dual-gpu

KDE Plasma widget to change the GPU mode using the EnvyControl command line tool.

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

D-Bus service to check the availability of dual-GPU

Real-time N-Body Algorithm Accelerated With CUDA and FFT (for up to 1 billion particles with multi GPU)

Quality-first local MiniMax H3 video-series generation and loopback API for dual RTX 4090 workstations, with native audio, references, P8/P9 continuity, and preserved artifacts.

Dual RTX 3090 local LLM infrastructure — 48 GB VRAM AI cluster build. Hardware spec, photos, power-limit script. Companion to the blog series at blog.zacharycangemi.com.

Restore, test, and use both AMD FirePro GPUs in the 2013 Mac Pro on Linux. I made this for Omarchy Linux. running on the "Trash Can" Mac Pro.

Reproducible vLLM recipe for shawnw3i/Huihui-Qwen3.6-27B-abliterated-AWQ-MTP on 2× RTX 3090 in a Proxmox LXC. MTP n=3, 256K context, full vision+tool-calling+reasoning. Silent 24/7 operation at 250W per card. Companion to the base-model recipe.

Hybrid AI routing: LOCAL Ollama + CLOUD GitHub Copilot

Minisforum MS-02 Ultra with two Blackwell workstation GPUs (RTX PRO 4000 SFF 24 GB + RTX PRO 2000 16 GB) on the stock PSU: build notes, BIOS/Proxmox settings, 2-hour load test, local LLM benchmarks

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Reproducible Mage-Flow-Turbo inference on Kaggle Tesla T4×2 using upstream PyTorch, BF16 dtype/materialization and SDPA. Includes canonical dual-GPU qualification, explicit transformer/VAE placement, fail-closed routing and provenance checks, sustained 12-image 512/768/1024 showcase, timing, GPU memory metrics, SHA-256 evidence, and bilingual docs.

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Dual-GPU LLM gateway with deterministic routing, agentic orchestration, constraint verification, and an Answer Quality Gate

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).

Qwen3.8-Flash-Next on 2× RTX 3090: with 128 GB RAM up to 4,191 tok/s prefill · 111.5 tok/s decode (131K prompt), full 256K window at 2,865 tok/s; with 64 GB RAM 3,410 tok/s prefill · 84 tok/s decode. New: opt-in uncensored mode (runtime abliteration, no new weights).