Ranking updated on 09/10/2026
The RTX 5070 Ti (16 GB GDDR7, 896 GB/s) is the price/performance sweet spot in the mid-range Blackwell lineup. 16 GB unlocks 24B models in Q4 and 14B models in Q5/Q6. A direct competitor to the 4070 Ti Super.
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Dense 14B with hybrid thinking. Equals Qwen 2.5 32B Based on STEM/code.
ollama run qwen3:14b
MIT 14B reasoner. Beats R1-Distill-Llama-70B on AIME/GPQA with 50× fewer parameters.
ollama run phi4-reasoning:14b
Exceptional reasoning for its size. STEM-focused.
ollama run phi4:14b
Coding 14B. HumanEval 89.6, LiveCodeBench 37.1. VRAM sweet spot for self-hosted coding.
ollama run qwen2.5-coder:14b
Distilled R1 Qwen 14B. AIME24 69.7, MATH-500 93.9. Outperforms o1-mini on many benchmarks.
ollama run deepseek-r1:14b
Dense 14B Apache 2.0. MMLU 79.7, HumanEval 83.5. 29+ languages. Good compromise.
ollama run qwen2.5:14b
Dense 8B Apache 2.0, 12 languages including FR, 131k context, GQA 32Q/8KV. MMLU 73.84, HumanEval 85.37. Released April 29, 2026.
# HuggingFace : ibm-granite/granite-4.1-8b
| Rank | Model | Params | Q4 VRAM | Context | License | On RTX 5070 Ti |
|---|---|---|---|---|---|---|
| #1 | Qwen 3 14B | 14B | 9 GB | 131 072 | Apache 2.0 | 20 tok/s · Q8 |
| #2 | Phi-4 Reasoning 14B | 14B | 9 GB | 32 768 | MIT | 20 tok/s · Q8 |
| #3 | Phi-4 14B | 14B | 9 GB | 16 384 | MIT | 20 tok/s · Q8 |
| #4 | Qwen 2.5 Coder 14B Instruct | 14B | 9 GB | 131 072 | Apache 2.0 | 20 tok/s · Q8 |
| #5 | DeepSeek R1 Distill Qwen 14B | 14B | 9 GB | 131 072 | MIT | 20 tok/s · Q8 |
| #6 | Qwen 2.5 14B Instruct | 14B | 9 GB | 131 072 | Apache 2.0 | 20 tok/s · Q8 |
| #7 | Granite 4.1 8B Instruct | 8B | 5 GB | 131 072 | Apache 2.0 | 35 tok/s · FP16 |
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
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Get the memo VRAM → best coding model → Ollama command (one screen, copy and paste). Then switch to the Copilote Local kit for a setup that actually works.
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Your card → the best coding model to run locally, and the exact Ollama command:
| Your VRAM | Typical GPUs / Macs | Recommended coding model | Command Ollama |
|---|---|---|---|
| 8 GB | RTX 4060 / 3060 · M1-M2 16 GB | Qwen 3.5 9B (Q4, 6.6 GB — 256k context) | ollama run qwen3.5:9b |
| 12 GB | RTX 3060 12 GB / 4070 / 5070 | Qwen 3.5 9B (Q8, 11 GB) or Gemma 4 12B (7.6 GB) | ollama run qwen3.5:9b-q8_0 |
| 16 GB | RTX 5070 Ti / 4080 / 5080 · RX 9070 XT · M4 24 GB | Devstral 24B (Q4, 14 GB) — coding-agent specialist | ollama run devstral:24b |
| 24 GB | RTX 3090 / 4090 · RX 7900 XTX · M4 Pro 48 GB | Qwen 3.8 27B (Q4, 18 GB) — the “close to Copilot” option | ollama run qwen3.8:27b |
| 32 GB | RTX 5090 | Qwen 3.6 35B-A3B (Q4, 23 GB) — fast MoE | ollama run qwen3.6:35b |
| 48 GB+ | Mac M4 Max 64 GB · M2 Ultra 128 GB | Qwen3-Coder 30B-A3B (Q8, 32 GB — 256k context) | ollama run qwen3-coder:30b-a3b-q8_0 |
-base : ollama run qwen2.5-coder:7b-base — it’s still the reference for this specific use case. ⚠️ Qwen 3.8: its reasoning is set very high by default and it “overthinks” simple requests — lower it to low (or turn it off) on first launch. ⚠️ License trap: Codestral 22B = Mistral Non-Production License → prohibited for coding at work. Qwen 3.5/3.8, Gemma 4, and Devstral are Apache 2.0. 💡 Running out of memory? Keep ~1.5 GB of VRAM free for context, or drop down one quantization level.🔌 To connect it to VS Code: Cline (multi-file agent), Aider (CLI) or Tabby/Twinny (FIM autocomplete) — they all connect to Ollama locally. The kit Local Copilot — ready-to-paste configs + tested setup — is available: /copilote-local.
Filter: Q4_K_M models ≤ 14 GB. 7–14B and 13–24B bonus options available. GDDR7 bandwidth: 896 GB/s.
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
RTX 5070 Ti vs. 4070 Ti Super?
Even 16 GB. 5070 Ti GDDR7 896 GB/s vs 4070 Ti Super GDDR6X 672 GB/s. ~30% gain. Mistral Small 24B Q4: 5070 Ti ~35 tok/s vs 4070 Ti Super ~26 tok/s. See RTX 4070 Ti Super.
LLM sweet spot for 2026 on a 5070 Ti?
Qwen 3 14B Q6 (~12 GB) or Mistral Small 24B Q4 (~13 GB) — GPT-4-ish quality, 35–50 tok/s. Comfortable for long RAG (32k context).
Can you train LoRA on a 5070 Ti?
Yes for 7B QLoRA (Unsloth + 4-bit): ~12-13 GB with an 8-bit optimizer. 14B is too tight. See complete guide.
5070 Ti or 5080?
5080 = 16 GB as well, but 960 GB/s + 10,752 CUDA cores vs. 8,960 on the 5070 Ti. Gain ~10–15%. Extra cost ~€300–400—not worthwhile unless you have a heavy workflow. See RTX 5080.
Learn more with our detailed head-to-head matchups of the finalists:
Prices in euros (€) are French market prices including VAT, checked by QuelLLM. US prices differ: the Amazon buttons show the current US price.