Ranking updated on 09/10/2026
The RTX 2060 (6 GB GDDR6, 336 GB/s), available used for ~€100, is the most affordable entry-level LLM option. 6 GB limits you to 3–7B models in Q4 but lets you explore local LLMs.
RTX 2060 : purchasing alternative available for local AI — RTX 5060 Ti 16 GB :
Which PC should you choose for your budget? Our picks from €800 to €3,500 →
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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
Gemma 4 E2B: 2B active (5.1B total), ~3 GB VRAM Q4 (weights Ollama 4.3 GB in QAT, 7.2 GB by default). Text-and-image multimodal, 128k context, Apache 2.0.
ollama run gemma4:e2b
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
Dense 3B Apache 2.0, 12 languages including FR, 131k ctx, GQA 40Q/8KV. Tool calling and code FIM. Released April 29, 2026.
ollama run granite4.1:3b
| Rank | Model | Params | Q4 VRAM | Context | License | On RTX 2060 |
|---|---|---|---|---|---|---|
| #1 | OLMo 3 7B Think (SFT) | 7B | 4.2 GB | 16 000 | Apache 2.0 | 32 tok/s · Q5_K_M |
| #2 | GLM 5.3 7B | 7B | 4.1 GB | 128 000 | MIT | 32 tok/s · Q5_K_M |
| #3 | Gemma 4 E2B | 2B | 3 GB | 128 000 | Apache 2.0 | 20 tok/s · Q8 |
| #4 | OLMo 3 7B | 7B | 5 GB | 8 192 | Apache 2.0 | 12 tok/s · Q5_K_M |
| #5 | LFM2.5 7B | 7B | 4.1 GB | 32 768 | LFM Open License v1.0 | 32 tok/s · Q5_K_M |
| #6 | LFM2.5 DSpark | 7B | 4.1 GB | 32 768 | LFM Open License v1.0 | 32 tok/s · Q5_K_M |
| #7 | Granite 4.1 3B Instruct | 3B | 2 GB | 131 072 | Apache 2.0 | 25 tok/s · FP16 |
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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.
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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 ≤ 5 GB. Bonus for 1-7B and a strong bonus for ≤ 3B. 336 GB/s is a solid entry point.
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
RTX 2060 6 GB: Is an LLM possible?
Yes for 1–7B. Phi-4 Mini 3.8B Q4 at 35–40 tok/s, Llama 3.2 3B Q4 at 50+ tok/s, Mistral 7B Q4 at 15–18 tok/s. See guide.
Is 6 GB enough in 2026?
Just barely—Mistral 7B Q4 takes 4.5 GB + 1 GB context + 0.5 GB OS = tight. Prefer Phi-4 Mini or Llama 3.2 3B (2–3 GB). See 6 GB guide.
Used 2060 vs. 3050 8 GB?
3050 8 GB = +2 GB VRAM but older (224 GB/s vs 2060 336 GB/s). 2060 is faster for 7B, but 3050 supports more models. Depends on your priorities. See RTX 3050 8GB.
Is it better to upgrade to 5050?
New 5050 8 GB ~€280 vs used 2060 6 GB ~€100. If an LLM is the main use case, 5050 (GDDR7, +8 GB, modern Neural Engine). Otherwise, the 2060 remains functional. See RTX 5050.