Local AI suitability
The RTX 4070 hits a sweet spot for local AI: 12 GB of VRAM with real bandwidth (504 GB/s). It holds the whole 7B–8B class at high-quality quantization, runs 12B–14B at 4-bit entirely in VRAM, and keeps long contexts practical.
One of the best value targets. 12 GB is enough for 14B at Q4 including KV cache at moderate context, and coding models like Qwen2.5 Coder 7B run at Q8 with room to spare. Reasoning models with long chains of thought benefit from the extra context headroom.
Recommended model sizes
- 7B (Q8)
- 8B (Q6/Q8)
- 12B (Q5)
- 14B (Q4/Q5)
Recommended quantizations
- Q5_K_M / Q6_K (GGUF)
- GPTQ Int4
- AWQ 4-bit
Limitations
- Not enough for 32B models except extreme quantization with offload
- Power draw requires a reasonable PSU (~650 W system)
How it compares
Versus the RTX 4060, the 4070's jump to 12 GB and 504 GB/s roughly doubles usable model size and substantially improves speed. Versus the 4070 Ti Super (16 GB), you trade some capacity for price.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
What is the largest model an RTX 4070 can run?
Comfortably 14B at 4-bit quantization with a moderate context. Larger models (32B+) need heavy quantization and CPU offload, which drops speed significantly.
Can it run DeepSeek-R1-Distill 14B?
Yes, at Q4 GGUF it fits in 12 GB with a modest context. Reasoning models generate many more tokens per answer, so throughput matters — expect the 4070 to feel responsive but not instant.
Is 12 GB VRAM enough for coding assistants?
Yes. Qwen2.5 Coder 7B at Q5/Q6 runs well with an 8K–16K context, which covers most coding workflows. Larger coder models need more VRAM.
4070 vs used 3090 (24 GB) for AI?
The 3090's 24 GB doubles capacity (32B at Q4 becomes possible) at the cost of power draw, heat and age. For pure local-AI value, a used 3090 is often the stronger buy; for a balanced modern system, the 4070 is cleaner.