Every published setup that reports this model, with its machines, quantization, runtime and reported results. Use the directory filters below to narrow by quantization, capacity or runtime.
Observed across 60 published setups. Counts describe what contributors run, not what the model is “good for”.
Capacity is the reported total for this setup. Multi-GPU and unified-memory layouts can behave differently.
A gaming PC with an RTX 4060 8GB and 32GB DDR5 runs a 17.5GB Qwen 3.8 27B by splitting it between VRAM and system RAM and adding speculative decoding, then drives it as a remote coding agent; the same machine also runs Qwen3-VL for invoice extraction.
A 128GB NVIDIA Jetson Thor T5000 runs Qwen 3.8 27B, Qwen-Image 2.1, ComfyUI and the Hermes agent to turn one Telegram message into a comic book and deploy it to a website, entirely locally.
Two single-GPU Ubuntu servers, one with a blower-modded Tesla V100 32GB and one with an RTX 3090, run Strata with Qwen 3.8 Flash Next at 262K context for local business management automation.
A purpose-built engine (NInfer 4080) runs ISTA-DASLab's Qwen 3.8 27B GSQ at 100K+ context on one RTX 4080 16GB, measuring up to ~2,720 tok/s prefill and ~262 tok/s decode through DFlash2 speculative decoding.
The author runs Qwen 3.8 27B at Q6 with a 160k context and vision on an RTX 5090, using the Hermes agent harness and SearxNG in Docker, and reports around 80 tok/s but about 15 minutes for a one-shot website.
Long-context decode benchmark of Qwen 3.8 Flash Next on an M1 Max 64 GB through an unofficial Splash/ds4 fork in real OpenCode agent sessions.
Two Radeon Instinct MI50 cards limited to 145W each are benchmarked with llama.cpp Vulkan across dense and MoE models, with MoE models far ahead of dense ones.
Context-scaling benchmark of Qwen 3.8 27B in NVFP4/MLX through Ollama on a 36 GB M4 Max for agentic coding, measuring TTFT, prefill and decode from 4K to 115K.
A single RTX 3090 running Qwen 3.8 27B Q4 via llama.cpp, orchestrated by GPT-6.1 Sol in the cloud, finished three small 3D games in 43.4 minutes and $0.17 total.
A documented, measured local coding setup: Qwen 3.8 27B on one RTX 4090 with llama.cpp and OpenCode, holding a 128k context and reporting around 92 tokens/s generation with speculative decoding.
A purpose-built engine (NInfer v1.3) fits Qwen 3.8 27B at a real 131,072-token context and KV capacity, plus vision, on one RTX 5080 16GB, qualifying ~71.6 tok/s decode and ~1,381 tok/s prefill on a 118,001-token prompt.
Daily OCR of math- and table-heavy PDFs into LaTeX-correct Markdown and Word, using Qwen vision models on llama.cpp instead of a smaller model that misreads numbers.
