Best Local LLM for RTX 4070 Ti Super 16GB VRAM (2026)
If you searched for the best local LLM for RTX 4070 Ti or 4070ti llm, this is the 16GB VRAM answer: run Qwen 3.5 9B at Q8 for quality, gpt-oss 20B at Q4 for OpenClaw, or a Qwen 3.6 27B IQ3 squeeze only when you accept quality loss.
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The 4070 Ti Super's 16 GB and high memory bandwidth make it the fast 16 GB pick for 14B-class and tight 27B quants. The 4060 Ti 16 GB is the cheaper 16 GB option; a 24 GB RTX 4090 is the step up for bigger models.
Best Local LLM for RTX 4070 Ti Super: Short Answer
The RTX 4070 Ti Super is a good local LLM card because it has 16GB VRAM and much higher bandwidth than the RTX 4060 Ti 16GB. It is not a 70B card, and it is not a clean 27B Q4 card with long context.
- Best local LLM for RTX 4070 Ti Super: Qwen 3.5 9B at Q8_0.
- Best OpenClaw/Ollama pick: gpt-oss 20B at Q4_K_M.
- Best multimodal / long-context pick: Gemma 4 12B at Q5_K_M.
- Best 16GB VRAM squeeze: Qwen 3.6 27B at IQ3_XS, only if you accept degraded quality.
- Skip: 70B models, huge context windows, and the regular RTX 4070 12GB if you are buying for local LLMs.
What Fits in 16GB VRAM on RTX 4070 Ti Super?
| Workload | Model | Quant | VRAM | Verdict |
|---|---|---|---|---|
| Daily chat and coding | Qwen 3.5 9B | Q8_0 | ~10 GB | Best quality-to-speed balance |
| OpenClaw agents and tool calls | gpt-oss 20B | Q4_K_M | ~13 GB | Best production pick |
| Multimodal + long context | Gemma 4 12B | Q5_K_M | ~8.4 GB | 256K context, text/image/audio |
| Long documents | Mistral Nemo 12B | Q5_K_M | ~9 GB | Use when context matters |
| Math and step-by-step reasoning | Phi-4 14B | Q4_K_M | ~9 GB | Specialist model |
| Capability squeeze | Qwen 3.6 27B | IQ3_XS | ~11 GB | Better model class, lower quant quality |
| Avoid | 70B-class models | IQ2/Q2 | Does not fit cleanly | Bad daily-driver setup |
RTX 4070 Ti Super vs 4060 Ti 16GB vs 4090
| GPU | VRAM | Best local LLM role | When to choose it |
|---|---|---|---|
| RTX 4060 Ti 16GB | 16 GB | Budget gpt-oss 20B Q4 host | Cheapest usable 16GB option |
| RTX 4070 Ti Super | 16 GB | Faster 16GB OpenClaw/Ollama host | Same fit tier as 4060 Ti, much better speed |
| RTX 4090 | 24 GB | Qwen 3.6 27B Q4 host | Choose when 16GB VRAM feels tight |
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. Gemma 4 12B (Q5_K_M) — multimodal, 256K context
Google’s dense 12B (11.95B params) is the Gemma variant that actually belongs on a 16 GB card. Q4_K_M is 7.12 GB and Q5_K_M is 8.41 GB, so you have real room for KV cache instead of fighting for it. It is unified multimodal — text, image, audio and video in, 140+ languages — with a 256K context window.
Note the bigger Gemma is out of reach here: Gemma 4 26B-A4B is about 16.9 GB at Q4_K_M, which does not fit 16 GB before KV cache. That one is a 24 GB card’s pick.
ollama pull gemma4:12b-q5_K_M openclaw config set agents.defaults.models.chat ollama/gemma4:12b-q5_K_M
4. 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.
5. 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.
6. 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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