Following up on my previous post about my budget server setup (Intel N100 + RTX 5060 Ti 16GB), a few of you asked for a deeper dive into my actual inference config and real-world agentic performance. Like many of you, I was refreshing the page waiting to download Qwen 3.8 27B the second it dropped. After spending the entire weekend stress-testing it with agentic coding workflows, I managed to run a complete, large-scale project almost entirely autonomously ( over 1M total tokens processed , only 3 prompts total). Here is a quick breakdown of the core setup before we dive into the config and workflow details. Quick Specs & Params Model: Qwen3.8-27B-UD-Q3_K_XL.gguf Hardware: RTX 5060 Ti (16GB VRAM) + Intel N100 (4C/4T, 16GB RAM) Context Window: 73,728 (73k context) running comfortably in 16GB VRAM! KV Cache Quant: q4_1 for main context, q5_1 for MTP draft context Speculative Decoding: Native MTP enabled ( spec-type = draft-mtp , n-max = 2 ) Sampling: temp = 0.4 , top_p = 0.90 , top_k = 15 , min_p = 0.02 The Experiment: Building a full API in 3 Prompts Instead of running synthetic benchmarks, I put this setup through a real-world software engineering pipeline: building an unofficial REST API and MCP Server for a legacy vBulletin forum. Prompt 1 (Site Architecture & Analysis): Asked the model to map out the target site. It generated a flawless ~1,500-lines Markdown spec covering structural analysis, scrapable HTML nodes, expected JSON payloads, stack selection, pagination logic, session auth, and search endpoints—far more thorough than I would have written manually. Prompt 2 (Development Architecture): Using the spec as the single source of truth, it designed a modular NestJS API implementation plan broken into 9 execution phases: Phase 1: Project Scaffolding Phase 2: Domain Models Phase 3: Scraping Core (HTTP + Rate Limiting + Retries) Phase 4: HTML Parsers ( cheerio ) Phase 5: Cache Layer Phase 6: Application Services + REST API Phase 7: Authentication (Cookie Sessio…
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