Best Local LLMs for Privacy
作者： Jakub Rusinowski · 最后更新： 2026年8月15日
Work with data that must never leave your machine — legal, medical, financial or personal records.
Top pick: GPT-OSS 20B
Scores 93.4/100 for privacy-sensitive work. 21B parameters, needing about 13.4 GB at Q4_K_M, 128K context, Apache-2.0.
Ranked for privacy-sensitive work
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. GPT-OSS 20B | 93.4 | 21B | 128K | Apache-2.0 | — (estimated) |
| 2. Qwen 3.7 35B-A3B | 92.5 | 35B | 256K | Apache-2.0 | — (estimated) |
| 3. Gemma 4 31B | 92.2 | 31B | 250K | Apache-2.0 | — (estimated) |
| 4. GLM-4.7-Flash 30B-A3B | 92.1 | 30B | 193K | MIT | — (estimated) |
| 5. Qwen 3.6 35B-A3B | 91.8 | 35B | 256K | Apache-2.0 | — (estimated) |
| 6. 1-bit Bonsai 27B | 91.2 | 27B | 256K | Apache 2.0 | — (estimated) |
Frontier open-weight models (multi-GPU / private cloud)
These also score for privacy-sensitive work, but none runs on a single consumer GPU or workstation, so they are listed apart rather than ranked against the local picks above.
- DeepSeek V4-Pro — Private cloud / datacenter cluster
- DeepSeek R1 (671B) — Multi-GPU server
- Mistral Large 3 675B-A41B — Multi-GPU server
- GLM-5 744B — Multi-GPU server
- DeepSeek V4.1 Flash — Multi-GPU server
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 | 1-bit Bonsai 27B (90.8) GLM-6 9B (90.5) GLM-4 9B (90.4) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | Qwen 3 14B (91.7) DeepSeek R1 Distill Qwen 14B (91.4) Qwen 3.5 14B (90.2) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | GPT-OSS 20B (95.2) Qwen 3 14B (91.7) Mistral Small 3.1 24B (91.4) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Qwen 3.7 35B-A3B (95.5) GLM-4.7-Flash 30B-A3B (95.2) Gemma 4 31B (95.2) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | GLM-5.1 72B (94.8) Qwen 3.7 35B-A3B (93.7) Cogito v1 70B (93.4) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | GPT-OSS 120B (99.3) Qwen 3.5 122B-A10B (93) Qwen 3.5 122B-A10B (MoE) (92.7) |
How this ranking works
Weighted toward models that are practical to run entirely on hardware you own: a high licence weight (0.8) plus a moderate quality floor, rather than chasing the largest model. A model you cannot actually run locally scores nothing here no matter how capable it is.
Worked example — GPT-OSS 20B: capability 82.8 × 0.388, quality 82 × 0.228, context 100 × 0.148, license 100 × 0.082, accessibility 88 × 0.154 + 6 tag bonus (local-first, privacy-sensitive).
Requirements applied: context floor 16,384 tokens (ideal 131,072), quality floor 50, licence weight 0.8, latency weight 0.45.
Running privacy-sensitive work locally
- Running locally is necessary but not sufficient for privacy — check that your runtime's telemetry and any editor integration are also disabled.
FAQ
What is the best local LLM for privacy-sensitive work?
GPT-OSS 20B, scoring 93.4/100 against this workload's published requirements. 159 models qualified.
What hardware do I need for privacy-sensitive work?
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?
Weighted toward models that are practical to run entirely on hardware you own: a high licence weight (0.8) plus a moderate quality floor, rather than chasing the largest model. A model you cannot actually run locally scores nothing here no matter how capable it is.
Hardware for This Workload
- Best GPU for privacy
- 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
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- Best local LLMs for offline ai
- Best local LLMs for enterprise assistant
- Best local LLMs for rag