Best Local LLM for RTX 4080 & 4080 Super (2026): 16GB VRAM Picks
The RTX 4080 and 4080 Super both ship 16 GB of VRAM, which puts them squarely in the 'gpt-oss 20B at Q4' tier for local LLMs and OpenClaw agents.
Picking hardware for an OpenClaw host?
Use the local model calculator first, then see our AI training options if you want help matching your workload to the right rig.
Short answer: on a 16 GB RTX 4080 or 4080 Super, run gpt-oss 20B at Q4_K_M for OpenClaw agent work (clean tool calls, ~30 tok/sec) or Qwen 3.5 9B at Q8 when you want maximum quality on a smaller model. 16 GB is a real 16 GB card — it does not fit Qwen 3.6 27B at a good quant, and 70B is out of reach on a single card.
The VRAM Math
The 4080 Super edges the original 4080 on memory bandwidth (~736 vs ~717 GB/s), a few percent faster in practice; both are the same 16 GB fit class.
What Actually Fits (Model Picks)
| Model | Quant | VRAM used | Speed | Notes |
|---|---|---|---|---|
| Qwen 3.5 9B | Q8_0 | ~10 GB | ~40 tok/s | Highest-quality small model |
| gpt-oss 20B | Q4_K_M | ~12-13 GB | ~30 tok/s | Best OpenClaw agent pick |
| Qwen 3.6 27B | Q3_K_S | ~14 GB tight | ~25 tok/s | Fits but quality drops at Q3 |
| Llama 3.1 8B | Q8_0 | ~9 GB | ~45 tok/s | Fast general assistant |
What You Can’t Run
- Qwen 3.6 27B at Q4_K_M or higher — needs ~17-18 GB, over the 16 GB ceiling.
- Any 70B model at a usable quant — a single 16 GB card is far short; you need 24 GB+ or two cards.
- Long 128K context on a 20B model — the KV cache pushes you over 16 GB; cap context at 16-32K.
4080/4080 Super listings come and go; for a linkable 16 GB card the 4070 Ti Super is the same fit class. Want to run 27B at a good quant? Step up to a 24 GB 4090 or the 32 GB 5090.
OpenClaw Setup
Point OpenClaw at your local model through Ollama:
# pull and run your pick, then set it as the OpenClaw default ollama pull gpt-oss:20b openclaw config set agents.defaults.models.chat "ollama/gpt-oss:20b"
For agent reliability, prefer a model with clean tool-call output (gpt-oss 20B where it fits) and cap context to what your memory holds. See the tool-calling reliability guide.
See Also
- Best Local LLM for RTX 4070 Ti Super 16GB — same 16GB tier
- Best Local LLM for RTX 4090 (24GB) — the 24GB step up
- Best Local LLM for RTX 5090 (32GB) — 32GB headroom
- Best Local LLM by GPU (hub)
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