Written by Jakub Rusinowski · Last updated July 16, 2026
Answering from a private document set: the model reads retrieved passages and must stay faithful to them.
Top pick: Gemma 4 31B
Scores 95.2/100 for retrieval-augmented generation over your own documents. 31B parameters, needing about 19.5 GB at Q4_K_M, 250K context, Apache-2.0.
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. Gemma 4 31B | 95.2 | 31B | 250K | Apache-2.0 | — (estimated) |
| 2. Qwen 3.6 27B | 94.5 | 28B | 256K | Apache-2.0 | — (estimated) |
| 3. Qwen 3.7 35B-A3B | 93 | 35B | 256K | Apache-2.0 | — (estimated) |
| 4. DeepSeek V4.1 Flash | 90.4 | 284B | 977K | MIT | — (estimated) |
| 5. DeepSeek V4-Flash | 90.2 | 284B | 977K | MIT | — (estimated) |
| 6. Inkling (NVFP4) | 88.1 | 1000B | 977K | Apache 2.0 | — (estimated) |
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-4.7 9B (79.3) GLM-6 9B (79.3) Qwen 3 8B (78.5) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | Qwen 3 14B (84.4) Qwen 2.5 14B Instruct (81.8) Qwen 3.5 14B (81.5) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | Qwen 3 14B (84.4) Mistral Small 3.1 24B (84) Qwen 2.5 14B Instruct (81.6) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Gemma 4 31B (97.5) Qwen 3.6 27B (96.7) Qwen 3.7 35B-A3B (95.2) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | Gemma 4 31B (95.6) Qwen 3.6 27B (94.5) Qwen 3.7 35B-A3B (93.8) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | Llama 4 Scout 17B (93.6) Gemma 4 31B (92.8) Llama 4.5 Scout (92.3) |
Context is the binding constraint, so the floor is the highest of any text workload (32K) and full marks need 256K. Reasoning carries the capability weight because RAG failures are almost always synthesis failures, not retrieval failures. Licence importance is raised to 0.6 — RAG is overwhelmingly deployed on private or commercial corpora.
Worked example — Gemma 4 31B: capability 92.8 × 0.418, quality 91.7 × 0.246, context 96.9 × 0.16, license 100 × 0.066, accessibility 80 × 0.111 + 3 tag bonus (long-context).
Requirements applied: context floor 65,536 tokens (ideal 262,144), quality floor 55, licence weight 0.6, latency weight 0.5.
Gemma 4 31B, scoring 95.2/100 against this workload's published requirements. 110 models qualified.
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.
Context is the binding constraint, so the floor is the highest of any text workload (32K) and full marks need 256K. Reasoning carries the capability weight because RAG failures are almost always synthesis failures, not retrieval failures. Licence importance is raised to 0.6 — RAG is overwhelmingly deployed on private or commercial corpora.