Real local agentic coding on a 12GB VRAM budget.

Thanks to Unsloth Dynamic 3.0 quants coming in slightly leaner and better preserved, I settled on Qwen 3.8 27B (UD_Q4_K_XL) at 100K context as my daily driver for Hermes Agent and OpenCode. On an RTX 5070 Ti Mobile (12GB) paired with an Intel Core Ultra 9 275HX and 32GB DDR5, this configuration consistently delivers ~9–11 t/s decode and 400–550 t/s prefill. It is fast enough to stay productive. The main bottleneck with Qwen 3.8 27B is its reasoning verbosity: it routinely blows past 100K tokens in the planning phase alone, forcing OpenCode into native context compaction. Since OpenCode's built-in compaction struggles with retention, I switched to Magic Context. With Magic Context in place, the session has scaled past 3.7M total processed tokens without losing critical details or derailments. Across a complex personal project, the local model shipped two major features end-to-end. I still use Claude Opus for final PR reviews to catch edge cases and minor bugs, which Qwen then fixes locally without issues. Hardware Specs: GPU: RTX 5070 Ti Mobile (12GB VRAM) CPU: Core Ultra 9 275HX RAM: 32GB DDR5 Llama.cpp Launch Parameters: llama-server \ -ctx 98304 -ub 512 -np 1 -ngl 99 \ -ot 'blk.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38|39|40|41|42|43|44|45|46|47|48|49|50|51|52|53|54|55|56|57|58|59|60|61|62|63|64).ffn_(gate|up|down).weight=CPU' \ -fa on -ctk q8_0 -ctv q8_0 -fit off \ --mmproj --no-mmproj-offload \ --spec-type draft-mtp --spec-draft-n-max 2 \ -ctkd q8_0 -ctvd q8_0 --load-mode 'none' \ --temp 1 --top-k 20 --top-p 0.95 --min-p 0 \ --repeat-penalty 1 --presence-penalty 0 \ --jinja -chat-template-kwargs '{"reasoning_effort": "xhigh"}' \ --reasoning preserve