[2608.16157] FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution

Source of Claims: https://x.com/Andy_ShuoYang/status/2090856976880472439 Your gaming PC can now serve frontier models at interactive speed using official checkpoints without extreme quantization! Qwen3.6 35B → 8GB RTX 4060 laptop @ 39 tok/s DeepSeek-V4-Flash 284B → RTX 5090 desktop @ 22-25 tok/s GLM-5.2 753B → RTX PRO 6000 workstation @ 15 tok/s Run your claude code or codex now with frontier model for $0 FreeToken is fast. Comparing to Ollama, we have 3–4× faster decode, and 6–30× faster prefill How? We introduce bandwidth-adaptive CPU–GPU execution + semantic-aware caching across agent turns. submitted by /u/SteppenAxolotl [link] [comments]

r/DestroyMyGame destroyed me to the void for using AI. I used Qwen 3.6 27B Q8 with MTP for about 20% of this single HTML file physics shooter game. I remember last year being blown away by GLM 4.5 Air being able to write a somewhat coherent HTML webpage.
Open Source Ternary LLM Engine in Rust/CUDA for Quantization, Serving, and Training of models on consumer GPUs, called Tritium (Apache 2.0)
[[release-wintermix-qwen35-122b-a10b-in-native-mlx-an-82-gib-b|[Release] WinterMix — Qwen3.5-122B-A10B in native MLX: an 82 GiB build that beats 94–95 GiB quants, plus a 68 GiB build for agent swarms]]
Qwen 3.6 27B BF16 vs Q4_K_M vs Q8_0 GGUF evaluation

Source: r/LocalLLaMA | 2026-08-24

Related