Meta EngineeringLive
Search “gpu unlock”
Found 9 items · updated 01:10
- Meta Engineering
Inception Labs BlogInception Labs launches the world's fastest inference large language model Mercury 2
Inception Labs has launched the world's fastest reasoning large language model, Mercury 2, based on a diffusion model architecture, achieving a generation speed of 1,009 tokens per second on NVIDIA Blackwell GPUs.
MarkTechPostSame story · 2 outletsBaseten released joey-1, a 2B parameter decision model, with a price of 0.01 USD per million input tokens
Baseten released joey-1, a 2B parameter decision model, which returns labels and calibration probabilities instead of generating text, used for high-volume routing, classification, and guardrail checks, with a price of 0.01 USD per million input tokens, and weights open-sourced on Hugging Face under the MIT license
Same story · 2 outletsHow the accounts differ:Baseten Blog states the model performs best when parameter size is ≤3B, while MarkTechPost mentions a 2B parameter model.
Hugging Face BlogFeaturedSame story · 2 outlets8585Mistral Large 4 (Le Chonk) released on Hugging Face
Mistral releases multimodal MoE model Mistral Large 4 (Le Chonk), with approximately 1T total parameters, 49B activation parameters, a context window of 1M tokens, offering two official versions FP8 and NVFP4, as well as GGUF made by Unsloth and llama.cpp teams.
Same story · 2 outletsWhy it matters: The original text provides specific scores for the 1T total parameter, 49B activation multimodal MoE model on cybersecurity and coding benchmarks, along with measured throughput for local and endpoint deployment.
- MarkTechPostFeaturedSame story · 4 outlets8383
Moonshot AI has released Kimi K3.1 Pro: a 2.8T total parameter, 104B activated MoE model that supports a context length of 1M.
Moonshot AI has released the first model of the K3.1 series, Kimi K3.1 Pro, which adopts a MoE architecture with a total parameter count of 2.8T and an activation parameter count of 104B, supporting a context length of 1M tokens. It has been launched on Kimi.ai, Kimi Work, Kimi Code, and Kimi API. The API pricing is $2 per million tokens for input and $10 for output, lower than K3's $3 and $15.
Same story · 4 outletsHow the accounts differ:Artificial Analysis gives the score 55 and ranks fourth; MarkTechPost mentions the total parameters of 2.8T and the MoE model with 104B active parameters.
Google Developers BlogGemini 4 Argon brings breakthrough progress to robotics technology
Gemini 4 Argon has made significant breakthroughs in physical understanding, code generation, and visual semantic reasoning, achieving an 87% score on the Embodied Reasoning (ERQA) benchmark test, surpassing all state-of-the-art models.
Cognition BlogRelease of the open-source multi-modal model SWE-1.6 and research progress
SWE-1.6 achieves 11% higher scores on SWE-Bench Pro, but exhibits overthinking and self-validation issues, running at a speed of 950 tok/s. The model improves RL algorithms and expands infrastructure training, increasing computational volume by two orders of magnitude, currently opening early access to a small number of users to collect feedback.
Liquid AI BlogLiquid AI has released LFM2.5: the next-generation edge AI model family
Liquid AI has released the LFM2.5-1.2B model family, with its pre-training extended to 28T tokens and significantly improved post-training performance through reinforcement learning, showing excellent performance on knowledge, instruction following, math, and tool use benchmarks. This series includes Base, Instruct, Japanese, Visual Language, and Audio Language models, all of which are open-source and available on Hugging Face and LEAP. The audio models are 8 times faster than their predecessors and natively run on vehicles, mobile devices, and IoT devices.