Which is better for local AI in 2026 — NVIDIA GeForce RTX 4070 Ti Super or NVIDIA GeForce RTX 3090?

For most local AI buyers, the NVIDIA GeForce RTX 3090 wins on the dimension that matters most: VRAM. 24 GB unlocks workloads the NVIDIA GeForce RTX 4070 Ti Super's 16 GB ceiling can't reach.

Can I run 70B Q4 models on these cards?

Yes — 24 GB+ VRAM fits 70B Q4 at usable context (4-8K). 32 GB unlocks 32K+ context.

Should I buy used or new at this tier?

Used wins decisively at the 24 GB tier where used cards (3090, 4090) deliver the same VRAM ceiling at half the cost. Verify ECC error counts, replace thermal pads, demand a 30-min under-load demonstration before paying. New wins when warranty matters psychologically or you specifically need newer architecture features.

What about power, noise, and heat under sustained AI load?

Sustained inference draws closer to nameplate TDP than gaming benchmarks suggest. Plan PSU sizing with 200-250W headroom over GPU TDP. Improving case airflow helps the GPU more than swapping the CPU cooler.

How long will these cards stay relevant for local AI?

24 GB consumer GPUs (3090, 4090) stay inference-relevant 4-6 years. Apple Silicon stays relevant ~5 years before macOS / framework drift. Don't buy for "future-proofing" — buy for what you'll run this year. Use /will-it-run to verify your specific model + hardware combination.

How is the custom comparison different from your editorial verdicts?

The custom comparison is generated from real catalog data (VRAM, bandwidth, compute, power, runtime support). The 13+ editorial pair pages are hand-written buyer guides with decision rules, avoid-each lists, and qualitative tier scoring. Use editorial when we have one for your pair; use custom for everything else.

Why don't all comparisons have an editorial verdict?

We hand-write editorial verdicts only for the highest-search-volume pairs (RTX 4090 vs 5090, dual 3090 vs 5090, etc.). Writing a quality verdict takes hours. The custom tool covers the long tail without inventing fake editorial.

Are the prices real-time?

No. Prices in the catalog are updated periodically by editorial. Click through to a retailer for the live price.

Can I compare laptops?

Yes — the catalog includes mobile GPUs and laptop SoCs. Pick any two entries from the dropdown.

Custom comparisonEditorialReviewed May 2026

NVIDIA GeForce RTX 4070 Ti Super vs NVIDIA GeForce RTX 3090

Spec-driven comparison from our catalog. For curated editorial verdicts on the most-asked pairs, see the head-to-head index.

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Spec matrix

Dimension	NVIDIA GeForce RTX 4070 Ti Super	NVIDIA GeForce RTX 3090
VRAM	16 GB mid (13B-32B Q4; 70B Q4 short ctx)	24 GB high (70B Q4 comfortable)
Memory bandwidth	— —	— —
FP16 compute	—	—
FP8 compute	—	—
Power draw	285 W enthusiast (850W PSU)	350 W enthusiast (850W PSU)
Price	~$829 (street)	~$899 (street)
Release year	2024	2020
Vendor	nvidia	nvidia
Runtime support	CUDA, Vulkan	CUDA, Vulkan

Spec data from our hardware catalog. This is a generated spec compare, not a hand-written editorial verdict. For editorial picks on the most-asked pairs, see our curated head-to-heads.

Most users should buy

Primary recommendation

NVIDIA GeForce RTX 3090

24 GB usable VRAM unlocks high (70B Q4 comfortable) workloads that the NVIDIA GeForce RTX 4070 Ti Super's 16 GB ceiling can't reach. For most local AI buyers in 2026, VRAM ceiling is the dimension that matters most.

Decision rules

Choose NVIDIA GeForce RTX 4070 Ti Super if

You hate used silicon and want a warranty. The NVIDIA GeForce RTX 4070 Ti Super is the new-with-warranty alternative.

Choose NVIDIA GeForce RTX 3090 if

You target high (70B Q4 comfortable) workloads — 24 GB is the working ceiling for that.
You're comfortable with used silicon and prioritize $/GB-VRAM.

Biggest buyer mistake on this comparison

Picking the NVIDIA GeForce RTX 4070 Ti Super for the warranty when the NVIDIA GeForce RTX 3090 (used) gives you 8 GB more VRAM at lower cost. At the 24 GB tier, used silicon's $/GB-VRAM advantage is decisive — verify ECC error counts before buying, but don't dismiss used out of hand.

Workload fit

How each card handles common local AI workloads. “Tie” means both cards meet the bar; pick on other axes (price, ecosystem, form factor).

Workload	Winner	Notes
Coding agents (Aider, Cursor, Continue)	Tie	Code agents work fine on 16 GB for 13-32B models. 24 GB unlocks 70B-class code models (DeepSeek Coder V3, Qwen 2.5 Coder).
Ollama / LM Studio chat	Tie	Both run Ollama fine. 16 GB unlocks multi-model serving via OLLAMA_KEEP_ALIVE.
Image generation (SDXL, Flux Dev)	Tie	Image gen needs 16 GB minimum for Flux Dev FP8; 24 GB for FP16 + LoRA training.
Local RAG (embedding + LLM)	Tie	RAG with 70B LLM concurrent fits at 24 GB. Embedding model overhead is negligible (<1 GB).
Long-context chat (32K+ context)	NVIDIA GeForce RTX 3090	24 GB fits 70B Q4 at 8-16K context. KV cache quantization (Q8 cache) extends to 32K with care.
Voice / Whisper transcription	Tie	Whisper Large V3 fits in 4-8 GB. Both cards likely overkill for transcription-only workloads.
Video generation (LTX-Video, Mochi)	NVIDIA GeForce RTX 3090	Local video gen viable at 24 GB. Plan for short clips, not long-form.
Multi-GPU tensor parallel (vLLM, ExLlamaV2)	NVIDIA GeForce RTX 3090	Tensor-parallel scaling works on PCIe 4.0 x8/x16. Used cards typically win on $/GB-VRAM at scale (dual 3090 vs single 5090).

VRAM reality check

Multi-GPU does NOT pool VRAM by default. Two 24 GB cards = 48 GB combined ONLY when the runtime supports tensor-parallel inference (vLLM, ExLlamaV2, llama.cpp split-mode). For models that don't tensor-parallel cleanly, you're stuck at single-card VRAM.
At 24 GB, 70B Q4 fits with 4-8K context comfortably. FP16 32B fits. 32K+ context on 70B Q4 starts to get tight — KV cache quantization (Q8 cache) extends this another ~30%.

Power, noise, and thermals

NVIDIA GeForce RTX 4070 Ti Super TDP: 285W. NVIDIA GeForce RTX 3090 TDP: 350W. Both fit standard ATX builds with 750-850W PSUs.
Used cards: replace thermal pads on any used purchase older than 18 months ($30-50 + 1 hour of work). Ex-mining cards specifically — cooler reseat improves thermals 5-10°C, often the difference between throttling and stable load.

Used-market intelligence

Mining-rig provenance is dominant for used NVIDIA GeForce RTX 3090 listings. Not inherently disqualifying — mining wears fans (replaceable) and thermal pads (replaceable), rarely silicon. Verify ECC error counts with nvidia-smi (or vendor equivalent); any value above ~100 = walk away.
Demand a 30-minute under-load demonstration before paying — screen-recorded inference at 90%+ utilization. Sellers refusing this are red flags.
Replace thermal pads on any used GPU older than 18 months. Cheap insurance ($30-50 + 1 hour) that often delivers 5-10°C cooler operation under sustained inference.
Used cards have no warranty. Budget for a 2-3 year operational horizon and plan to resell if your usage tier changes. Used silicon resale is mature in 2026 — selling later is realistic.

Upgrade-path logic

Don't downgrade VRAM for newer silicon. The NVIDIA GeForce RTX 4070 Ti Super is more recent but ships with 16 GB vs the NVIDIA GeForce RTX 3090's 24 GB. For VRAM-bound local AI workloads, newer-with-less-VRAM is a regression.
NVIDIA GeForce RTX 4070 Ti Super → NVIDIA GeForce RTX 3090 is a real VRAM-tier upgrade (16 GB → 24 GB). Worth it if you're outgrowing the lower-tier ceiling on 70B-class workloads.

Better alternatives to consider

Same VRAM cheaper

RTX 3090 (used) — cheapest 24 GB →

If 24 GB is your target tier, the used 3090 at $700-1,000 is the cheapest path. Both cards in your comparison cost more for the same VRAM ceiling.

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