RTX 5070 Ti for Local AI: What Fits in 16GB vs 4090

Short answer: the RTX 5070 Ti's 16GB of GDDR7 runs every 14B-class model at full Q4 quality with room for a long context — Qwen 3 14B, Phi-4 14B, Qwen 2.5 Coder 14B — which covers most local AI work. What it cannot do is the 32B tier: Qwen 2.5 Coder 32B needs roughly 20GB, so on 16GB it spills into system RAM and collapses. That one line is the entire RTX 5070 Ti versus RTX 4090 decision.

Published April 23, 2026 · Updated August 2026 · 14 min read · LocalAimaster Research Team

What changed in this update: the page now leads with the 16GB model-fit question rather than the gaming-style head-to-head, the VRAM and bandwidth figures are shown as arithmetic you can check, unsourced street prices and speed claims have been removed, and the FAQ moved onto the page itself.

Most "5070 Ti vs 4090" coverage is written for gamers and answers in frames per second. If you are buying to run local models, the frame-rate question is irrelevant — the only number that decides your experience is how many gigabytes of weights the card can hold. This page answers that one.

What can an RTX 5070 Ti actually run?

The RTX 5070 Ti holds 16GB, which lands it squarely in the 14B tier: any 14B model at Q4_K_M loads with 6–7GB spare for context. The 32B tier needs about 20GB and does not fit. These are the picks that make sense on a 16GB card:

Where those numbers come from. The VRAM column is arithmetic (below). The speed column is the published figures from our best LLM for 16GB VRAM ranking and best coding LLM for 16GB VRAM ranking, recorded on an RTX 4080 — the reference card for that tier. They are not RTX 5070 Ti measurements and are not presented as such. They are a conservative floor for the 5070 Ti, for a reason you can compute yourself in the next section.

The VRAM arithmetic, so you can check it

A Q4_K_M weight averages roughly 4.5 bits, so the weights alone come to:

VRAM (weights) ≈ parameters × 4.5 ÷ 8 = parameters × 0.5625 bytes

• 14B → 14 × 0.5625 ≈ 7.9GB of weights, about 9GB loaded once you add the KV cache and runtime overhead.
• 32B → 32 × 0.5625 ≈ 18GB of weights, about 20GB loaded.
• 70B → 70 × 0.5625 ≈ 39GB of weights, about 40GB loaded.

That is the whole story of the 16GB ceiling: 14B fits with headroom, 32B misses by roughly 4GB, and 70B is not a single-consumer-card model on either side of this comparison. The full ladder for every size and quantization is in our VRAM requirements reference, and you can pull any of these with the tags listed in the official Ollama model library.

How fast is the RTX 5070 Ti for LLM inference?

Token generation on a fully-resident model is memory-bandwidth-bound: to emit one token the runtime reads every weight once. So the hard ceiling is:

max tokens/sec = memory bandwidth ÷ model size in memory

Bandwidth itself is arithmetic from the published memory spec — data rate × bus width ÷ 8:

Two things fall straight out of that table. First, the 5070 Ti has about 25% more memory bandwidth than the RTX 4080 (896 ÷ 717 = 1.25), which is why the RTX 4080 speeds in the model-fit table are a floor rather than a target for this card. Second, the 4090's advantage on models that fit both cards is only about 12.5% (1,008 ÷ 896) — small enough that for anything at or under 14B, the two cards are in the same performance class. Specs are on NVIDIA's RTX 50-series comparison page.

Applying the roofline to the 5070 Ti: 896 GB/s ÷ 9GB is about 100 tok/s for a 14B at Q4_K_M, and 896 ÷ 4.7 is about 190 tok/s for an 8B. Real throughput always lands under the roofline — attention, sampling and KV-cache traffic all cost time — so treat these as the physical ceiling, not a prediction. What the roofline is genuinely good for is ratios: it tells you a 14B will run roughly half the speed of an 8B on the same card, and that is reliable.

The cliff past 16GB

The moment a model does not fit, Ollama and llama.cpp keep the overflow layers in system RAM and stream them across PCIe. A PCIe 5.0 x16 link tops out around 63 GB/s — roughly one fourteenth of the 5070 Ti's 896 GB/s. The offloaded fraction, not the GPU, then sets the pace. This is not a gentle taper as models get bigger; it is a cliff at the 16GB line.

That makes the useful comparison a fit table, not a frame-rate chart:

RTX 4090 speeds in that last column are the published figures from our best LLM for 24GB VRAM ranking and best coding LLM for 24GB VRAM ranking.

Where does the RTX 4090's extra 8GB actually pay?

24GB is not "the same thing but roomier" — it is a capability tier. It unlocks the full 32B class at proper Q4 quality, and it lets a 14B run at Q8 for near-lossless fidelity. On 16GB neither of those is available at any speed.

If you write code for a living, that first row is the entire argument. Qwen 2.5 Coder 32B is the best coding model you can run on hardware you own, and it needs 24GB. Nothing the 5070 Ti does compensates for not being able to load it.

RTX 5070 Ti vs RTX 4090: the spec sheet

The 4090 has 1.83x the CUDA cores and 1.5x the VRAM. The 5070 Ti has newer tensor hardware, two thirds of the power draw, and it is a card you can still buy new. Both traded well above these MSRPs through the 2026 memory shortage — check the current market before you budget, because the gap between them has moved a lot this year. Our GPU prices and the memory shortage breakdown tracks where things actually sit.

Is 16GB enough for Flux.1, SDXL and local video?

For image generation, 16GB is comfortable; for video, it is the constraint. Diffusion models are sized the same way LLMs are, so the same fit logic applies:

Two corrections worth knowing before you shop on this basis. Flux.1 pro is not a local option at all — Black Forest Labs ships it as an API product; the open-weight releases are Flux.1 dev and Flux.1 schnell, and dev is what people mean when they say they run Flux locally. And "16GB runs Flux" is true specifically of the quantized builds, not the FP16 release. Full picture in our local AI video generation guide.

Which one should you buy?

• Buy the RTX 5070 Ti if your largest planned model is 14B, you want a warranty and a new card, your PSU is 750W-class, your case is small, or you also game. On every workload that fits in 16GB it is in the same performance class as a 4090 and draws 150W less.
• Buy a used RTX 4090 if you run 32B models routinely — especially Qwen 2.5 Coder 32B — train LoRAs, run Flux.1 dev at FP16, or work with local video models. The extra 8GB buys capability, not just speed.
• Buy neither if your budget is below the 5070 Ti (a 12GB card covers the 7B–13B range fine), or if you can reach a 32GB card. Check what the tier above actually costs first — see the current GPU price breakdown before assuming it is reachable.

One question settles it for most people: have you ever opened a model card for something larger than 14B and wished you could run it? If yes, you want 24GB. If you had to think about it, 16GB is enough.

Worth knowing that the 4090 is not the only route to 24GB — a used RTX 3090 holds the same 24GB and is usually the cheaper path to this tier, at lower throughput. Our 4090 vs 3090 comparison covers that trade properly.

What does each card cost to run over three years?

Purchase price is the number that decides this, and it has moved all year, so plug in your own. Electricity, though, is fully computable. At 4 hours a day of sustained inference (1,460 hours a year) and $0.16/kWh, using each card's rated TDP as an upper bound:

• RTX 5070 Ti, 300W TDP: 1,460 × 0.300 = 438 kWh/yr × $0.16 = $70/yr, about $210 over three years.
• RTX 4090, 450W TDP: 1,460 × 0.450 = 657 kWh/yr × $0.16 = $105/yr, about $315 over three years.

Real inference draw sits below TDP, so both figures are conservative — but the shape holds: the 4090 costs roughly $35 a year more to run at these hours. That is noise next to the purchase gap, and anyone justifying a 5070 Ti purchase on electricity savings is arguing the wrong point. Substitute your own tariff and hours; the formula is kWh = hours × kW, then × your rate.

The 4090 also has a second cost the 5070 Ti does not: it wants an 850W PSU minimum where the 5070 Ti runs on 750W. If your current supply is 750W, add a PSU to the 4090 column before you compare.

For the comparison that usually matters more — running your own hardware against paying per token — see Ollama vs ChatGPT API cost at scale.

How do you check a used RTX 4090 before you buy?

The 4090 is out of production, so the only way to get one is second hand. A checklist that covers the failure modes people actually report:

1. Prefer sellers who can explain why they are selling. "Upgraded to a 5090" is a normal answer. Vagueness about the card's history is the single most common warning sign.
2. Insist on a return window. eBay's buyer protection or PayPal Goods & Services on a marketplace sale. A private cash handover with no recourse is not worth the discount.
3. Stress the card on day one. Run a sustained GPU load for 30 minutes and watch the hotspot temperature, not just the core. A hotspot pushing past 100°C under air points at dried-out thermal paste or a pad problem.
4. Then run a real AI workload. Pull an 8B model in Ollama and run a few hundred completions back to back. Throughput that degrades over the run, or CUDA errors, means the card is not thermally or electrically stable under the load you bought it for.
5. Inspect it physically. Chipped fan blades mean it was dropped. Bent fins mean bad packing. Check the 16-pin connector for discolouration — the 2023 connector-melt issue was rare but it leaves visible evidence.
6. Measure your case first. The Founders Edition is 3 slots and 304mm; AIB models run considerably longer and thicker. Check clearance before money changes hands, not after.
7. Confirm PSU headroom. 850W minimum. Transient spikes on this card are what trip undersized supplies, and a system that reboots under load looks exactly like a dead GPU.

Used prices move with the wider market, so establish the current going rate before you judge any listing — a price far below the going rate deserves scepticism rather than excitement.

What goes wrong on a 16GB card?

• Underestimating the ceiling. The 5070 Ti is excellent right up to the moment you try a 32B and it falls off the cliff. Map your real workload to VRAM using the VRAM requirements reference before buying, not after.
• Planning to live at Q3. Yes, a 32B squeezes into 16GB at Q3_K_S with a short context. It is slower and lower fidelity than a 14B at Q4, and it is not a reason to buy this card.
• Forgetting the KV cache. Model weights are not the whole footprint. A long context can add several gigabytes, which is exactly the margin that decides whether a 13GB model actually loads.
• Stale CUDA toolkit. GeForce RTX 50-series parts need CUDA 12.8 or newer for their compute capability; training repos that pin an older toolkit will not build. NVIDIA lists compute capability per card on its CUDA GPUs page.
• A driver that predates Blackwell. Use the driver branch NVIDIA shipped with the RTX 50-series or newer. Anything older will not enumerate the card properly, and the symptoms look like a hardware fault.
• Assuming FP8 is automatic. Blackwell's FP8 path needs a framework built for it — a PyTorch or vLLM build compiled against CUDA 12.8+. GGUF Q4 in Ollama is integer quantization and does not touch the FP8 units at all, so most Ollama users will never see that hardware work. If you serve an OpenAI-compatible endpoint, our private API guide covers the vLLM side.
• PSU mismatch on a 4090. Covered above, but it is the single most expensive surprise in this comparison: discovering your 750W supply sags under a 4090's transients after the card is already bought.

RTX 5070 Ti for AI: your questions

Is the RTX 5070 Ti good for AI?

Yes, within its tier. 16GB runs any 14B model at full Q4 quality with a long context, which covers local chat, RAG, and a genuinely useful local coding assistant. It is a poor choice if your plans include 32B models or FP16 Flux, because no amount of speed compensates for a model that will not load.

Is the RTX 5070 Ti faster than the RTX 4090 for AI?

For models that fit in 16GB, they are in the same class — the 4090 has about 12.5% more memory bandwidth (1,008 vs 896 GB/s), and inference on a resident model is bandwidth-bound, so that is roughly the gap you should expect. For models above 16GB the question does not apply: the 5070 Ti is offloading to system RAM over a link roughly one fourteenth as fast as its own VRAM.

Can the RTX 5070 Ti run a 70B model?

Not usefully. A 70B at Q4_K_M is about 40GB of weights against 16GB of VRAM, so the great majority of the model would stream from system RAM every single token. A 70B needs 48GB — two 24GB cards — regardless of which of these two you buy.

What is the best LLM for a 16GB GPU?

Qwen 3 14B at Q4_K_M is the general-purpose pick (~9GB, leaving room for a large context), Qwen 2.5 Coder 14B is the coding pick, and Gemma 3 12B is the best for multilingual and writing. The full ranked list with pull commands is in our best LLM for 16GB VRAM guide.

How does GDDR7 on the 5070 Ti compare to GDDR6X on the 4090?

GDDR7 on the 5070 Ti runs at 28 Gbps against 21 Gbps for the 4090's GDDR6X, but the 4090 has a 384-bit bus to the 5070 Ti's 256-bit. Multiply out and the 4090 still wins on total bandwidth: 1,008 GB/s vs 896 GB/s. Per-pin speed is not the number that matters — bus width × data rate is.

Does the RTX 5070 Ti work with the same AI tools as the 4090?

Yes. Ollama, llama.cpp, vLLM, ComfyUI and Continue.dev all run on Blackwell. The requirements are a driver from the RTX 50-series branch or newer and, if you compile anything yourself, CUDA 12.8+. Frameworks pinned to older toolkits are the usual source of trouble, not the card.

Should I buy a 5070 Ti now or wait?

If a model you need does not fit in your current VRAM, waiting has a real daily cost. If your workload fits comfortably in 16GB today, there is less urgency — and the 24GB mid-tier refresh rumours are covered in our GPU price and shortage breakdown, which is the honest place to weigh that up.

The bottom line

The RTX 5070 Ti is a good local AI card with a hard, knowable edge. It runs the 14B tier at full quality, it has the memory bandwidth to do so at least as fast as the 16GB cards that came before it, and it does that on 300W with a warranty. If your model list stops at 14B, it is the sensible buy and you will not feel constrained.

The 4090 exists in this comparison for one reason: 24GB opens the 32B tier, and the 32B tier is a real step up in capability rather than a speed bump. If Qwen 2.5 Coder 32B is on your list, no amount of Blackwell architecture on a 16GB card gets you there.

Write down the largest model you actually intend to run, multiply its parameter count by 0.5625, and compare that to 16. The answer is arithmetic, not architecture.

For the rest of the lineup, see our best GPUs for AI buying guide, or the RTX 5090 vs 4090 comparison if your budget reaches the 32GB tier.