Best Local LLM for RTX 4070 Ti SUPER (2026): 16GB VRAM Picks
The RTX 4070 Ti SUPER hits a sweet spot for local LLMs: 16 GB VRAM at 672 GB/s bandwidth, retail around $800. Enough room for Qwen 3.5 9B at full Q8, gpt-oss 20B at Q4 (the OpenClaw production pick), or a Qwen 3.6 27B squeeze at IQ3.
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Bottom Line
- Best quality: Qwen 3.5 9B at Q8_0 (~45 tok/sec)
- Best for OpenClaw: gpt-oss 20B at Q4_K_M (cleanest tool calls)
- Best squeeze: Qwen 3.6 27B at IQ3_XS (degraded but capable)
- Skip: 70B at any quant
Top Picks for RTX 4070 Ti SUPER (16 GB VRAM, 672 GB/s)
1. Qwen 3.5 9B (Q8_0) — best quality
About 10 GB at full Q8, near-FP16 quality with 64K context. Strong reasoning, decent code, multimodal capable.
ollama pull qwen3.5:9b-q8_0 openclaw config set agents.defaults.models.chat ollama/qwen3.5:9b-q8_0
Expected speed: 40-50 tokens/sec.
2. gpt-oss 20B (Q4_K_M) — best for OpenClaw production
About 13 GB at Q4_K_M with 16K context. The cleanest tool-call JSON of any open-weight model.
ollama pull gpt-oss:20b openclaw config set agents.defaults.models.chat ollama/gpt-oss:20b openclaw config set agents.defaults.context_limit 16000
3. Qwen 3.6 27B (IQ3_XS) — capability squeeze
The brand-new (April 22, 2026) 27B model at IQ3_XS uses about 11 GB. Scores 77.2 on SWE-Bench Verified — outperforming the 397B Qwen 3.5 MoE on agentic coding. Quality degraded at IQ3 but still beats most 14B models at higher quants.
4. Phi-4 14B (Q4_K_M) — math/reasoning specialist
Microsoft’s Phi-4 at Q4 uses about 9 GB. Best in class for math and step-by-step reasoning at this size.
5. Mistral Nemo 12B (Q5_K_M) — long context
Native 128K context. About 9 GB at Q5. Pick this if you regularly paste long documents.
OpenClaw Setup on RTX 4070 Ti SUPER
ollama pull gpt-oss:20b openclaw config set agents.defaults.models.chat ollama/gpt-oss:20b openclaw config set agents.defaults.context_limit 16000 openclaw config set agents.defaults.fallback openrouter/qwen/qwen-3.6-27b
Common Mistakes on RTX 4070 Ti SUPER
- Picking the 12 GB regular 4070 by mistake. The “Ti SUPER” 16 GB variant is what you need. The 4070 (12 GB) is too tight for 20B Q4 + context.
- Trying Llama 3.3 70B at IQ2. Doesn’t fit, and the quality wouldn’t be worth it even if it did. Stick with Qwen 3.5 9B at Q8 or gpt-oss 20B at Q4.
- Running 128K context with Qwen 3.5 9B Q8. KV cache alone eats 8 GB. Cap at 32K to leave headroom.
🛒 Mac alternative
Want 16-24GB unified memory in a quiet laptop? MacBook Pro M-series matches the 4070 Ti SUPER's workload.
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See Also
- Best Local LLM for RTX 4060 Ti 16GB → — budget alternative
- Best Local LLM for RTX 3090 — step up to 24GB
- Best Local LLM by GPU (hub)
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