LLamaGPT AI GUI Rakyat Edition VS ChatGPT Gratisan
📌 Model yang sudah diuji / Tested Model GGUF
| No | Nama Model GGUF | Ukuran Quant | RAM yang Digunakan | OS & Kondisi Tambahan | Status GUI |
|---|---|---|---|---|---|
| 1 | luna-ai-llama2-uncensored.Q4_0.gguf | Q4_0 | ±11.6 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + music & anime video | Stable & Smooth |
| 2 | Meta-Llama-3-8B-Instruct.Q8_0.gguf | Q8_0 | ±10.8 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + music & anime video | Stable & Smooth |
| 3 | WizardLM-13B-Uncensored.Q5_K_M.gguf | Q5_K_M | 12,6 GB of 16 GB | Windows 11 pro 24H2 + Office 2024 | Tested + Stable & Smooth |
| 4 | wizardcoder-python-13b-v1.0.Q5_K_M.gguf | Q5_K_M | 12,6 GB of 16 GB | Windows 11 pro 24H2 + Office 2024 | Tested + Stable & Smooth |
| 5 | All 7B | Q4_K_M | ≤11.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + music video 720p | Stable & Smooth |
| 6 | All 13B (Kecuali Yi 13B) | Q4_K_M | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + music video 720p | Stable & Smooth |
| 7 | deepseek-coder-7b-instruct-v1.5-Q8_0.gguf | Q8_0 | 10.8 GB of 16GB | Windows 11 pro 24H2 + Office 2024 | Stable & Smooth |
| 8 | deepseek-coder-1.3b-instruct.Q4_0.gguf | Q4_0 | 4 GB of 16GB | Windows 11 pro 24H2 + Office 2024 | Stable & Smooth |
| 9 | codellama-13b.Q6_K.gguf | Q6_K | ≤15.4 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad++ | Stable & Smooth |
| 10 | starcoder2-15b-Q5_K_M (1).gguf | Q5_K_M | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad++ | Stable & Smooth |
| 11 | DeepSeek-Coder-V2-Lite-Instruct-Q5_K_M.gguf | Q5_K_M | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad++ | Stable & Smooth |
| 12 | Llama-3-16B.Q5_K_M.gguf | Q5_K_M | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad++ | Stable & Smooth |
| 13 | orcamaidxl-17b-32k.Q5_K_M.gguf | Q5_K_M | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome | Stable & Smooth |
| 14 | llava-v1.5-13b-Q8_0.gguf | Q8_0 | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad + Notepad++ | Stable & Smooth |
| 15 | InternVL3-8B-Instruct-UD-Q8_K_XL.gguf | Q8_K_XL | ≤14.2 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad + Notepad++ | Stable & Smooth |
| 16 | InternVL3-14B-Instruct-Q6_K.gguf | Q6_K | ≤15.5 GB of 16GB | Windows 11 pro 24H2 + Office 2024 + Chrome + Notepad + Notepad++ | Stable & Smooth |
📌 AI Memory (ID)
| Aspek | ChatGPT Gratisan | Offline AI Memory Custom |
|---|---|---|
| Tipe memory | Ephemeral session memory → hanya konteks percakapan saat ini. | Persistent memory → bisa disimpan ke TXT, DOCX, database, atau format GGUF. |
| Batas konteks | ±3.000–4.000 token per sesi (~2–3 halaman teks panjang). | Tidak ada batas teoretis selain kapasitas hardisk. Bisa ratusan ribu baris teks, ratusan MB bahkan GB. |
| Persistence | Hilang saat tab ditutup atau sesi selesai. | Selalu tersimpan di file lokal, bisa dipanggil kapan saja, tetap ada meski PC mati. |
| Kontrol | User tidak bisa memilih atau menambah memory jangka panjang. | User bebas menambah, menghapus, atau mengupdate memory sesuai kebutuhan. Bisa dikustomisasi penuh. |
| Privasi | Tergantung server OpenAI → data dikirim online. | 100% offline → semua data tetap di PC sendiri. |
| Kapasitas “hard limit” | Sangat terbatas → model harus melupakan bagian awal percakapan saat token habis. | Hanya dibatasi storage → bisa diisi topik ribuan halaman atau dataset besar, termasuk “topik terlarang” kalau mau. |
| Fleksibilitas | Sedikit → cuma bisa mengandalkan konteks aktif. | Sangat tinggi → bisa buat index, search, recall, role mode, chunking, bahkan resume panjang. |
📌 AI Memory (EN)
| Aspect | Free ChatGPT | Offline Custom AI Memory |
|---|---|---|
| Memory Type | Ephemeral session memory → only stores the current conversation context. | Persistent memory → can be saved to TXT, DOCX, databases, or GGUF format. |
| Context Limit | ±3,000–4,000 tokens per session (~2–3 long text pages). | No theoretical limit besides hard drive capacity. Can store hundreds of thousands of text lines, hundreds of MBs or even GBs. |
| Persistence | Lost when the tab is closed or session ends. | Always saved locally, can be recalled anytime, remains even if the PC is off. |
| Control | User cannot select or add long-term memory. | User can freely add, delete, or update memory as needed. Fully customizable. |
| Privacy | Depends on OpenAI servers → data sent online. | 100% offline → all data stays on your PC. |
| Hard Capacity Limit | Very limited → model must forget earlier parts of conversation when tokens run out. | Only limited by storage → can hold thousands of pages or large datasets, including “restricted topics” if desired. |
| Flexibility | Limited → only relies on active context. | Very high → supports indexing, searching, recall, role modes, chunking, and long resume functions. |
📊 Perbandingan Model GGUF untuk Analisis Data
| Model | RAM Usage (estimasi) | Kecepatan (tokens/s) | Kualitas Reasoning (1–5) | Catatan |
|---|---|---|---|---|
| mistral-7b-instruct-v0.3-q4_k_m | ±4–5 GB | 🔹 Cepat (20–30 tok/s) | ⭐⭐⭐ (3/5) | Ringan, cocok buat data text/csv sederhana. |
| Llama-3-16B.Q5_K_M | ±12–14 GB | 🔹 Sedang (8–12 tok/s) | ⭐⭐⭐⭐ (4/5) | Reasoning bagus, pas buat analisis tabel besar. |
| InternVL3-8B-Instruct-UD-Q8_K_XL | ±8–9 GB | 🔹 Sedang (12–18 tok/s) | ⭐⭐⭐⭐ (4/5) | Multimodal support, bagus untuk teks+visual. |
| InternVL3-14B-Instruct-UD-Q6_K_XL | ±12–13 GB | 🔹 Lebih lambat (6–10 tok/s) | ⭐⭐⭐⭐⭐ (5/5) | Analisis kompleks kuat, lebih stabil di reasoning tabel. |
| Qwen2.5-Omni-7B-UD-Q8_K_XL | ±7–8 GB | 🔹 Cepat (18–25 tok/s) | ⭐⭐⭐⭐ (4/5) | Multimodal + reasoning lumayan rapi. |
| Meta-Llama-3-8B-Instruct.Q8_0 | ±7–8 GB | 🔹 Sedang (15–20 tok/s) | ⭐⭐⭐⭐ (4/5) | Balanced, lumayan smooth buat dokumen Excel besar. |
| llama-2-13b-chat.Q4_K_M | ±8–9 GB | 🔹 Sedang (12–16 tok/s) | ⭐⭐⭐ (3/5) | Masih lumayan, tapi kalah akurat dibanding Llama 3. |
| llava-v1.5-13b-Q8_0 | ±9–10 GB | 🔹 Lambat (8–12 tok/s) | ⭐⭐⭐⭐ (4/5) | Kuat di multimodal, pas buat CSV + chart/gambar. |
| llava-v1.6-vicuna-13b.Q6_K | ±10–11 GB | 🔹 Lambat (7–11 tok/s) | ⭐⭐⭐⭐ (4/5) | Versi lebih baru, multimodal lebih rapi. |
📊 Comparison Table of GGUF Models for Data Analysis
| Model (GGUF) | RAM Usage (16GB System) | Speed (Excel/CSV Processing) | Reasoning Quality (Data Insights) | Best Use Case |
|---|---|---|---|---|
| LLaMA-2 7B GGUF | 🟢 ~6–7GB | ⚡ Fast (lightweight) | 🟡 Basic – handles simple analysis, summaries | Quick reports & simple stats |
| Mistral 7B GGUF | 🟢 ~6–7GB | ⚡⚡ Very Fast | 🟢 Better reasoning than LLaMA-2 7B | Exploratory data analysis, light BI |
| LLaMA-2 13B GGUF | 🟡 ~12–13GB | ⚡ Medium | 🟢🟢 Strong reasoning, more accurate correlations | Deeper insights & medium datasets |
| Mixtral 8x7B GGUF (MoE) | 🟡 ~10–12GB (active params only) | ⚡⚡ Good speed for large model | 🟢🟢 Excellent logical reasoning | Complex trend analysis & forecasting |
| LLaMA-3 8B GGUF | 🟢 ~7–8GB | ⚡⚡ Fast | 🟢 Stronger logical flow than LLaMA-2 | Balanced – business analysis & predictions |
| LLaMA-3 70B GGUF | 🔴 ~40–48GB (not practical for 16GB) | ⚠️ Very slow/offloading needed | 🟢🟢🟢 Near-human reasoning | Enterprise-scale BI (requires big GPU/cluster) |
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