Ranking updated on 09/10/2026
The RTX 4070 Ti Super (16 GB GDDR6X, 672 GB/s) doubled the 4070 Ti's VRAM. Ideal for reaching 24B in Q4 without stepping up to the 4080.
RTX 4070 Ti Super : purchasing alternative available for local AI — RTX 5070 Ti 16 GB :
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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 4070 Ti Super |
|---|---|---|---|---|---|---|
| #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 |
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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 ≤ 14 GB. Bonus: 7-14B and 13-24B (Mistral Small 24B). Bandwidth 672 GB/s.
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
4070 Ti Super vs. 4070 Ti?
16 GB vs. 12 GB. This is CRITICAL for LLMs: the 4070 Ti Super unlocks Mistral Small 24B Q4, while the 4070 Ti tops out at 14B Q4. See RTX 4070 Ti.
4070 Ti Super vs. 5070 Ti?
Also 16 GB. 5070 Ti GDDR7 896 GB/s = ~30% faster than 4070 Ti Super GDDR6X 672 GB/s. If buying new, 5070 Ti. If buying used, 4070 Ti Super is still excellent. See RTX 5070 Ti.
4070 Ti Super LLM sweet spot?
Qwen 3 14B Q6 (~12 GB) or Mistral Small 24B Q4 (~13 GB). 30–50 tok/s depending on the model. For code, Qwen 2.5 Coder 14B Q6.
Can you fine-tune on a 4070 Ti Super?
Comfortable QLoRA 7B (~10 GB). Tight QLoRA 14B (~14 GB). For serious fine-tuning, target 24 GB (RTX 4090/3090). See guide.
Learn more with our detailed head-to-head matchups of the finalists:
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