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Explore Run Ornith 1.5 35B Locally: 155 Tokens/Sec on Your GPU? with 20,781 views, 259 likes, and 23 comments. Experience the impact of this video content that has captured audience attention.

Can you actually run Ornith-1.5-35B-A3B on a single 12GB RTX 3060 or RTX 4070 Ti at 53 tokens/sec with 170K context? In this deep dive, Cloud Codes audits the GGUF Q4_K_M quantizations, Qwen 3.5 MoE architecture (256 experts, 8 active), linear attention KV cache memory math, and llama.cpp CUDA performance across NVIDIA RTX 4090 (24GB), RTX 4070 Ti (12GB), and RTX 3060 (12GB) GPUs. 🔔 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 dissect why hybrid linear attention reduces 170K KV cache memory from 13.9GB down to 1.74GB, benchmark prompt prefill vs decode generation throughput, test tool-calling reasoning degradation past 70K tokens, and compare Ornith 1.5 against Qwen3.8-27B on S.W.E. bench Verified and Terminal-Bench 2.1. Learn how sampling temperature retuning fixes stock model refusals, and why allocated context windows differ from usable reasoning limits. If this breakdown helped you optimize local LLM inference, quantizations, and GPU VRAM management, subscribe to Cloud Codes for a new open-source AI infrastructure deep dive every week! Build, solve, deploy. 🔗 Repositories & Sources Mentioned: • Ornith 1.5 Hugging Face Model Collection: https://huggingface.co/collections/ornith-ai/ornith-15 • Ornith-1.5-35B-A3B Repository & Model Card: https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B • Official GGUF Quantization Repo: https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF • Terminal-Bench 2.1 Public Leaderboard: https://tbench.ai/leaderboard/terminal-bench/2.1 • r/LocalLLaMA RTX 4070 Ti Benchmark Run: https://reddit.com/r/LocalLLaMA/comments/1vt6hwc/ • AtomicChat Quantization Quality Analysis: https://reddit.com/r/LocalLLaMA/comments/1vsx94f/ • Hugging Face Discussion #22 (Context Degradation Test): https://huggingface.co/ornith-ai/Ornith-1.0-35B-GGUF/discussions/22 ⏱️ Video Chapters: 0:00 - The 35B Local LLM Claims 1:07 - Ornith 1.5 Architecture: Qwen 3.5 MoE & Self-Improvement 3:32 - GGUF Quantization & The 20KB/Token KV Cache Math 5:28 - Real Benchmarks: 53 Tok/s Decode on RTX 4070 Ti 7:06 - The Context Trap: Memory Allocation vs Reasoning Collapse 9:06 - Ornith 1.5 vs Qwen3.8-27B & Stock Refusal Bugs 11:04 - The Final Verdict: What to Run on Your GPU #localllm #ornith #rtx4090 #rtx3060 #opensourceai #llamacpp #gguf #machinelearning #cloudcodes #aihardware User Queries: ornith 1 5 35b a3b rtx 3060 12gb vram performance how to run 35b moe llm on consumer gpu llamacpp ornith 1.5 35b vs qwen 3.8 27b coding benchmark rtx 4090 24gb 170k context kv cache memory calculation local llm context window degradation 70k tokens ornith 1.5 gguf q4_k_m vs iq4_xs quantization quality mixture of experts 256 experts 8 active local inference speed terminal bench 2.1 swe bench verified ornith 1.5 llama.cpp cuda n_cpu_moe flash attention 12gb vram setup best open source coding model for 12gb gpu 2026

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