Initial release: TurboQuant practical guide for consumer hardware

- README.md: bilingual (EN/DE), benchmark results, quick start, 5 error fixes
- WHITEPAPER.de.md: full German white paper
- Dockerfile: reproducible CUDA build (feature/turboquant-kv-cache branch)
- scripts/: download-model.sh, run-baseline.sh, run-turbo.sh
- results/: RTX 3090 benchmark JSON (April 2026)

Results: 100K context on RTX 3090 with +1.8 GB VRAM and -8.5% TPS
Based on TurboQuant (ICLR 2026, arXiv:2504.19874)

Dockerfile

@@ -0,0 +1,56 @@
+# TurboQuant llama.cpp — CUDA Build
+# Builds llama-server with TurboQuant KV-cache quantization support
+# turbo2 / turbo3 / turbo4 cache types enabled
+#
+# CRITICAL: Use --branch feature/turboquant-kv-cache (NOT master!)
+# The master branch is a standard llama.cpp without TurboQuant support.
+#
+# Usage:
+#   docker build -t turboquant:feature .
+#   docker run --rm turboquant:feature llama-server -h 2>&1 | grep -A3 "cache-type-k"
+#   # Must show: turbo2, turbo3, turbo4
+
+FROM nvidia/cuda:12.6.3-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && apt-get install -y \
+    cmake \
+    build-essential \
+    git \
+    wget \
+    curl \
+    python3 \
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /build
+
+# CRITICAL: Must use --branch feature/turboquant-kv-cache
+# Default 'master' does NOT have turbo2/turbo3/turbo4 cache types!
+RUN git clone https://github.com/TheTom/llama-cpp-turboquant.git \
+    --branch feature/turboquant-kv-cache \
+    --depth=1
+
+WORKDIR /build/llama-cpp-turboquant
+
+# Fix: libcuda.so.1 is not available at build time (driver is injected at runtime only)
+# The devel image provides a stub at /usr/local/cuda/lib64/stubs/libcuda.so
+# Symlink to .1 so the linker finds it during cmake build
+RUN ln -sf /usr/local/cuda/lib64/stubs/libcuda.so \
+           /usr/local/cuda/lib64/stubs/libcuda.so.1 \
+    && echo "/usr/local/cuda/lib64/stubs" > /etc/ld.so.conf.d/cuda-stubs.conf \
+    && ldconfig
+
+# IMPORTANT: Use -DGGML_CUDA=ON (not -DLLAMA_CUBLAS=ON which was renamed in ~2024)
+RUN cmake -B build \
+    -DGGML_CUDA=ON \
+    -DCMAKE_BUILD_TYPE=Release \
+    && cmake --build build --config Release -j4 --target llama-server
+
+RUN cp build/bin/llama-server /usr/local/bin/llama-server
+
+WORKDIR /models
+EXPOSE 8180
+
+# Default: show help. Override CMD in docker run to actually serve a model.
+CMD ["llama-server", "--help"]

LICENSE

@@ -0,0 +1,35 @@
+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+Copyright (c) 2026 AI Engineering Lab (ai-engineering.at)
+
+You are free to:
+  Share — copy and redistribute the material in any medium or format
+  Adapt — remix, transform, and build upon the material for any purpose, even commercially
+
+Under the following terms:
+  Attribution — You must give appropriate credit, provide a link to the license,
+  and indicate if changes were made.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/legalcode
+
+---
+
+The Dockerfile and shell scripts in this repository are additionally available under MIT License:
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software
+and associated documentation files, to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
+and/or sell copies of the Software, and to permit persons to whom the Software is furnished
+to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED.
+
+---
+
+Based on TurboQuant (arXiv:2504.19874) by Thomas et al., licensed under their respective terms.
+llama.cpp fork: https://github.com/TheTom/llama-cpp-turboquant

README.md

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+# llama-cpp-turboquant-guide
+
+<div align="center">
+
+![TurboQuant](https://img.shields.io/badge/TurboQuant-turbo3%20KV--Cache-blueviolet)
+![Context](https://img.shields.io/badge/Context-100%2C000%20tokens-brightgreen)
+![GPU](https://img.shields.io/badge/GPU-RTX%203090%2024GB-76b900?logo=nvidia)
+![VRAM Overhead](https://img.shields.io/badge/VRAM%20overhead-%2B1.8%20GB%20only-blue)
+![Speed Loss](https://img.shields.io/badge/Speed%20loss--8.5%25%20only-orange)
+![License](https://img.shields.io/badge/license-CC%20BY%204.0-green)
+
+**Practical guide: TurboQuant KV-cache quantization on consumer hardware.**
+**100,000 token context on a single RTX 3090 — verified, reproducible, step-by-step.**
+
+*Based on [TurboQuant (ICLR 2026, arXiv:2504.19874)](https://arxiv.org/abs/2504.19874)*
+
+[Results](#-results) · [Quick Start](#-quick-start) · [How It Works](#-how-it-works) · [Errors & Fixes](#-errors--fixes) · [Deutsch](#-deutsch)
+
+</div>
+
+---
+
+## 📊 Results
+
+Tested on **NVIDIA RTX 3090 (24 GB VRAM)** with **Mistral-Small-3.2-24B Q4_K_M**.
+
+| | Baseline (f16) | TurboQuant turbo3 | Delta |
+|--|:--------------:|:-----------------:|:-----:|
+| **Context** | 8,192 tokens | **100,000 tokens** | **+12.2×** |
+| **VRAM** | 15.4 GB | 17.2 GB | +1.8 GB only |
+| **Tokens/s** | 49.2 | 45.0 | −8.5% |
+| **KV-Cache size** | ~1 GB (f16) | ~2.8 GB (3-bit) | **4.3× compression** |
+
+> **12× more context. +11% VRAM. −8% speed. Same model weights.**
+
+Raw data: [`results/turboquant-rtx3090-2026-04-01.json`](results/turboquant-rtx3090-2026-04-01.json)
+
+---
+
+## 🚀 Quick Start
+
+### 1. Build the Docker Image (~20 minutes)
+
+```bash
+docker build -t turboquant:feature .
+
+# Verify TurboQuant is compiled in:
+docker run --rm turboquant:feature llama-server -h 2>&1 | grep -A3 "cache-type-k"
+# Must show: turbo2, turbo3, turbo4
+```
+
+### 2. Download a Model
+
+```bash
+# Set your HuggingFace token
+export HF_TOKEN=hf_your_token_here
+
+bash scripts/download-model.sh
+```
+
+### 3. Run Baseline (f16, 8K context)
+
+```bash
+bash scripts/run-baseline.sh
+# Server starts on port 8180
+```
+
+### 4. Run TurboQuant (turbo3, 100K context)
+
+```bash
+bash scripts/run-turbo.sh
+# Server starts on port 8182
+```
+
+### 5. Test It
+
+```bash
+# Check available context
+curl -s http://localhost:8180/v1/models | jq '.data[0].context_length'
+# Baseline: 8192
+
+curl -s http://localhost:8182/v1/models | jq '.data[0].context_length'
+# TurboQuant: 131072 (model max, allocated to 100000)
+
+# Generate tokens (measures TPS in response)
+curl http://localhost:8182/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model":"local","messages":[{"role":"user","content":"Count from 1 to 200"}],"max_tokens":500}'
+```
+
+---
+
+## ⚙️ How It Works
+
+### The KV-Cache Problem
+
+When an LLM runs, it caches Key-Value pairs for every token in the context window.
+This cache grows **linearly** with context length:
+
+```
+Mistral-Small-3.2 24B on RTX 3090 (24 GB total, ~14.4 GB for model weights):
+
+Context    KV-Cache (f16)   Available after model   Fits?
+  8,192        ~1 GB             9.6 GB               ✅
+ 32,000        ~4 GB             9.6 GB               ✅
+100,000       ~12 GB             9.6 GB               ❌ OOM without TurboQuant
+100,000     ~2.8 GB (turbo3)     9.6 GB               ✅
+```
+
+### What TurboQuant Does
+
+TurboQuant compresses the KV-cache from 16-bit floats to 2–4-bit integers.
+**It does NOT compress the model weights** — only the runtime cache.
+
+```
+f16 KV-Cache  →  turbo3 KV-Cache
+  16 bits     →    3 bits  =  4.3× compression
+```
+
+The model reads the quantized cache and generates text normally.
+Quality loss: <1% perplexity increase at turbo3 (per paper).
+
+### Two Repos — Critical Distinction
+
+There are two TurboQuant repositories with confusing names:
+
+| Repo | What it is | When to use |
+|------|-----------|-------------|
+| `TheTom/turboquant_plus` | Python library for research | HuggingFace models, Python API |
+| `TheTom/llama-cpp-turboquant` | llama.cpp fork | **This guide — llama-server** |
+
+**This guide uses `TheTom/llama-cpp-turboquant`, branch `feature/turboquant-kv-cache`.**
+
+---
+
+## 🐛 Errors & Fixes
+
+Every error we hit during setup, documented so you don't repeat them:
+
+### E1: Wrong Repository
+
+**Symptom:** No `turbo2`/`turbo3`/`turbo4` options after building.
+**Cause:** Built from `TheTom/turboquant_plus` (Python library) instead of `TheTom/llama-cpp-turboquant`.
+**Fix:** Use the correct repo. See Dockerfile.
+
+### E2: Wrong cmake Flag
+
+**Symptom:** CUDA not used during inference, slow CPU fallback.
+**Cause:** Old flag `-DLLAMA_CUBLAS=ON` was renamed in llama.cpp post-GGML-refactor.
+**Fix:**
+```dockerfile
+# WRONG (old, silently ignored):
+cmake -DLLAMA_CUBLAS=ON -DLLAMA_CUDA=ON
+
+# CORRECT:
+cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
+```
+
+### E3: libcuda.so.1 Not Found at Build Time
+
+**Symptom:** Build fails with `cannot find -lcuda` or linker error for `libcuda.so.1`.
+**Cause:** CUDA devel images have a stub `libcuda.so` but not `libcuda.so.1` (the runtime driver is injected at container start, not build time).
+**Fix:** Add symlink before cmake:
+```dockerfile
+RUN ln -sf /usr/local/cuda/lib64/stubs/libcuda.so \
+           /usr/local/cuda/lib64/stubs/libcuda.so.1 \
+    && echo "/usr/local/cuda/lib64/stubs" > /etc/ld.so.conf.d/cuda-stubs.conf \
+    && ldconfig
+```
+
+### E4: Wrong Branch
+
+**Symptom:** `Unsupported cache type: turbo3` at runtime despite clean build.
+**Cause:** Cloning the default `master` branch of `llama-cpp-turboquant` — which is a standard llama.cpp fork **without** TurboQuant. The implementation is on `feature/turboquant-kv-cache`.
+**Fix:**
+```bash
+git clone https://github.com/TheTom/llama-cpp-turboquant.git \
+  --branch feature/turboquant-kv-cache --depth=1
+```
+Always verify before building:
+```bash
+curl -s "https://api.github.com/repos/TheTom/llama-cpp-turboquant/branches" \
+  | python3 -c "import sys,json; [print(b['name']) for b in json.load(sys.stdin)]"
+```
+
+### E5: Wrong HuggingFace Repo Name
+
+**Symptom:** 404 or 401 when downloading model.
+**Cause:** Model repo names change. Don't rely on memory or cached context.
+**Fix:** Always query HF Search API before downloading:
+```bash
+curl -s -H "Authorization: Bearer $HF_TOKEN" \
+  "https://huggingface.co/api/models?search=bartowski+mistral+small+3.2&limit=5" \
+  | python3 -c "import sys,json; [print(m['modelId']) for m in json.load(sys.stdin)]"
+```
+
+---
+
+## 🔬 Reproduce Our Results
+
+```bash
+# 1. Build
+docker build -t turboquant:feature .
+
+# 2. Download model (~14 GB)
+export HF_TOKEN=hf_your_token
+bash scripts/download-model.sh
+
+# 3. Baseline measurement
+bash scripts/run-baseline.sh &
+sleep 45  # wait for server startup
+curl -s http://localhost:8180/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model":"local","messages":[{"role":"user","content":"Count from 1 to 200, one per line."}],"max_tokens":500}' \
+  | python3 -c "import sys,json; d=json.load(sys.stdin); u=d['usage']; print(f'TPS: {u[\"completion_tokens\"] / (d[\"usage\"].get(\"total_time_ms\",10000)/1000):.1f}')"
+nvidia-smi --query-gpu=memory.used --format=csv,noheader
+
+# 4. Turbo3 measurement
+docker stop turboquant-baseline
+bash scripts/run-turbo.sh &
+sleep 90  # 100K context allocation takes longer
+# repeat curl + nvidia-smi on port 8182
+```
+
+Expected results matching our run: see [`results/turboquant-rtx3090-2026-04-01.json`](results/turboquant-rtx3090-2026-04-01.json)
+
+---
+
+## Hardware Requirements
+
+| | Minimum | Our Setup |
+|--|---------|----------|
+| GPU VRAM | 16 GB | RTX 3090 24 GB |
+| System RAM | 16 GB | 32 GB |
+| Disk | 30 GB | SSD |
+| CUDA | 12.x | 12.6.3 |
+| OS | Linux / Windows + Docker | Windows + Docker Desktop |
+
+> **Note on Windows:** Docker Desktop works fine. Avoid `/tmp/` paths — use named Docker volumes for model storage.
+
+---
+
+## Model Compatibility
+
+Tested with **Mistral-Small-3.2-24B Q4_K_M** (14 GB).
+Should work with any GGUF model that fits the VRAM budget after KV-cache allocation.
+
+| Model | Size | VRAM (model) | Max ctx (turbo3) |
+|-------|------|-------------|-----------------|
+| Mistral-Small-3.2 24B Q4_K_M | 14 GB | 14.4 GB | ~100K on 24 GB GPU |
+| Llama-3.1 8B Q4_K_M | 4.7 GB | 5.1 GB | ~200K on 16 GB GPU |
+| Qwen2.5 14B Q4_K_M | 8.5 GB | 8.8 GB | ~150K on 16 GB GPU |
+
+*Estimates. Actual values depend on architecture and batch size.*
+
+---
+
+## 📄 License
+
+Content and scripts: [CC BY 4.0](LICENSE)
+Based on [TurboQuant (arXiv:2504.19874)](https://arxiv.org/abs/2504.19874) by Thomas et al. (ICLR 2026)
+llama.cpp fork: [TheTom/llama-cpp-turboquant](https://github.com/TheTom/llama-cpp-turboquant)
+
+---
+
+---
+
+## 🇩🇪 Deutsch
+
+### TurboQuant auf Consumer-Hardware — Praktischer Guide
+
+Dieses Repository dokumentiert unsere Erfahrungen beim Einsatz von TurboQuant (ICLR 2026)
+auf einer RTX 3090 im Homelab-Betrieb. Wir sind das erste europäische Team,
+das diese Methode praktisch auf Consumer-Hardware veröffentlicht dokumentiert hat.
+
+### Das Ergebnis
+
+Mit TurboQuant turbo3 (3-bit KV-Cache) haben wir auf einer RTX 3090 (24 GB):
+
+- **12× mehr Context** (8.192 → 100.000 Tokens)
+- nur **+1.8 GB VRAM** Mehrverbrauch
+- nur **−8.5% Geschwindigkeitsverlust**
+- **gleiche Modellgewichte** — nur der Laufzeit-Cache wird komprimiert
+
+### Warum das wichtig ist
+
+Größerer Context bedeutet: Längere Dokumente, mehr Gesprächshistorie, besseres RAG,
+Code-Analyse ganzer Codebasen — alles auf einer einzigen Consumer-GPU.
+
+### Fehler-Protokoll (5 Fehler die wir gemacht haben)
+
+Alle 5 Fehler aus unserem Setup sind unter [Errors & Fixes](#-errors--fixes) dokumentiert.
+Der häufigste: falscher Branch (`master` statt `feature/turboquant-kv-cache`).
+
+### Schnellstart (Deutsch)
+
+```bash
+# Image bauen (~20 Minuten)
+docker build -t turboquant:feature .
+
+# Modell herunterladen (14 GB)
+export HF_TOKEN=dein_token
+bash scripts/download-model.sh
+
+# Baseline starten (f16, 8K Context)
+bash scripts/run-baseline.sh
+
+# TurboQuant starten (turbo3, 100K Context)
+bash scripts/run-turbo.sh
+```
+
+Vollständige deutsche Dokumentation: [`WHITEPAPER.de.md`](WHITEPAPER.de.md)
+
+---
+
+*AI Engineering Lab · April 2026 · [ai-engineering.at](https://ai-engineering.at)*

WHITEPAPER.de.md

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+# TurboQuant auf Consumer-Hardware
+## 100.000 Token Context auf einer RTX 3090 — Schritt für Schritt
+
+> AI Engineering Lab | April 2026
+> Getestet auf: NVIDIA RTX 3090 (24 GB VRAM), Windows + Docker Desktop
+> Modell: Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M
+
+---
+
+## Executive Summary
+
+TurboQuant ist eine KV-Cache-Quantisierungsmethode aus dem Paper
+"TurboQuant: Ultra-Low-Bit KV-Cache Quantization for LLMs" (ICLR 2026, arXiv:2504.19874).
+
+**Das Ergebnis in einer Zeile:**
+Mit TurboQuant turbo3 (3-bit KV-Cache) erreichen wir auf einer RTX 3090
+einen Context von **100.000 Tokens** — bei nur 8,5% Geschwindigkeitsverlust
+und ohne Änderung der Modellgewichte.
+
+| | Baseline (f16) | TurboQuant turbo3 | Delta |
+|--|:---:|:---:|:---:|
+| **Context** | 8.192 | **100.000** | **+12,2×** |
+| **VRAM** | 15,4 GB | 17,2 GB | +1,8 GB |
+| **Tokens/s** | 49,2 | 45,0 | −8,5% |
+| **KV-Cache** | ~1 GB (f16) | ~2,8 GB (3-bit) | 4,3× Kompression |
+
+---
+
+## 1. Das Problem: Der KV-Cache frisst VRAM
+
+Wenn ein LLM läuft, berechnet es für jeden Token Key-Value-Paare (KV).
+Diese werden im VRAM gecacht, damit spätere Tokens darauf zugreifen können.
+
+Das Problem: Der KV-Cache **wächst linear mit der Kontextlänge**.
+
+Für Mistral-Small-3.2 24B auf einer RTX 3090 (24 GB, davon ~14,4 GB für Modellgewichte):
+
+```
+Context       KV-Cache (f16)    Verfügbar nach Modell    Passt?
+  8.192           ~1 GB              9,6 GB                 ✅
+ 32.000           ~4 GB              9,6 GB                 ✅
+100.000          ~12 GB              9,6 GB                 ❌ OOM
+100.000        ~2,8 GB (turbo3)      9,6 GB                 ✅
+```
+
+100K Context ist ohne Optimierung auf einer 24-GB-GPU schlicht nicht möglich.
+
+---
+
+## 2. Was TurboQuant macht
+
+TurboQuant komprimiert den KV-Cache von 16-bit auf 2–4-bit.
+**NICHT die Modellgewichte** — nur den Laufzeit-Cache.
+
+```
+f16 KV-Cache (16 bit) → turbo3 KV-Cache (3 bit) = 4,3× weniger Speicher
+```
+
+Das Modell liest den quantisierten Cache und generiert Text ganz normal.
+Qualitätsverlust: laut Paper <1% Perplexity-Anstieg bei turbo3.
+
+---
+
+## 3. Das Ecosystem — Zwei Repos, ein häufiger Fehler
+
+Es gibt zwei TurboQuant-Repositories mit verwirrenden Namen:
+
+| Repo | Was es ist | Wann benutzen |
+|------|-----------|--------------|
+| `TheTom/turboquant_plus` | Python-Bibliothek | HuggingFace-Modelle, Forschung |
+| `TheTom/llama-cpp-turboquant` | llama.cpp-Fork | **Dieser Guide — llama-server** |
+
+**Dieser Guide verwendet `TheTom/llama-cpp-turboquant`, Branch `feature/turboquant-kv-cache`.**
+
+Kritisch: Der Default-Branch `master` ist ein normales llama.cpp **ohne TurboQuant**.
+Die Implementierung liegt auf `feature/turboquant-kv-cache`.
+
+---
+
+## 4. Setup — Schritt für Schritt
+
+### 4.1 Branch verifizieren (vor dem Build!)
+
+```bash
+curl -s "https://api.github.com/repos/TheTom/llama-cpp-turboquant/branches" \
+  | python3 -c "import sys,json; [print(b['name']) for b in json.load(sys.stdin)]"
+# Erwartet: feature/turboquant-kv-cache, master
+```
+
+### 4.2 Docker Image bauen (~20 Minuten)
+
+```bash
+docker build -t turboquant:feature .
+
+# Verifizieren: turbo2, turbo3, turbo4 müssen erscheinen
+docker run --rm turboquant:feature llama-server -h 2>&1 | grep -A3 "cache-type-k"
+```
+
+### 4.3 Modell herunterladen (~14 GB)
+
+```bash
+export HF_TOKEN=hf_dein_token
+bash scripts/download-model.sh
+```
+
+### 4.4 Baseline starten (Referenzwert)
+
+```bash
+bash scripts/run-baseline.sh
+# → Port 8180, f16 KV-Cache, 8192 Context
+```
+
+### 4.5 TurboQuant starten
+
+```bash
+bash scripts/run-turbo.sh
+# → Port 8182, turbo3 KV-Cache, 100.000 Context
+```
+
+---
+
+## 5. Fehler-Protokoll
+
+Alle 5 Fehler aus unserem Setup — damit du sie nicht wiederholst:
+
+### Fehler 1: Falsches Repository
+**Symptom:** Kein `turbo2`/`turbo3`/`turbo4` nach dem Build.
+**Ursache:** `TheTom/turboquant_plus` (Python-Bibliothek) statt `TheTom/llama-cpp-turboquant` gebaut.
+**Fix:** Richtiges Repo verwenden — siehe Dockerfile.
+
+### Fehler 2: Falsches cmake-Flag
+**Symptom:** Kein CUDA, CPU-Fallback.
+**Ursache:** `-DLLAMA_CUBLAS=ON` wurde umbenannt.
+**Fix:** `-DGGML_CUDA=ON` (modernes llama.cpp, post-GGML-Refactor).
+
+### Fehler 3: libcuda.so.1 fehlt beim Build
+**Symptom:** Linker-Fehler beim cmake-Build.
+**Ursache:** Docker-Build-Time hat keinen NVIDIA-Treiber — nur ein Stub ohne `.1`-Suffix.
+**Fix:** Symlink VOR cmake setzen (siehe Dockerfile).
+
+### Fehler 4: Falscher Branch
+**Symptom:** `Unsupported cache type: turbo3` zur Laufzeit.
+**Ursache:** Master-Branch geklont (standard llama.cpp, kein TurboQuant).
+**Fix:** `git clone --branch feature/turboquant-kv-cache`
+
+### Fehler 5: Falscher HuggingFace-Repo-Name
+**Symptom:** 404 beim Modell-Download.
+**Ursache:** Repo-Namen aus dem Gedächtnis rekonstruiert — falsch.
+**Fix:** Immer live via HF Search API verifizieren (nie aus dem Kontext nehmen).
+
+---
+
+## 6. Benchmark-Methodik
+
+Jede Messung:
+1. VRAM messen nach Server-Start (nvidia-smi)
+2. 3× curl an `/v1/chat/completions` mit "Count from 1 to 200"
+3. Durchschnitt aus 3 Läufen
+4. Container stoppen, 30s warten, nächste Messung
+
+TPS-Berechnung: `completion_tokens / (total_duration_ms / 1000)`
+
+---
+
+## 7. Produktions-Checkliste
+
+Bevor du TurboQuant in Produktion einsetzt:
+
+- [ ] Image gebaut von `feature/turboquant-kv-cache` (NICHT master)
+- [ ] Verifiziert: `llama-server -h | grep turbo` zeigt turbo2, turbo3, turbo4
+- [ ] VRAM-Budget berechnet: Modell + KV-Cache + Overhead ≤ GPU-VRAM
+- [ ] Port-Konflikte geprüft (kein anderer Service auf dem Port)
+- [ ] Startup-Zeit eingeplant: 100K Context braucht ~90s Startzeit
+- [ ] Qualität getestet: Stichproben-Outputs mit turbo3 vs f16 verglichen
+- [ ] Modell-Download via HF Search API verifiziert (nicht aus Erinnerung)
+
+---
+
+## 8. Rohdaten
+
+Alle Benchmark-Rohdaten: [`results/turboquant-rtx3090-2026-04-01.json`](results/turboquant-rtx3090-2026-04-01.json)
+
+---
+
+*AI Engineering Lab · April 2026 · [ai-engineering.at](https://ai-engineering.at)*
+*Basierend auf TurboQuant (arXiv:2504.19874) von Thomas et al. (ICLR 2026)*

results/turboquant-rtx3090-2026-04-01.json

@@ -0,0 +1,92 @@
+{
+  "date": "2026-04-01",
+  "hardware": {
+    "node": ".90",
+    "gpu": "NVIDIA GeForce RTX 3090",
+    "vram_total_mb": 24576,
+    "vram_total_gb": 24.0,
+    "driver": "CUDA 12.6"
+  },
+  "model": {
+    "name": "Mistral-Small-3.2-24B-Instruct-2506",
+    "file": "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf",
+    "hf_repo": "bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF",
+    "quantization": "Q4_K_M",
+    "size_gb": 14.0
+  },
+  "llama_cpp_build": {
+    "repo": "TheTom/llama-cpp-turboquant",
+    "branch": "feature/turboquant-kv-cache",
+    "commit": "feature/turboquant-kv-cache@depth1",
+    "image_baseline": "turboquant-plus:latest",
+    "image_turboquant": "turboquant-plus:feature",
+    "note_e116": "turboquant-plus:latest was built from master branch WITHOUT turbo cache types. turboquant-plus:feature built from feature/turboquant-kv-cache has turbo2/turbo3/turbo4."
+  },
+  "baseline": {
+    "cache_type_k": "f16",
+    "cache_type_v": "f16",
+    "max_context": 8192,
+    "port": 8180,
+    "vram_total_mb": 15748,
+    "vram_total_gb": 15.38,
+    "vram_model_only_estimate_gb": 14.4,
+    "vram_kv_cache_estimate_gb": 0.98,
+    "tps_run1": 49.6,
+    "tps_run2": 48.9,
+    "tps_run3": 49.2,
+    "tps_avg": 49.2,
+    "toolcall_note": "INCOMPATIBLE: 'Failed to parse input at pos 70: </s>' — Mistral chat template + this llama.cpp version. tool_choice=auto triggers grammar-constrained parsing which fails on Mistral EOS token.",
+    "toolcall_score": null,
+    "toolcall_max": 10
+  },
+  "turbo3": {
+    "cache_type_k": "turbo3",
+    "cache_type_v": "turbo3",
+    "max_context": 100000,
+    "port": 8182,
+    "vram_total_mb": 17634,
+    "vram_total_gb": 17.22,
+    "vram_model_only_estimate_gb": 14.4,
+    "vram_kv_cache_estimate_gb": 2.82,
+    "tps_run1": 45.7,
+    "tps_run2": 44.2,
+    "tps_run3": 45.2,
+    "tps_avg": 45.0,
+    "toolcall_note": "NOT TESTED (same toolcall issue as baseline — Mistral template incompatibility, not TurboQuant-related)",
+    "toolcall_score": null,
+    "toolcall_max": 15
+  },
+  "analysis": {
+    "kv_cache_compression": {
+      "f16_8k_gb": 0.98,
+      "turbo3_100k_gb": 2.82,
+      "f16_100k_estimate_gb": 12.25,
+      "note": "f16 100K would require ~12.25GB KV-cache (OOM on RTX 3090 after 14.4GB model). turbo3 at 100K uses only 2.82GB.",
+      "turbo3_vs_f16_at_100k_ratio": 4.34,
+      "context_extension_factor": 12.2,
+      "context_extension_x": "8192 → 100000 (12.2x)"
+    },
+    "vram_delta": {
+      "baseline_mb": 15748,
+      "turbo3_mb": 17634,
+      "delta_mb": 1886,
+      "delta_gb": 1.84,
+      "note": "Turbo3 at 100K uses only 1.84GB MORE VRAM than baseline at 8K, despite 12x larger context"
+    },
+    "tps_delta": {
+      "baseline_avg": 49.2,
+      "turbo3_avg": 45.0,
+      "delta_pct": -8.5,
+      "note": "8.5% TPS reduction with turbo3 — within expected range (<10%). KV-cache reads are slightly more complex."
+    },
+    "recommendation": "TurboQuant turbo3 is production-ready for workloads requiring >8K context. At 100K context, it uses only 2.82GB KV-cache vs ~12GB for f16 (4.3x compression). TPS drops from 49.2 to 45.0 (-8.5%), which is acceptable for long-context use cases. For short-context (<8K) high-throughput workloads, f16 is still preferred."
+  },
+  "errors": ["E111", "E112", "E113", "E114", "E116"],
+  "learnings": ["L191", "L192", "L193"],
+  "notes": [
+    "E116: Default master branch of TheTom/llama-cpp-turboquant does NOT have TurboQuant. Must use --branch feature/turboquant-kv-cache",
+    "Port 8181 is occupied by ocr-api-waitress on .90. TurboQuant turbo3 uses port 8182.",
+    "ToolCall-15 test not completed: Mistral chat template incompatibility with grammar-constrained tool parsing in this llama.cpp version. Not a TurboQuant issue.",
+    "Model used: Mistral-Small-3.2-24B (instead of planned Qwen3.5-27B due to E114 hallucinated repo name)"
+  ]
+}

scripts/download-model.sh

@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+# Download Mistral-Small-3.2-24B-Instruct Q4_K_M GGUF model
+#
+# Usage: export HF_TOKEN=hf_... && bash scripts/download-model.sh
+#
+# Always verify the repo name via HF Search API before downloading.
+# HF repo names change and compressed context can reconstruct them incorrectly.
+
+set -e
+
+MODEL_REPO="bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF"
+MODEL_FILE="mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf"
+VOLUME_NAME="${VOLUME_NAME:-turboquant-models}"
+
+if [ -z "$HF_TOKEN" ]; then
+  echo "ERROR: HF_TOKEN not set."
+  echo "Get a free token at: https://huggingface.co/settings/tokens"
+  echo "Then: export HF_TOKEN=hf_..."
+  exit 1
+fi
+
+echo "=== Verifying repo exists ==="
+HF_CHECK=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "Authorization: Bearer $HF_TOKEN" \
+  "https://huggingface.co/api/models/${MODEL_REPO}")
+
+if [ "$HF_CHECK" != "200" ]; then
+  echo "ERROR: Repo not found or unauthorized (HTTP $HF_CHECK)"
+  echo "Search for available repos:"
+  curl -s -H "Authorization: Bearer $HF_TOKEN" \
+    "https://huggingface.co/api/models?search=bartowski+mistral+small+3.2&limit=5" \
+    | python3 -c "import sys,json; [print(m['modelId']) for m in json.load(sys.stdin)]"
+  exit 1
+fi
+
+echo "Repo: ${MODEL_REPO} ✓"
+echo "File: ${MODEL_FILE}"
+echo "Volume: ${VOLUME_NAME}"
+echo ""
+
+docker volume create ${VOLUME_NAME} 2>/dev/null || true
+
+echo "=== Downloading (~14 GB, may take 20-30 min) ==="
+docker run --rm \
+  -v ${VOLUME_NAME}:/models \
+  -e HF_TOKEN="${HF_TOKEN}" \
+  python:3.11-slim \
+  bash -c "
+    pip install -q huggingface_hub && \
+    python -c \"
+import os
+from huggingface_hub import hf_hub_download
+path = hf_hub_download(
+    repo_id='${MODEL_REPO}',
+    filename='${MODEL_FILE}',
+    local_dir='/models',
+    resume_download=True,
+    token=os.environ.get('HF_TOKEN')
+)
+print('Downloaded to:', path)
+print('Size: {:.1f} GB'.format(os.path.getsize(path) / 1e9))
+\"
+  "
+
+echo ""
+echo "=== Done ==="
+echo "Model ready at: /models/${MODEL_FILE} (in Docker volume '${VOLUME_NAME}')"
+echo "Run: bash scripts/run-baseline.sh"

scripts/run-baseline.sh

@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+# TurboQuant Baseline — f16 KV-Cache, context=8192
+# Reference measurement for comparison with TurboQuant run
+#
+# Usage: bash scripts/run-baseline.sh [model-path] [port]
+# Default model: /models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf
+# Default port: 8180
+
+MODEL="${1:-/models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf}"
+PORT="${2:-8180}"
+VOLUME="${VOLUME_NAME:-turboquant-models}"
+IMAGE="${IMAGE:-turboquant:feature}"
+
+echo "=== TurboQuant Baseline Run ==="
+echo "Model: $MODEL"
+echo "Cache: f16 (full precision)"
+echo "Context: 8192 tokens"
+echo "Port: $PORT"
+echo ""
+
+# Stop any existing baseline container
+docker rm -f turboquant-baseline 2>/dev/null || true
+
+docker run --rm --gpus all \
+  -v "${VOLUME}:/models" \
+  -p "${PORT}:8180" \
+  --name turboquant-baseline \
+  "${IMAGE}" \
+  llama-server \
+    --model "${MODEL}" \
+    --cache-type-k f16 \
+    --cache-type-v f16 \
+    -c 8192 \
+    --host 0.0.0.0 \
+    --port 8180 \
+    -ngl 99
+
+echo ""
+echo "Baseline serving at: http://localhost:${PORT}"
+echo "OpenAI-compatible:   http://localhost:${PORT}/v1/chat/completions"
+echo ""
+echo "After startup (~45s), measure VRAM: nvidia-smi --query-gpu=memory.used --format=csv,noheader"

scripts/run-turbo.sh

@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+# TurboQuant turbo3 — 3-bit KV-Cache, context=100000
+# 12× more context than baseline, +1.8 GB VRAM only
+#
+# Usage: bash scripts/run-turbo.sh [model-path] [port]
+# Default model: /models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf
+# Default port: 8182
+#
+# NOTE: Port 8180 is used by the baseline run. Use a different port here.
+
+MODEL="${1:-/models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf}"
+PORT="${2:-8182}"
+VOLUME="${VOLUME_NAME:-turboquant-models}"
+IMAGE="${IMAGE:-turboquant:feature}"
+
+echo "=== TurboQuant turbo3 Run ==="
+echo "Model: $MODEL"
+echo "Cache: turbo3 (3-bit KV quantization)"
+echo "Context: 100,000 tokens"
+echo "Port: $PORT"
+echo ""
+echo "Expected VRAM: ~17.2 GB (+1.8 GB vs baseline)"
+echo "Expected TPS: ~45 (-8.5% vs baseline)"
+echo ""
+
+# Stop any existing turbo container
+docker rm -f turboquant-turbo3 2>/dev/null || true
+
+docker run --rm --gpus all \
+  -v "${VOLUME}:/models" \
+  -p "${PORT}:8182" \
+  --name turboquant-turbo3 \
+  "${IMAGE}" \
+  llama-server \
+    --model "${MODEL}" \
+    --cache-type-k turbo3 \
+    --cache-type-v turbo3 \
+    -c 100000 \
+    --host 0.0.0.0 \
+    --port 8182 \
+    -ngl 99
+
+echo ""
+echo "TurboQuant serving at: http://localhost:${PORT}"
+echo "OpenAI-compatible:     http://localhost:${PORT}/v1/chat/completions"
+echo ""
+echo "After startup (~90s, 100K context allocation takes longer):"
+echo "  VRAM: nvidia-smi --query-gpu=memory.used --format=csv,noheader"