Developers Test Run 2.4T Parameter Qwen Model on Consumer Hardware
Several developers successfully ran the massively quantized 2.4T parameter Qwen model on high-end consumer hardware. Tests included a multi-GPU desktop setup achieving 0.8 tok/s and a 512GB Mac Ultra utilizing 1-bit quantization, proving the feasibility of running ultra-large models locally.
2026-08-14 ~ 2026-08-14 · 3 related posts
- Episode 1: Developers Test Run 2.4T Parameter Qwen Model on Consumer Hardware(2026-08-14, 3 posts)
- Episode 2: Qwen3.8-27B Benchmarking on RTX 6000(2026-08-15, 2 posts)
- Episode 3: Community Shares Qwen3.8-27B Deployment on 32GB VRAM(2026-08-15, 4 posts)
- Episode 4: RTX 3090 Optimized for Efficient Qwen3.8-27B Inference(2026-08-15, 3 posts)
- Episode 5: Qwen3.8-27B Hits 50 tok/s on Dual RTX 3060s(2026-08-15, 2 posts)
- Episode 6: Qwen2.5 Local Performance Review(2026-08-15, 2 posts)
- Episode 7: Qwen3.8-27B local benchmarks: from RTX 4080 Super to RTX 6000 Pro(2026-08-16, 7 posts)
- Episode 8: Running Local Agent Models on Mac: Memory Is the Deciding Factor(2026-08-16, 2 posts)
- Episode 9: RTX 3090 users seek best local setup for running Qwen3 models(2026-08-16, 2 posts)
- Episode 10: Qwen3.8-27B local deployment tested: stable single-GPU agent runs and 128K context(2026-08-16, 3 posts)
- Episode 11: Qwen3.8-27B Inference Optimized for RTX 3090(2026-08-18, 2 posts)
- Episode 12: Inco AI Launches DFlash 2, Speeding Up LLM Inference Up to 4.6x(2026-08-19, 4 posts)
- Episode 13: llama.cpp's New DFlash2 Boosts Qwen Inference Speed Up to 3x(2026-08-20, 2 posts)
- Episode 14: Qwen3.8-27B Inference Hits 381 TPS on RTX 3090(2026-08-20, 2 posts)
- Running 2.4T Qwen3.8 Model on RTX 5090 + 5060 Ti: 0.8 tok/s Tested — mossy_troll_84 · 2026-08-14
- Running Qwen 2.4T Locally: 5 GPUs Still Can't Make It Viable — klicker0 · 2026-08-14
- Running 2.4T Params on Mac Ultra: 1-bit Quantization Real-world Test — Ok_Technology_5962 · 2026-08-14