Best Local LLMs for Enterprise assistant
Written by Jakub Rusinowski · Last updated September 6, 2026
A shared internal assistant over company knowledge, serving several people from one deployment.
Top pick: Gemma 4 31B
Scores 96/100 for a private company assistant. 31B parameters, needing about 19.5 GB at Q4_K_M, 250K context, Apache-2.0.
Ranked for a private company assistant
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. Gemma 4 31B | 96 | 31B | 250K | Apache-2.0 | — (estimated) |
| 2. MiMo-V2.5-Pro 1T | 95.1 | 1000B | 977K | MIT | — (estimated) |
| 3. Qwen3.8 27B | 94.6 | 28B | 256K | Apache-2.0 | — (estimated) |
| 4. DeepSeek V4-Pro | 94.1 | 1600B | 977K | MIT | — (estimated) |
| 5. DeepSeek V4.1 | 94.1 | 1600B | 977K | MIT | — (estimated) |
| 6. Qwen 3.7 35B-A3B | 93.9 | 35B | 256K | Apache-2.0 | — (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 | IBM Granite 4.1 Granite 4.1 8B (82.2) IBM Granite 4.2 Granite 4.2 8B (81.2) GLM-6 9B (81.1) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | Qwen 3 14B (84.9) DeepSeek R1 Distill Qwen 14B (82) IBM Granite 4.1 Granite 4.1 8B (81.4) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | Devstral Small 2 24B (89) Mistral Small 3.1 24B (87.3) Qwen 3 14B (84.9) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Gemma 4 31B (97.5) Qwen3.8 27B (96) Qwen 3.7 35B-A3B (95.3) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | Gemma 4 31B (96.3) Qwen3.8 27B (94.6) Qwen 3.7 35B-A3B (94.4) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | Gemma 4 31B (94.4) Qwen3.8 27B (92.9) Llama 4 Scout 17B (92.7) |
How this ranking works
Licence importance is set to the maximum (1.0) — this is the one workload where a non-commercial or restricted licence is disqualifying in practice regardless of capability, so it is weighted as heavily as the model's own quality.
Worked example — Gemma 4 31B: capability 92.6 × 0.415, quality 91.7 × 0.244, context 97.3 × 0.159, license 100 × 0.11, accessibility 80 × 0.073 + 3 tag bonus (long-context).
Requirements applied: context floor 32,768 tokens (ideal 262,144), quality floor 60, licence weight 1, latency weight 0.5.
Running a private company assistant locally
- Multi-user serving changes the hardware maths: concurrent requests each carry their own KV cache, so memory scales with users, not just context.
- Check the licence text, not the label — several "open" model licences carry user-count or field-of-use restrictions.
FAQ
What is the best local LLM for a private company assistant?
Gemma 4 31B, scoring 96/100 against this workload's published requirements. 159 models qualified.
What hardware do I need for a private company assistant?
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?
Licence importance is set to the maximum (1.0) — this is the one workload where a non-commercial or restricted licence is disqualifying in practice regardless of capability, so it is weighted as heavily as the model's own quality.
Hardware for This Workload
- Best GPU for enterprise assistant
- 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 fine-tuning
- Best local LLMs for offline ai
- Best local LLMs for privacy
- Best local LLMs for vision