Add minimum hardware requirements section

README.md

@@ -85,7 +85,39 @@ This is fundamentally different from a **reasoning model** (like Apex), which ex
 
 ---
 
-## 📊 Tooling Benchmark — BugTraceAI Ultra Bench v1.0
+## � Minimum Hardware Requirements
+
+Getting a 27B model running well on consumer hardware is not trivial — it requires careful quantization. The IMatrix-guided Q4_K_S used here preserves quality in the most critical weight layers, so you get near-F16 performance at a fraction of the VRAM cost.
+
+**Q4_K_S — 15 GB (Recommended)**
+
+- Minimum: **RTX 3090 (24 GB VRAM)** — full GPU offload, fast inference
+- RTX 4090 (24 GB) — same, slightly faster
+- RTX 4080 (16 GB) — runs with reduced context (2048–4096)
+- A4000 (16 GB) — workstation-grade, solid for pipelines
+- 2× RTX 3060 (12 GB) — split layers across GPUs with `-ts` flag
+- CPU fallback: 64 GB+ RAM — slower but fully functional
+
+**Q6_K — 21 GB (High Fidelity)**
+
+- Minimum: **RTX 3090 / A5000 (24 GB VRAM)** — tight fit, recommended 4096 ctx
+- A6000 (48 GB) — comfortable full offload
+- H100 / A100 (80 GB) — server-grade, full context at speed
+
+**Practical tip for llama-server:**
+```bash
+# RTX 3090/4090 — full GPU offload
+./llama-server -m model.gguf -ngl 99 -c 4096 --port 8080
+
+# RTX 4080 16GB — partial offload
+./llama-server -m model.gguf -ngl 28 -c 2048 --port 8080
+```
+
+> The fact that this model runs on a single consumer GPU is the result of significant quantization work — IMatrix calibration on a domain-specific security corpus ensures the quality loss is minimal where it matters most.
+
+---
+
+## �📊 Tooling Benchmark — BugTraceAI Ultra Bench v1.0
 
 Benchmarked on 2026-05-11 at temperature 0.1 and 0.3.