Qwen3.8 27b for agentic coding and next .... what?

First, I'd like to thank the Qwen and Unsloth teams for the Qwen3.8 27b UD Q4_K_XL. Fits the poor 24GB of 3090 VRAM with 100k context at Q8 and works phenomenally well! Imho if theres anything that can threaten Anthropic/OpenAI profits is not another frontier model but actually these small ones you can run fast locally that can do 80..90% of mundane work for hours without paying a single dollar to any external company. But next, if you want to jump up to a bigger smarter model I feel there is a gap now. Kimi-K3 is out of reach for many businesses let alone prosumers. So what frontier-like models do you use on what setups? Is a DGX cluster (2..4 machines) or a GPU server with dual or quad GPU (~96 ... 192 GB of VRAM + >256GB DDR4) a suitable setup to run something like MiniMax-M3 at reasonable speeds for agentic coding (>30tps)? And privacy aside, is hardware cost worth it? I have a dual rtx3090 + 128GB ddr4 machine, running Qwen3.8-Flash-Next Q4 quite fast but despite being larger doesn't feel much smarter than the Qwen2.8 27b and while I can run larger quantized models, Minimax-M2.7 being my workhorse, it way too slow for coding. submitted by /u/ChopSticksPlease [link] [comments]

After pushing 1M+ tokens through Qwen 3.8 27B, here is my optimal llama.cpp config for 16GB VRAM (73k Context, Agentic Coding)
Has anyone actually made 64k feel like 300k+ with recursive local agents?
Am I doing something wrong? Qwen 3.8 27B seems useless for agentic coding
Bro wtf, Qwen Lab cooked with Qwen 3.8 27B, it's so fucking good
[[benchmark-optimal-dflash2-quants-for-speed-and-context-size-|[Benchmark] Optimal DFlash2 quants for speed and context size, 5090 RTX, llama.cpp, Qwen 3.8 27B Dynamic3 Unsloth. Comparison with MTP]]

Source: r/LocalLLaMA | 2026-09-05

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