Here we go Confirmation from MiniMax’s Head of Engineering on what I have been saying: MiniMax-M2.7 weights will be opensourced within the next couple of weeks x.com/TheAhmadOsman/…
@theahmadosman
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MiniMax M2.7 Model Weights to Be Open-Sourced Soon
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Here we go Confirmation from MiniMax’s Head of Engineering on what I have been saying: MiniMax-M2.7 weights will be opensourced within the next couple of weeks
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Weekly Space Planned for Local AI Hardware Discussions
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I am thinking of hosting a weekly space to discuss and answer questions on hardware options for local ai / llm If you’re interested please interact w/ this tweet I am open to suggestions for other topics as well, but I am hoping this will lead to active discussions in the space
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Local LLM Bottlenecks: Bandwidth, Interconnect, and Inference Engines
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When running LLMs locally,
the bottleneck isn’t just “VRAM size” It’s: – memory bandwidth
– interconnect (PCIe vs NVLink vs RDMA)
– inference engine (vLLM, TensorRT-LLM, SGLang) Unified Memory is way slower than VRAM btw -
MiniMax-M2.7 Weights to Be Open-Sourced Within Weeks
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MiniMax-M2.7 weights will be opensourced within the next couple of weeks
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Open Source AI Models Poised to Win, Analyst Says
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this week in San Jose made me more bullish on open models than ever there is NO WAY opensource AI doesn't win
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RTX PRO GPU: Why Unified Memory Limits Multi-Agent Concurrency
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RTX PRO for sure, the below is relevant as to why Unified Memory isn’t ideal for concurrency/multi-agents
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Matching Inference Engines to GPUs and Model Architectures
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Each inference engine must implement the model architecture & its tool calling Getting a model to run correctly isn’t trivial, many parts are still inconsistent/broken Set the right baseline: match the GPUs, the inference engine for those GPUs & right inference engine for model
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TensorRT-LLM Optimized for DGX Spark: Inference Engines Matter
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Inference Engines have different optimizations for different hardware Most optimized for DGX Spark: TensorRT-LLM Inference Engines MATTER and they are NOT EQUAL (e.g. blogpost below) Opensource models ARE NOT just a memory size issue BTW
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DGX Spark vs RTX PRO 6000 Memory Bandwidth: Why Tool Choice Matters
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DGX Spark uses unified memory > 273 GB/s RTX PRO 6000 delivers > 1.8 TB/s (1792 GB/s) If someone told you they’re comparable, they’re wrong And this is exactly why llama.cpp isn’t the right tool here Try vLLM or SGLang on a GPU and you’ll see very different results