Ranking updated on 09/10/2026
The RTX 4060 (8 GB GDDR6, 272 GB/s) is the entry-level Ada Lovelace. 8 GB is enough for 7–9B models in Q4, but throughput remains modest (~20 tok/s on 7B).
RTX 4060 : purchasing alternative available for local AI — RTX 5060 Ti 16 GB :
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SFT “thinking” fine-tune of OLMo 3 7B: step-by-step reasoning, 16k context, ~4.2 GB VRAM in Q4. 100% open, Apache 2.0 license.
# HuggingFace : zimplex/olmo3-7b-think-sft-eosfix-16k-3ep-euc
GLM 5.3 (Zhipu): dense 7B specialized in code and reasoning, 128k context, ~4.1 GB VRAM in Q4. Lightweight, runs on a 6–8 GB GPU, MIT license.
ollama pull glm-5.3
Dense 7B 100% open (weights + data + code). Complete transparency for research.
ollama run olmo-3:7b
LFM2.5 7B (Liquid AI): dense Liquid Foundation Model, 32k context, 4.1 GB VRAM Q4. Optimized for CPU and edge. Released May 2026.
ollama pull lfm2.5
LFM2.5 DSpark (Liquid AI): dense 7B Liquid Foundation Model, 32k context, ~4.1 GB VRAM Q4. Versatile chat optimized for edge/CPU.
# HuggingFace : LiquidAI/LFM2.5-DSpark
Sovereign French-language LLM, trained on French corpora.
ollama run lucie:7b
Specialized in code. Competes with proprietary models on HumanEval.
ollama run qwen2.5-coder:7b
| Rank | Model | Params | Q4 VRAM | Context | License | On RTX 4060 |
|---|---|---|---|---|---|---|
| #1 | OLMo 3 7B Think (SFT) | 7B | 4.2 GB | 16 000 | Apache 2.0 | 32 tok/s · Q8 |
| #2 | GLM 5.3 7B | 7B | 4.1 GB | 128 000 | MIT | 32 tok/s · Q8 |
| #3 | OLMo 3 7B | 7B | 5 GB | 8 192 | Apache 2.0 | 12 tok/s · Q5_K_M |
| #4 | LFM2.5 7B | 7B | 4.1 GB | 32 768 | LFM Open License v1.0 | 32 tok/s · Q8 |
| #5 | LFM2.5 DSpark | 7B | 4.1 GB | 32 768 | LFM Open License v1.0 | 32 tok/s · Q8 |
| #6 | Lucie 7B | 7B | 5 GB | 4 096 | Apache 2.0 | 12 tok/s · Q5_K_M |
| #7 | Qwen 2.5 Coder 7B | 7B | 5 GB | 131 072 | Apache 2.0 | 12 tok/s · Q5_K_M |
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
Free memo
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.
The Local Copilot kit — the Ollama + Cline + Aider configs are ready to paste, with tuned Modelfiles, troubleshooting, and lifetime online access →No spam. Unsubscribe in 1 click. Your data stays with us (never resold).
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 ≤ 7 GB. Bonus for 1–7B and ≤ 3B (fast). 272 GB/s is adequate for 7B.
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
RTX 4060 vs 5060?
5060 GDDR7 448 GB/s vs 4060 GDDR6 272 GB/s = ~65% gain. Mistral 7B Q4: 5060 ~40 tok/s vs 4060 ~24 tok/s. If buying new, choose the 5060. See RTX 5060.
Used 4060 or 12 GB 3060?
3060 12 GB = +4 GB VRAM but ~25% slower (GDDR6 360 GB/s vs. 4060 272 GB/s, but Ada is more efficient). 3060 12 GB wins for LLMs (13B accessible). See RTX 3060.
LLM 4060 sweet spot?
Mistral 7B Q5 (~5.5 GB) at 18 tok/s, Llama 3.2 3B Q4 at 40+ tok/s, Phi-4 Mini at 45+ tok/s. For 13B+, target 12 GB (RTX 4070).
Do you need a 4060 in 2026?
For LLMs alone, target a 5060 (GDDR7 gain) or 4060 Ti 16 GB (VRAM gain). The 4060 remains adequate if it's already in the PC. See 4060 Ti 16 GB.