What is the best local LLM for an RTX 5070?

On the 12 GB RTX 5070, run Qwen 3.5 9B at Q6 or Q8 — it uses about 9-10 GB and is fast thanks to GDDR7 bandwidth. gpt-oss 20B does not leave enough room for context at 12 GB, so for agent work either step to the 16 GB 5070 Ti or use a cloud model.

Is the RTX 5070 Ti good for local LLMs?

Yes, for the 16 GB tier. The 5070 Ti runs gpt-oss 20B at Q4_K_M (~12-13 GB) with clean tool calls, making it a solid OpenClaw host. It cannot fit Qwen 3.6 27B at a good quant — that needs 24 GB.

RTX 5070 vs 5070 Ti for AI — which should I buy?

Buy the 5070 Ti if you want to run 20B-class agent models locally; its extra 4 GB (16 vs 12) is the difference between fitting gpt-oss 20B and being stuck on 9B models. Buy the 5070 only if your budget is tight and 9B assistants are enough.

Can the RTX 5070 run 70B models?

No. Neither the 12 GB 5070 nor the 16 GB 5070 Ti has anywhere near the ~40 GB a 70B model needs at Q4. For 70B locally, use a 32 GB+ card, dual GPUs, or a high-memory Mac.

← All guides

Hardware July 6, 2026

Best Local LLM for RTX 5070 & 5070 Ti (2026): Blackwell VRAM Picks

The RTX 5070 ships 12 GB and the 5070 Ti ships 16 GB. GDDR7 bandwidth makes both quick for their tier, but VRAM — not speed — decides which models you can load.

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: the RTX 5070 (12 GB) is a Qwen 3.5 9B card — run it at Q6/Q8 for a fast, high-quality small assistant. The RTX 5070 Ti (16 GB) steps up to gpt-oss 20B at Q4_K_M, the better OpenClaw agent pick. Neither fits Qwen 3.6 27B at a good quant; that needs 24 GB.

The VRAM Math

GDDR7 gives the 5070 series strong bandwidth for its class, so tokens/sec are high — but a fast 12 GB card still cannot load a model that does not fit. Match the model to the VRAM first.

What Actually Fits (Model Picks)

Card · Model	Quant	VRAM	Speed	Notes
RTX 5070 · Qwen 3.5 9B	Q6_K	~9 GB	~35 tok/s	Fast small assistant (12GB)
RTX 5070 · Llama 3.1 8B	Q8_0	~9 GB	~40 tok/s	General chat (12GB)
RTX 5070 Ti · gpt-oss 20B	Q4_K_M	~12-13 GB	~35 tok/s	Best OpenClaw pick (16GB)
RTX 5070 Ti · Qwen 3.5 9B	Q8_0	~10 GB	~45 tok/s	Max quality small (16GB)

What You Can’t Run

RTX 5070 (12 GB): gpt-oss 20B at a usable quant — 20B Q4 wants ~12-13 GB, too tight with any context; stay on 9B.
Either card: Qwen 3.6 27B at Q4+ — needs ~17-18 GB, beyond even the 16 GB 5070 Ti.
70B models — not on a single 12 or 16 GB card at any usable quant.

🎮 12 GB AND 16 GB CARDS YOU CAN BUY TODAY

5070-series listings vary; for a linkable 12 GB card the RTX 3060 is the value option, and the 4070 Ti Super covers the 16 GB tier. Want to run 27B well? Step up to the 32 GB 5090.

3060MSI RTX 3060 12 GB ↗ 4070TiSGIGABYTE RTX 4070 Ti Super 16 GB ↗ 5090GIGABYTE RTX 5090 32 GB ↗

OpenClaw Setup

Point OpenClaw at your local model through Ollama:

# pull and run your pick, then set it as the OpenClaw default
ollama pull qwen3:9b
openclaw config set agents.defaults.models.chat "ollama/qwen3:9b"

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.