt-Aware Inference Latency Prediction The rapid proliferation of large language models (LLMs) in production systems has exposed a fundamental limitation: inference latency varies dramatically across prompts due to differences in semantic complexity, required reasoning depth, output length, and generation dynamics. Conventional prediction methods—ranging from token-count heuristics and hardware Roofline models to traditional machine-learning regressors—fail to generalize because they cannot capture these prompt-specific nuances. Accurate a priori estimation of processing time is essential for resource scheduling, dynamic batching, cost forecasting, service-level guarantees, and user-experience enhancements. We introduce MetaTimer, the first framework to repurpose a lightweight LLM itself as a high-precision meta-predictor capable of forecasting the exact wall-clock inference duration required by any target LLM for an arbitrary input prompt. A compact 8B-parameter model is fine-tuned on a massive corpus of millions of prompt–execution pairs collected across heterogeneous model families (GPT-4-class, Llama 3.1, Claude, Mistral), quantization levels, decoding strategies, and hardware accelerators. The predictor employs chain-of-thought reasoning to decompose prompt semantics, estimate output token distributions and reasoning trajectories, and integrate model- and hardware-specific performance profiles, yielding fine-grained predictions for Time-to-First-Token (TTFT), Time-Per-Output-Token (TPOT), and total latency. Extensive evaluations on held-out benchmarks spanning reasoning, creative writing, coding, and long-context tasks demonstrate state-of-the-art accuracy: a mean absolute percentage error (MAPE) of 6.3% for end-to-end latency—representing a >40% reduction in mean squared error relative to the strongest Roofline–ML baselines—and strong zero-shot generalization to unseen models and platforms. When integrated into production serving stacks (vLLM, TensorRT-LLM, Triton), MetaTimer delivers up to 31% gains in resource utilization and tail-latency reduction. These results establish that LLMs possess emergent capabilities for computational self-modeling, opening a new paradigm for self-aware, adaptive, and energy-efficient generative AI infrastructure. We publicly release the predictor model, dataset, and serving plugins to accelerate research in meta-performance modeling for frontier AI systems.

@Solana_Monk3 @NASA cya on da m🙉🙉n

$monke ready for a moon mission @NASA u ready?

@BebulgaBounty @MrBeast Monke too small on that pic 😅

@MrBeast To become full time crypto degen and ape $MONKE 👀🐒

I’m deleting my YouTube channel

@Solana_Monk3 @KITKAT Monkes dindu nuffin 🥸

@KITKAT we didn't took it.....

@BebulgaBounty @Dior100x Monkes and Bonkers 🦧

@Dior100x Coming for $MONKE 🐒

Millions and billions

@BebulgaBounty @3orovik Same pattern as BTC, soon moon

@3orovik Bottom is in. Time to grab more $MONKE 🐵 gqqPZaSV2UDmEi3dhBzA6u6x5ZTLAXPntHZ5gQ4pump

Imagine looking at the Bitcoin chart and not thinking that it has bottomed after dropping so much Once again, I predict the bottom is in

@BebulgaBounty @elonmusk I'm $MONKE and u know it 😎

@elonmusk $MONKE likes that 🐒

Tesla Supercharger energy delivered is growing exponentially