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Explore DeepSeek V4 Flash (0731) vs Kimi K3: The 2.8 Trillion Parameter Lie with 8,362 views, 164 likes, and 19 comments. Experience the impact of this video content that has captured audience attention.

Can you actually run Moonshot's 2.8 Trillion Parameter Kimi K3 locally, or is DeepSeek V4 Flash the only real open-weights choice? In this head-to-head architectural benchmark, we expose the brutal hardware realities between the two models. Kimi K3 crushes DeepSeek on the Artificial Analysis intelligence index, but requires a 600GB RAM minimum (which the 512GB Mac Studio literally cannot load), while DeepSeek V4 Flash runs flawlessly at 34 tokens per second on an M3/M5 Max MacBook Pro using Unsloth's 2-bit quantization. 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/4kJqEBMMf We dive deep into the API pricing traps (where K3 costs 21x more for inputs and 54x more for outputs), expose how DeepSeek's verbose "thinking out loud" eats into its cost advantage, and break down the dirty secrets behind their vendor-run Terminal-Bench scores. We also analyze the new custom C engine ds4 built by Redis creator antirez, how DSpark speculative decoding works, and the massive legal difference between DeepSeek's true MIT license and Moonshot's commercial restriction traps. If you found this technical deep-dive into AI infrastructure and hardware scaling helpful, drop a like and subscribe for more content on software engineering and system design! ⏱️ TIMESTAMPS: 00:00 - The 2.8 Trillion vs 284 Billion Parameter Gap 01:49 - Kimi K3 Architecture: Kimi Delta Attention & MoonViT 02:34 - DeepSeek V4 Flash: 13B Active MoE & DSpark 04:01 - The Benchmark Lie: Why Vendor Scores Don't Match 05:38 - Independent Tests: Artificial Analysis & Datacurve 06:22 - The True API Cost: Input vs Output Tokens 08:03 - Hardware Limits: Why 512GB Macs Can't Run Kimi K3 08:43 - DeepSeek V4 Local Inference (ds4 Engine by antirez) 09:21 - Open Source vs Restricted: The Kimi K3 License Trap 10:17 - Final Verdict: Which Model Should You Deploy? 🔗 Resources Mentioned: ds4 Inference Engine (by antirez): https://github.com/antirez/ds4 DeepSWE Benchmark: https://github.com/datacurve-ai/deep-swe DeepSeek Official Hugging Face: https://huggingface.co/deepseek-ai Moonshot Kimi Models: https://github.com/MoonshotAI Unsloth (GGUF Quants): https://huggingface.co/unsloth Artificial Analysis: https://artificialanalysis.ai/ #deepseek #kimik3 #localllm #machinelearning #apple #macstudio #softwareengineering #systemdesign #artificialintelligence #llamacpp User Queries: deepseek v4 flash vs kimi k3 benchmark comparison how to run deepseek v4 local on macbook pro m5 max kimi k3 2.8 trillion parameter model download requirements unsloth 1 bit quantization deepseek v4 gguf deepswe terminal bench ai coding agent leaderboard kimi delta attention vs deepseek mixture of experts ds4 c engine deepseek inference antirez redis kimi k3 open weights commercial license restrictions deepseek v4 api pricing vs kimi k3 token cost dspark speculative decoding throughput deepseek

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