Local LLM model fit
Qwen3.5 9B is a 9B Qwen3.5 model. This page estimates Q4 VRAM fit, Ollama command, context planning, and fallback choices for common local AI GPUs.
Modern multimodal local assistant, agent experiments, and coding support on mainstream GPUs. Weakness: Still a small model for large repo-scale coding tasks.
| Hardware | Examples | Clean capacity | Q4 need | Status | Calculator |
|---|---|---|---|---|---|
| 6 GB VRAM entry GPU | GTX 1660, RTX 2060 6GB | 4.50 GB clean VRAM | 6.60 GB | RAM offload | Open calculator |
| 8 GB VRAM mainstream GPU | RTX 3060 Ti, RTX 4060, RTX 3070 | 6.50 GB clean VRAM | 6.60 GB | RAM offload | Open calculator |
| 10 GB VRAM older high-end GPU | RTX 3080 10GB | 8.50 GB clean VRAM | 6.60 GB | Runs locally | Open calculator |
| 12 GB VRAM local agent GPU | RTX 3060 12GB, RTX 4070 | 10.5 GB clean VRAM | 6.60 GB | Runs locally | Open calculator |
| 16 GB VRAM creator GPU | RTX 4060 Ti 16GB, RTX 4080 | 14.5 GB clean VRAM | 6.60 GB | Runs locally | Open calculator |
| 24 GB VRAM homelab workstation | RTX 3090, RTX 4090 | 22.5 GB clean VRAM | 6.60 GB | Runs locally | Open calculator |
| 48 GB VRAM workstation | RTX A6000, L40S 48GB | 46.5 GB clean VRAM | 6.60 GB | Runs locally | Open calculator |
| Apple Silicon 32 GB unified memory | M2 Max 32GB, M3 Max 36GB | 26 GB unified | 6.60 GB | Runs locally | Open calculator |
| Quantization | Estimated memory | Use case |
|---|---|---|
| Q4 / 4-bit | 6.60 GB | Default local inference balance |
| Q5 / 5-bit | 8.25 GB | Better quality, more VRAM |
| Q8 / 8-bit | 13.2 GB | High quality, much more VRAM |
| FP16 / 16-bit | 26.4 GB | Mostly workstation/server use |
8B · Fast local chat, lightweight agents, low-cost local testing
8B · Fast general local assistant with reasoning/coding balance
8B · Local reasoning experiments and step-by-step technical analysis
7B · Small local coding assistant and agent tool generation