Best Local LLMs for Offline AI
Written by Jakub Rusinowski · Last updated July 15, 2026
A model that works with no internet connection at all — on a laptop, in the field, or inside an air-gapped network.
Top pick: Ternary Bonsai 27B
Scores 93.5/100 for fully offline and air-gapped use. 27B parameters, needing about 17.1 GB at Q4_K_M, 256K context, Apache 2.0.
Ranked for fully offline and air-gapped use
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. Ternary Bonsai 27B | 93.5 | 27B | 256K | Apache 2.0 | — (estimated) |
| 2. Gemma 4 12B (Unified) | 92.4 | 12B | 125K | Apache-2.0 | — (estimated) |
| 3. Qwen 3.7 35B-A3B | 92.1 | 35B | 256K | Apache-2.0 | — (estimated) |
| 4. Qwen 3 14B | 91.9 | 15B | 125K | Apache 2.0 | — (estimated) |
| 5. Qwen 3.6 35B-A3B | 91.5 | 35B | 256K | Apache-2.0 | — (estimated) |
| 6. Phi-4 Mini (3.8B) | 91.4 | 4B | 125K | MIT | — (estimated) |
Best pick for your memory budget
The strongest model overall is rarely the right answer — what matters is the strongest model that fits the memory you have. These picks are re-ranked per tier, so each one uses its budget rather than simply being small.
| Memory | Typical hardware | Recommended models |
|---|---|---|
| 8 GB | RTX 4060, RTX 3070, base MacBook Air | GLM-6 9B (90.9) GLM-4 9B (90.6) Qwen 3 8B (90.1) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | Gemma 4 12B (Unified) (93.2) Qwen 3 14B (92.7) DeepSeek R1 Distill Qwen 14B (91.3) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | Qwen 3 14B (92.7) Mistral Small 3.1 24B (92.4) Gemma 4 12B (Unified) (91.8) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Ternary Bonsai 27B (96.9) Qwen 3.7 35B-A3B (95.6) Gemma 4 31B (95.1) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | GLM-5.1 72B (95.2) Qwen 3.5 72B (93.8) Qwen 3.7 35B-A3B (93.5) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | Qwen 3.5 122B-A10B (MoE) (93.7) GPT-OSS 120B (93.2) Qwen 3.5 122B-A10B (93) |
How this ranking works
The lowest quality floor in the set (40) and the strongest tag weighting toward small, low-memory models. Offline use is constrained by the hardware you happen to have with you, so a model that runs on 8 GB and never stalls beats a stronger model that needs a desktop.
Worked example — Ternary Bonsai 27B: capability 88.6 × 0.389, quality 87.7 × 0.229, context 100 × 0.149, license 70 × 0.062, accessibility 80 × 0.172 + 6 tag bonus (laptop, on-device).
Requirements applied: context floor 8,192 tokens (ideal 65,536), quality floor 40, licence weight 0.6, latency weight 0.7.
Running fully offline and air-gapped use locally
- Download the weights *and* the runtime before going offline; most runtimes fetch model metadata on first run.
- On battery, sustained decode speed is well below the plugged-in figures quoted on hardware pages.
FAQ
What is the best local LLM for fully offline and air-gapped use?
Ternary Bonsai 27B, scoring 93.5/100 against this workload's published requirements. 159 models qualified.
What hardware do I need for fully offline and air-gapped use?
A credible answer starts at 8 GB of memory. Larger budgets unlock materially stronger models — the table above lists the best pick at each tier.
How were these models ranked?
The lowest quality floor in the set (40) and the strongest tag weighting toward small, low-memory models. Offline use is constrained by the hardware you happen to have with you, so a model that runs on 8 GB and never stalls beats a stronger model that needs a desktop.
Hardware for This Workload
- Best GPU for offline ai
- Best models for the NVIDIA GeForce RTX 4060 Ti 8GB
- Best models for the NVIDIA GeForce RTX 3080 Ti
- Best models for the NVIDIA GeForce RTX 4090 Laptop GPU
Related Workloads
- Best local LLMs for privacy
- Best local LLMs for enterprise assistant
- Best local LLMs for vision
- Best local LLMs for fine-tuning