NVIDIA GeForce RTX 4070 Ti
12GB Ada — fits 7B–14B Q4 with usable context.
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Sub-scores sum to 502 / 1000. Headline = 502 × 0.70 (Estimated-confidence discount) = 351. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 504 GB/s bandwidth — 60.5 tok/s estimated. No measured benchmarks yet.
Plain-English: Comfortable at 14B and below — snappy enough for a coding agent; vision models supported.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
What it does well
The RTX 4070 Ti is the entry into "real CUDA tensor compute" for cost-conscious local AI buyers — but the 12 GB VRAM ceiling is a hard constraint. 12 GB GDDR6X at 504 GB/s + Ada-generation tensor cores + the full CUDA stack at $799 MSRP / $550-700 used. For 7B–13B class models the card is genuinely strong: ~80–120 tok/s on Llama 3.1 8B, comfortable 14B Q5 with 32K context, smaller MoE models. Power draw at 285 W TDP is workstation-friendly. The card was the Ada-generation 12 GB sweet spot at launch, and used pricing has settled enough that it's a reasonable pick for buyers whose primary local AI workload is sub-14B and who don't need the RTX 4070 Ti Super's 16 GB.
Where it breaks
- 12 GB ceiling kills serious local AI. 14B FP16 doesn't fit (needs ~28 GB). 32B Q4 doesn't fit (needs ~16 GB). 70B Q4 is wildly out of reach. The card is firmly a "small model" tier. Reader who lands here Googling "is 12 GB enough for local AI" should be told the truth: only for 7B-13B-class. For anything serious, look at 16 GB+ (4070 Ti Super, 4080, 5070 Ti) or 24 GB+ (4090, 5090, used 3090).
- Pricing competition is brutal. RTX 4070 Ti Super at $799 has 33% more VRAM (16 GB) at the same MSRP. Used 4080 at $700 has 33% more VRAM at lower price. Both are dramatically better picks for AI.
- No 16 GB pathway in this exact SKU. 4070 Ti is firmly 12 GB. To get 16 GB Ada-gen you upgrade to 4070 Ti Super or 4080.
- Resale erosion under pressure from Blackwell. RTX 5070 Ti (16 GB) at $749 MSRP and RTX 5070 12 GB are squeezing 4070 Ti from both sides. Used 4070 Ti pricing should soften further over 12 months.
- Limited fine-tuning headroom. 12 GB barely fits 7B QLoRA with paged optimizer. Anything bigger needs more VRAM.
Ideal model range
- Sweet spot: 7B–13B FP16 / Q5 inference at ~80–120 tok/s decode with 32K context. Genuinely strong for this tier.
- Sweet spot: Smaller MoE inference (sub-14B parameters active) — fits 12 GB with reasonable speed.
- Sweet spot: Multi-model agentic loops fitting 12 GB total — 4B + embedding + small re-ranker.
- Stretch: 14B Q4 with 8K context (just fits 12 GB).
- Stretch: 7B QLoRA fine-tuning with paged optimizer.
- Bad fit: 32B-class anything, 70B-class anything, very long context on bigger models.
Bad use cases
- Anyone targeting 70B / 32B local AI. Hard 12 GB ceiling. Pick 16 GB+ minimum, ideally 24 GB+.
- Production multi-tenant serving. Consumer single-card pick, not production.
- Cost-conscious 16 GB seekers. RTX 4070 Ti Super at $799 wins (same price, 33% more VRAM). Don't buy 4070 Ti new at MSRP.
- Long-horizon investment as primary AI card. Used pricing should drop further; buy for use, not investment.
- Anyone considering used 3090 vs new 4070 Ti. Used 3090 at $700 has 24 GB at similar money — 2× the VRAM at minor compute / power tradeoffs. For pure AI usage, 3090 wins.
Verdict
Buy this if you find a used 4070 Ti at $500–$650, your local AI workload is firmly sub-14B (8B / 13B classes), you also game / do creator work where 4070 Ti matters more than just for AI, and you're not paying full MSRP. RTX 4070 Ti is the right pick for buyers who care about CUDA + decent compute + a small VRAM budget that fits their actual workloads.
Skip this if you want serious local AI (12 GB is below the practical floor for 14B+ models), RTX 4070 Ti Super is available at similar prices (16 GB wins decisively), you can find a used 3090 at $700 (24 GB at the same money — much better $/VRAM), you're going to also use the card for AI development long-term (pick the 16 GB tier for headroom), or you're paying full $799 MSRP (always pick 4070 Ti Super at the same money).
How it compares
- vs RTX 4070 Ti Super (16 GB) → Same $799 MSRP. 4070 Ti Super has 33% more VRAM, ~5% more compute, and the strict upgrade path. Don't pay the same money for less VRAM. Pick 4070 Ti Super if shopping new at MSRP. Pick 4070 Ti only at meaningful used discount. See /compare/rtx-4070-ti-vs-rtx-4070-ti-super.
- vs RTX 4080 (16 GB) → 4080 has 33% more VRAM + ~30% more compute at higher MSRP but used pricing is competitive. Pick 4080 used at $700–$800 over 4070 Ti at any price.
- vs RTX 5070 Ti (16 GB) → 5070 Ti is the Blackwell successor at $749 MSRP with 33% more VRAM + FP4 native + slightly more bandwidth. Same MSRP territory; pick 5070 Ti for new builds.
- vs used RTX 3090 (24 GB) → Used 3090 at $700 has 2× the VRAM at similar money. Slightly less compute and FP8 absent, but for 70B Q4 / 32B FP16 use cases it wins decisively because 4070 Ti can't fit those workloads at all. See /compare/rtx-4070-ti-vs-rtx-3090.
- vs RTX 4070 Super (12 GB) → Same VRAM tier (12 GB), 4070 Ti has ~15% more compute + bandwidth at $200 MSRP premium. Pick 4070 Super for value-conscious 12 GB; 4070 Ti when extra compute matters and budget allows.
Overview
12GB Ada — fits 7B–14B Q4 with usable context.
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Specs
| VRAM | 12 GB |
| Power draw (peak) | 285 W |
| Released | 2023 |
| MSRP | $799 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 4070 Ti with usable context.
Frequently asked
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Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.