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Model: "mamba-2"
Cerebras Inference: Faster, Better, AND Cheaper
llama-3.1-8b llama-3.1-70b gemini-1.5-flash gemini-1.5-pro cogvideox-5b mamba-2 rene-1.3b llama-3.1 gemini-1.5 claude groq cerebras cursor google-deepmind anthropic inference-speed wafer-scale-chips prompt-caching model-merging benchmarking open-source-models code-editing model-optimization jeremyphoward sam-altman nat-friedman daniel-gross swyx
Groq led early 2024 with superfast LLM inference speeds, achieving ~450 tokens/sec for Mixtral 8x7B and 240 tokens/sec for Llama 2 70B. Cursor introduced a specialized code edit model hitting 1000 tokens/sec. Now, Cerebras claims the fastest inference with their wafer-scale chips, running Llama3.1-8b at 1800 tokens/sec and Llama3.1-70B at 450 tokens/sec at full precision, with competitive pricing and a generous free tier. Google's Gemini 1.5 models showed significant benchmark improvements, especially Gemini-1.5-Flash and Gemini-1.5-Pro. New open-source models like CogVideoX-5B and Mamba-2 (Rene 1.3B) were released, optimized for consumer hardware. Anthropic's Claude now supports prompt caching, improving speed and cost efficiency. "Cerebras Inference runs Llama3.1 20x faster than GPU solutions at 1/5 the price."
Not much happened today
gemini-1.5-flashmodel gemini-pro mixtral mamba-2 phi-3-medium phi-3-small gpt-3.5-turbo-0613 llama-3-8b llama-2-70b mistral-finetune twelve-labs livekit groq openai nea nvidia lmsys mistral-ai model-performance prompt-engineering data-curation ai-safety model-benchmarking model-optimization training sequence-models state-space-models daniel-kokotajlo rohanpaul_ai _arohan_ tri_dao _albertgu _philschmid sarahcat21 hamelhusain jachiam0 willdepue teknium1
Twelve Labs raised $50m in Series A funding co-led by NEA and NVIDIA's NVentures to advance multimodal AI. Livekit secured $22m in funding. Groq announced running at 800k tokens/second. OpenAI saw a resignation from Daniel Kokotajlo. Twitter users highlighted Gemini 1.5 FlashModel for high performance at low cost and Gemini Pro ranking #2 in Japanese language tasks. Mixtral models can run up to 8x faster on NVIDIA RTX GPUs using TensorRT-LLM. Mamba-2 model architecture introduces state space duality for larger states and faster training, outperforming previous models. Phi-3 Medium (14B) and Small (7B) models benchmark near GPT-3.5-Turbo-0613 and Llama 3 8B. Prompt engineering is emphasized for unlocking LLM capabilities. Data quality is critical for model performance, with upcoming masterclasses on data curation. Discussions on AI safety include a Frontier AI lab employee letter advocating whistleblower protections and debates on aligning AI to user intent versus broader humanity interests.
Mamba-2: State Space Duality
mamba-2 mamba transformer++ llama-3-70b gpt-3 hugging-face state-space-models perplexity training-efficiency data-pruning benchmarking multimodality video-analysis _albertgu tri_dao arankomatsuzaki _akhaliq clementdelangue karpathy
Mamba-2, a new state space model (SSM), outperforms previous models like Mamba and Transformer++ in perplexity and wall-clock time, featuring 8x larger states and 50% faster training. It introduces the concept of state space duality (SSD) connecting SSMs and linear attention. The FineWeb-Edu dataset, a high-quality subset of the 15 trillion token FineWeb dataset, filtered using llama-3-70b for educational quality, enables better and faster LLM learning, potentially reducing tokens needed to surpass GPT-3 performance. Additionally, perplexity-based data pruning using a 125M parameter model improves downstream performance and reduces pretraining steps by up to 1.45x. The Video-MME benchmark evaluates multi-modal LLMs on video analysis across multiple visual domains and video lengths.