Best AI Models for the RTX 5070 Ti
The 14B class with context to spare, a 12B at Q8, gpt-oss:20b and the 24B coder as a tight fit — plus the 12GB laptop trap. Footprints from Ollama’s tag sizes, speeds as bandwidth ceilings.
The best AI model for the RTX 5070 Ti is Qwen 3 14B — 9.3GB at Q4_K_M as Ollama lists it, which leaves ~6–7GB of the 16GB for context, with a bandwidth ceiling of ~96 tok/s (896 GB/s ÷ 9.3GB — a ceiling, not a measurement). The family rule: weights plus KV cache must fit inside VRAM, so on 16GB the comfortable class is 12–14B at Q4 with a 12B at Q8 (Gemma 4 12B, 13GB) as the quality lane, gpt-oss:20b (14GB, MoE) as the fast general assistant, and Devstral Small 2 24B (15GB) as the strongest coder that fits — barely. The honest ceiling is the 27B class: Qwen3.6 27B is 17GB at Q4 and misses this card by about a gigabyte, which is the whole argument for a 24GB card. We do not own a 5070 Ti; nothing here is a measured rate.
Models that run well on the RTX 5070 Ti
| Model | Size | Build | Memory used | Ceiling tok/s* |
|---|---|---|---|---|
| WINNERQwen 3 14B The 16GB winner: the whole 14B class with ~6–7GB of KV budget, which is the difference between single-file edits and a 30K-token session. The q8_0 tag is 16GB — the entire card — so stay at Q4 here. ollama pull qwen3:14b | 14B (dense) | Q4_K_M | ~9.3GB | ceiling ~96 tok/s (896 ÷ 9.3GB) |
| Gemma 4 12B The near-lossless lane a 12GB card cannot run: Google’s dense 12B at Q8 with text and image input, ~3GB spare. The q4_K_M build is 8.0GB if you need the context back. ollama pull gemma4:12b-it-q8_0 | 12B (dense) | Q8_0 | ~13GB | ceiling ~69 tok/s (896 ÷ 13GB) |
| Qwen 2.5 Coder 14B The context-heavy coding pick — ~7GB of KV room for feeding an agent whole modules. Devstral scores higher but leaves no room; this one is the daily driver for repo work. ollama pull qwen2.5-coder:14b | 14B (dense) | Q4_K_M | ~9.0GB | ceiling ~100 tok/s (896 ÷ 9.0GB) |
| gpt-oss:20b OpenAI’s open-weight model sized for exactly this tier. Fast chat and tool use; the trade is it fills most of the card, so it runs alone with a modest context. ollama pull gpt-oss:20b | 20.9B (MoE, ~3.6B active) | MXFP4 | ~14GB | MoE — ~3.6B active per token; bandwidth is not the limit; not measured |
| Devstral Small 2 24B The strongest agentic coder that fits 16GB, with about a gigabyte left for the KV cache. Set num_ctx deliberately and give the card nothing else; a 32K agent loop will spill to system RAM. ollama pull devstral-small-2:24b | 24B (dense) | Q4_K_M | ~15GB | ceiling ~60 tok/s (896 ÷ 15GB) |
| Flux.1 dev FP8 Blackwell has native FP8 and FP4 tensor cores, so this is the tier where the FP8 build is both the fit and the fast path. Room for two or three LoRAs; the FP16 reference build (~24GB) stays a 24GB-card job. ComfyUI: flux1-dev-fp8.safetensors | 12B | FP8 | ~11GB (~13GB with LoRAs) | diffusion — compute-bound, not timed |
* Ceiling tok/s = the device’s vendor-listed memory bandwidth ÷ the model’s file size as Ollama lists it — the rate a dense model cannot exceed, because every generated token reads the whole weight set once. We have not measured this hardware. Real-world generation usually lands at 50–80% of the ceiling and falls as the context grows; mixture-of-experts rows read only their active parameters, so no ceiling is quoted for them, and image models are compute-bound, so no render time is.
What won't fit on the RTX 5070 Ti
- ✗Qwen3.6 27B (needs 17GB at Q4_K_M) — Misses by about a gigabyte before a single token of context. The best open model in the 27B class is the near-miss that sells 24GB cards.
- ✗Qwen 3 14B at Q8_0 (needs 16GB) — The tag is exactly the size of the card, so it loads with nothing left for the KV cache. The Q8 lane on 16GB is a 12B (Gemma 4 12B, 13GB), not a 14B.
- ✗Qwen 2.5 Coder 32B (needs 20GB) — The strongest dense open coder, and a 24GB pull. On 16GB, Devstral Small 2 is the ceiling.
- ✗Llama 3.3 70B (needs 43GB at Q4_K_M, 26GB even at Q2_K) — A 70B is a 48GB pair at Q4 or a 32GB card at Q2. Nothing a 16GB card can hold.
RTX 5070 Ti-specific notes
- →Where this page’s numbers come from: we do not own a 5070 Ti. Footprints are the file sizes Ollama prints on each tag page (read 2 Nov 2026), and every tok/s is a ceiling — NVIDIA’s listed 896 GB/s divided by the gigabytes a dense model reads per token. No engine reaches its ceiling; read each figure as the speed a model cannot exceed on this card. We quote no measured rate.
- →The laptop trap is bigger on this card than on the 5080: NVIDIA’s laptop comparison page lists the RTX 5070 Ti Laptop GPU at 12 GB GDDR7 on a 192-bit bus with 5,888 CUDA cores and a 60–115 W power range, against the desktop card’s 16 GB, 256-bit and 8,960 cores. A “5070 Ti” laptop is a 12GB machine: the gpt-oss:20b (14GB), Devstral (15GB) and Gemma 4 12B at Q8 (13GB) rows on this page do not fit it, and the 14B class at Q4 fits with ~3GB spare. Check the memory before you check the badge.
- →Same 16GB as the RTX 5080, same model list. NVIDIA’s comparison table lists the 5070 Ti at 8,960 CUDA cores, 896 GB/s and 300 W against the 5080’s 10,752 cores, 960 GB/s and 360 W. Nothing that fits one card fails on the other; the 5080 has a ~7% higher decode ceiling and more compute, and the only question is whether that is worth its price gap.
- →Power, from NVIDIA’s spec page: 300 W total graphics power, a 750 W required system power supply, and either 2× PCIe 8-pin through the adapter in the box or a single 300 W or greater PCIe Gen 5 cable. That is the easiest install in the 50-series above the 5070. Prefer a native cable to an adapter chain and seat it fully; LLM decoding is a sustained load measured in minutes.
- →The Blackwell software trap: every RTX 50-series GPU reports compute capability 12.0 (sm_120), and PyTorch wheels built for CUDA 12.6 or older contain no kernels for it. Any project that pins an old torch fails with “no kernel image is available for execution on this device”. Ollama bundles its own CUDA runtime and is largely unaffected; ComfyUI and anything with compiled extensions (xformers, flash-attn, bitsandbytes) are where it bites. Our fix guide is linked below.
- →Price sanity on 2 Nov 2026: NVIDIA announced the 5070 Ti “starting at $749”, and videocardprices.com’s lowest in-stock new price was $1,319 at Amazon as of 26 Oct 2026 — about 75% over list. At that money, check what a used 24GB card costs the same day (our RTX 3090 page carries a dated band): if the models you keep wanting are Qwen3.6 27B and the 32B coders, no 16GB card at any price gets you there.
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Frequently asked questions
What is the best AI model for an RTX 5070 Ti?
Qwen 3 14B at Q4_K_M — 9.3GB as Ollama lists it, ~6–7GB of the 16GB left for context, and a bandwidth ceiling of ~96 tok/s (896 ÷ 9.3GB). For a near-lossless build, Gemma 4 12B at Q8 (13GB). For agentic coding, Devstral Small 2 24B (15GB) is the strongest that fits, but Qwen 2.5 Coder 14B (9.0GB) is the better daily driver because it leaves room for a real context. For images, Flux.1 dev FP8. All footprints are tag sizes and all speeds are ceilings.
RTX 5070 Ti vs RTX 4090 for local AI?
The 4090’s 24GB runs the 27–32B class at full Q4 — Qwen3.6 27B (17GB), Qwen 2.5 Coder 32B and DeepSeek-R1 32B (20GB each). The 5070 Ti’s 16GB runs none of them; it tops out at gpt-oss:20b and the 14B class. On decode bandwidth the two are closer than the generation gap suggests (896 vs 1,008 GB/s per NVIDIA), so the newer card is not the faster one for token generation. If the 27B+ class is the point, the 4090 wins on capacity alone; our 5070 Ti vs 4090 comparison goes through price and power.
Is the RTX 5070 Ti Laptop the same as the desktop RTX 5070 Ti?
No — and this is the one to check. NVIDIA lists the RTX 5070 Ti Laptop GPU at 12 GB GDDR7 on a 192-bit bus with 5,888 CUDA cores, against the desktop’s 16 GB, 256-bit and 8,960 cores. A 5070 Ti laptop is a 12GB machine: the 14B class at Q4 fits with ~3GB spare, but gpt-oss:20b, Devstral Small 2 and the Q8 rows on this page do not. The power range (60–115 W) is chosen by the laptop maker, so two laptops with the same badge can differ a lot.
Can an RTX 5070 Ti run a 27B or 32B model?
Not at Q4. Qwen3.6 27B is 17GB at Q4_K_M and Qwen 2.5 Coder 32B is 20GB; both exceed 16GB before any context. A 32B does load at 3-bit (qwen2.5:32b-instruct-q3_K_S is 14GB as Ollama lists it) with a short context, but a 14B at Q4 with real KV room usually answers better than a 32B squeezed to Q3. Test both on your own prompts; full-quality 27–32B is what 24GB cards are for.
RTX 5070 Ti or RTX 5080 for local AI?
Same 16GB, same model list — every row on this page runs on both. NVIDIA lists the 5080 at 960 GB/s and 10,752 cores against the 5070 Ti’s 896 GB/s and 8,960 cores, so the 5080’s decode ceiling is ~7% higher and its compute advantage is larger. Neither can hold a 27B at Q4. If you are paying the 5080 premium for capacity, you want a 24GB card instead; if you are paying it for speed on 14B-class models, it is a real but modest gain.
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