Hands-on running inclusionAI Ling-flash-2.0 (100B / 6.1B active, MXFP4 quant, 54.7GB) on SwiftLM via mlx-swift-lm on an M1 Max 64GB. Covers bailing_moe + MXFP4 support check in mlx-swift, the startup surprise, and what --stream-experts actually saves.
WAI-Illustrious SDXL v17 tested on M1 Max 64GB ComfyUI against v16 with the same seed. Hires fix now auto-corrects hands and feet, the four rating tags (general/sensitive/nsfw/explicit) still drive NSFW output, and v16-trained LoRAs mostly carry over — with one case where they don't.
A hands-on build and run of the Swift-based LLM inference server SwiftLM on an M1 Max 64GB. Covers Qwen3.6-35B-A3B and Qwen3.5-122B-A10B, with the same BST, BBS, and persona tests used in the existing Ollama and MLX-lm write-ups.
The NotebookLM clone open-notebook assumes Docker and cloud APIs by default. I installed SurrealDB natively, ran four processes in tmux, and wired everything through Ollama's qwen3.6:35b and bge-m3. I fed it the Qwen3.6 benchmark article I wrote this morning, and it answered with the correct numbers.
Tried Qwen3.6-27B on both Ollama and MLX. Ollama couldn't load the VL-projector-embedded GGUF, MLX ran it at 11 tok/s. On the side, running 35B-A3B under MLX was roughly 2× faster than the Ollama GGUF. Also had both models build a BBS to gauge intent handling.
Hands-on Qwen3.6-35B-A3B (23GB 4bit GGUF) on M1 Max 64GB via Ollama 0.20.6. Generation speed stays at 27 tok/s — same as Qwen3.5-35B-A3B — but the same prompt produces 13× more thinking tokens. Multi-turn behavior, persona handling, and a three-tier NSFW probe included.
Z-Image has its own pixel art LoRAs, but can they actually convert photos to pixel art via i2i? Tested Z-Image Turbo, base model, and compared with Illustrious on M1 Max 64GB.
Tested WAI-Anima v1, Anima preview3-base, and WAI-Illustrious v160 side by side on M1 Max 64GB ComfyUI with same seed/prompt. WAI-Anima inherits Anima's atmospheric lighting and natural running poses but still loses to WAI-Illustrious on tag control and character consistency. Includes i2i pipeline test (denoise 0.5), ~275s generation times, and how the Anima derivative ecosystem (WAI-Anima, CottonAnima, Kirazuri, RDBT) expanded in two months.
Based on EE Times' interview with AMD AI Software VP Anush Elangovan, we assess the ROCm vs CUDA ecosystem gap. Includes hands-on experience with ROCm breaking four times on Strix Halo, plus practical guidance on choosing between NVIDIA, AMD, and Apple Silicon.
Tested 5 approaches including Qwen Image Edit, JS color reduction, and Illustrious i2i + LoRA. Illustrious i2i alone turned out to be the fastest and lightest solution for pixel art conversion.
I tested local Vision LLMs (Gemma 3, Qwen2.5-VL, Llama 3.2 Vision, Gemma 4) to see if they could look at character illustrations and pixel art and generate RPG-style stats in JSON format.
SwiftLM, an Apple Silicon–only MLX inference server, provides a native Metal implementation of TurboQuant V2+V3 hybrid KV‑cache compression and NVMe SSD expert streaming.
