Best AI Models for the RTX 4070
The mainstream card at the top of its tier — 14B LLMs with a ~55 tok/s bandwidth ceiling and Flux FP8 with room to spare
The best AI model for the RTX 4070 is Phi-4 14B — ~9GB at Q4_K_M with a bandwidth ceiling of ~55 tokens/sec on this card (504 GB/s ÷ 9.1GB — the top of the 12GB tier, and a ceiling rather than a measurement). For coding, Qwen 2.5 Coder 14B is the same size and the real step up from the 7B coders; for images, Flux.1 dev FP8 fits at ~11GB with no GGUF tricks. One warning before anything else: the “RTX 4070 Laptop” has 8GB, not 12GB — nothing in the 14B class fits it.
Models that run well on the RTX 4070
| Model | Size | Build | Memory used | Ceiling tok/s* |
|---|---|---|---|---|
| WINNERPhi-4 14B Best overall at 12GB — STEM and logic standout, with ~3GB left for the KV cache. ollama pull phi4 | 14B | Q4_K_M | ~9.0GB | ceiling ~55 tok/s (504 ÷ 9.1GB) |
| Qwen 2.5 Coder 14B Handles the multi-file refactors and repo questions the 7B coders fumble. ollama pull qwen2.5-coder:14b | 14B | Q4_K_M | ~9.0GB | ceiling ~56 tok/s (504 ÷ 9.0GB) |
| DeepSeek-Coder-V2 Lite 16B MoE — faster than the dense 14B at similar quality; the snappiest editor experience at 12GB. ollama pull deepseek-coder-v2:16b | 16B (MoE, 2.4B active) | Q4_K_M | ~8.9GB | MoE — ~2.4B active per token; bandwidth is not the limit; not measured |
| Gemma 3 12B Best multilingual and writing quality at this tier. ollama pull gemma3:12b | 12B | Q4_K_M | ~8.2GB | ceiling ~61 tok/s (504 ÷ 8.2GB) |
| Flux.1 dev FP8 Near-FP16 quality with no GGUF tricks — the 12GB Flux sweet spot. ComfyUI FP8 checkpoint | 12B | FP8 | ~11GB | diffusion — compute-bound, not timed |
| SDXL 1.0 The full toolkit fits: SDXL + ControlNet + a LoRA stack inside 12GB, no memory flags. ComfyUI / Forge / Automatic1111 | 3.5B | FP16 | ~7GB | diffusion — compute-bound, not timed |
| Llama 3.1 8B The speed lane — leaves ~7GB free for a long context or a second small model. ollama pull llama3.1:8b | 8B | Q4_K_M | ~5.0GB | ceiling ~103 tok/s (504 ÷ 4.9GB) |
* Ceiling tok/s = the device’s vendor-listed memory bandwidth ÷ the model’s file size as Ollama lists it — the rate a dense model cannot exceed, because every generated token reads the whole weight set once. We have not measured this hardware. Real-world generation usually lands at 50–80% of the ceiling and falls as the context grows; mixture-of-experts rows read only their active parameters, so no ceiling is quoted for them, and image models are compute-bound, so no render time is.
What won't fit on the RTX 4070
- ✗Codestral 22B (needs ~13GB) — Needs 16GB even with a short context.
- ✗gpt-oss:20b (needs ~14–16GB) — Sized for 16GB cards — one tier up.
- ✗Qwen 2.5 Coder 32B (needs ~20GB) — The SOTA local coder — still a 24GB card away.
RTX 4070-specific notes
- →The laptop trap, and it’s a big one: the “RTX 4070 Laptop” is an 8GB card on a 128-bit bus (256 GB/s vs the desktop’s 504). It cannot load anything in the 14B class — a 4070 laptop is an 8GB-tier machine, full stop. Our 8GB VRAM guide is its real page.
- →Variant map: the 4070, 4070 Super and 4070 Ti all carry 12GB — same model list, increasing speed. The 4070 Ti Super carries 16GB and changes the list (gpt-oss:20b, Codestral 22B). Check the actual memory spec, not the family name.
- →504 GB/s of memory bandwidth is the 4070’s quiet AI advantage — ~40% more than the RTX 3060 12GB, which lifts the bandwidth ceiling on every model by the same 40%. Same tier list, higher ceiling.
- →200W board power keeps it far from the PSU drama of the 24GB class — no special power planning, modest heat, quiet under sustained generation.
- →Price sanity in the 2026 shortage: launched at $599, and street prices across tiers are running ~50%+ over MSRP (our GPU-prices guide). If 4070 pricing creeps toward used-RTX-3090 money, think hard — the 3090’s 24GB unlocks the 27–32B class this card never will.
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Frequently asked questions
Can the RTX 4070 run a 32B model?
No — a 32B needs ~20GB at Q4 and still ~15GB at an aggressive Q3, both past 12GB. Ollama would offload most layers to system RAM and crawl. The honest ceiling is the 14B class, and it’s a good one: near-Copilot coding and strong reasoning with a ~55 tok/s bandwidth ceiling.
RTX 4070 desktop vs RTX 4070 Laptop for AI?
They are different tiers wearing the same name. The desktop card has 12GB and runs everything on this page; the laptop chip has 8GB on a narrower bus and is limited to the 7–9B class. If you’re buying a laptop for local AI, evaluate it as an 8GB machine — our 8GB VRAM guide is the honest list.
Is the RTX 4070 better than the RTX 3060 12GB for AI?
Same 12GB, same model list — the 4070 just carries a 40% higher bandwidth ceiling (504 vs 360 GB/s) and more compute for diffusion. Whether that speed is worth the price gap is the only question; capacity-wise the two cards are identical.
What image models run well on the RTX 4070?
This is the card where Flux stops needing tricks: Flux.1 dev FP8 (~11GB) runs at 1024px with near-FP16 quality (we have not timed it). SDXL runs with the full ControlNet + LoRA toolkit loaded. Only full FP16 Flux (~24GB) stays out of reach.
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