GPUFits
GPU Comparisons / RTX 3060 12GB vs RTX 4070 Ti Super
RTX 3060 12GB vs RTX 4070 Ti Super for Local LLMs: Which Should You Pick
Under $300 targeting the 8B tier: RTX 3060 12GB. Need Phi-4 14B / gpt-oss-20b: pay up for the 4070 Ti Super. For 24B+, neither card works.
Spec Comparison
| RTX 3060 12GB | RTX 4070 Ti Super | |
|---|---|---|
| Nominal VRAM | 12 GB | 16 GB |
| Usable VRAM (for models) | 12.0 GB | 16.0 GB |
| Memory bandwidth | 360 GB/s | 672 GB/s |
| Type | Discrete GPU | Discrete GPU |
| Release year | 2021 | 2024 |
| MSRP | $329 | $799 |
| Used reference price | $260 | $760 |
| TDP | 170 W | 285 W |
Model Verdict Matrix (Q4_K_M @8K)
| Model | VRAM needed | RTX 3060 12GB | Theoretical speed | RTX 4070 Ti Super | Theoretical speed |
|---|---|---|---|---|---|
| Llama 3.2 3B | 4.4 GB | Comfortable | ≈137 | Comfortable | ≈256 |
| Llama 3.1 8B | 7.5 GB | Comfortable | ≈55 | Comfortable | ≈102 |
| Qwen3 8B | 7.7 GB | Comfortable | ≈54 | Comfortable | ≈100 |
| Phi-4 14B | 12.2 GB | Needs multi-GPU | — | Comfortable | ≈56 |
| Mistral Small 3.2 24B | 17.5 GB | Not feasible | — | Needs multi-GPU | — |
| Gemma 3 27B | 22.4 GB | Not feasible | — | Not feasible | — |
| Qwen3.8 27B | 20.7 GB | Not feasible | — | Not feasible | — |
| Muse Glimmer 30B | 20.1 GB | Not feasible | — | Not feasible | — |
| Qwen3 30B-A3B | 21.0 GB | Not feasible | — | Not feasible | — |
| Qwen3 32B | 23.7 GB | Not feasible | — | Not feasible | — |
| gpt-oss-20b | 14.7 GB | Not feasible | — | Tight fit | ≈229 |
| Llama 3.3 70B | 47.4 GB | Not feasible | — | Not feasible | — |
| gpt-oss-120b | 73.6 GB | Not feasible | — | Not feasible | — |
| DeepSeek-R1 671B | 413.1 GB | Not feasible | — | Not feasible | — |
Highlighted rows = watershed models where the two cards' verdicts differ. Theoretical speed = bandwidth × 0.75 ÷ per-token weight bytes (active params for MoE); real-world results vary with framework/driver/CPU, ±30%. Speed is hidden for multi-GPU/infeasible verdicts.
Value Comparison
| RTX 3060 12GB | RTX 4070 Ti Super | |
|---|---|---|
| Bandwidth per dollar | 1.38 GB/s/$ | 0.88 GB/s/$ |
| Usable VRAM per dollar | 0.046 GB/$ | 0.021 GB/$ |
Pricing basis: RTX 3060 12GB = used reference price; RTX 4070 Ti Super = used reference price; used prices are market estimates, see analysis for volatility.
Analysis
This is the classic entry-vs-midrange fork: a used 3060 12GB runs $250-275 (up ~40% since July 2026 — the DRAM price surge hit entry cards too), a used 4070 Ti Super $720-800, nearly a 3× spread. The capability difference is clear: 12GB runs the 8B tier comfortably, while Phi-4 14B (Q4 ~12.2GB) already needs Q3 or multi-GPU and gpt-oss-20b is infeasible; 16GB makes Phi-4 comfortable, gpt-oss-20b a tight fit, and lets the 8B tier run at Q8_0 for higher quality.
The value math is interesting: the 3060 posts 1.38 GB/s/$ and 0.046 GB/$ — the highest on this site, every dollar working; the 4070 Ti Super's 0.88 / 0.021 is still healthy. Bandwidth is 360 vs 672 GB/s, so 8B Q4 runs theoretically ~55 vs ~102 tok/s — nearly double the chat fluency, a difference you feel every day.
Both cards share the same ceiling: the 24B tier is out of reach for either. So the logic is: first local-LLM card, targeting 8B chat and light tasks — the 3060 12GB is the cheapest way to make mistakes, 170W needs no PSU upgrade, and mining risk is lower than high-end 30-series; clearly need the 14-20B tier or higher-quality 8B quants — go straight to the 4070 Ti Super, a 2024 mining-free card with lower power. If you secretly want 24-32B, skip both and save toward a ~$1,100 used 3090.
FAQ
- Can the RTX 3060 12GB run 14B models?
- Phi-4 14B Q4 needs ~12.2GB, over the 12GB nominal — you'd drop to Q3_K_M or multi-GPU, not recommended. Its comfort zone is the 8B tier and below (Q8_0 fits).
- How much faster is the 4070 Ti Super than the 3060?
- 672 vs 360 GB/s — theoretically +87% decode: 8B Q4 ~102 vs ~55 tok/s. Add the 16 vs 12GB capacity gap and it's nearly a full tier of experience apart.
- Which card is better value?
- On paper the 3060: 1.38 vs 0.88 GB/s per dollar. But value only counts within your target tier — for 14-20B, the 3060 can't fit gpt-oss-20b at any price; the 4070 Ti Super is the answer.
Related guides
- Run GGUF Models Locally: llama.cpp and Ollama Walkthrough
- Quantization Explained: Q4 vs Q8 and What You Actually Lose
- Best GPU for Local LLMs in 2026: Every Budget Tier
- Read the full RTX 3060 12GB breakdown →
- Read the full RTX 4070 Ti Super breakdown →
- Try RTX 3060 12GB in the GPU compatibility checker →
- Try RTX 4070 Ti Super in the GPU compatibility checker →
Data verified 2026-09-01