Best GPU for Local AI RAG
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 7 października 2026
Ranked for retrieval-augmented generation over your own documents: Answering from a private document set: the model reads retrieved passages and must stay faithful to them.
Best overall: NVIDIA GeForce RTX 5090
32 GB VRAM at 1792 GB/s. It runs 130 of the models that qualify for this workload; the strongest is Gemma 4 31B at an estimated 59.4 tokens/sec.
The picks
| GPU | VRAM | MSRP | Models that fit | Best model it runs | Est. speed | |
|---|---|---|---|---|---|---|
| Best overall | NVIDIA GeForce RTX 5090 | 32 GB | $1,999 | 130 | Gemma 4 31B | ~59.4 tok/s |
| Best value | AMD Radeon RX 7900 XT | 20 GB | $899 | 118 | Gemma 4 31B | ~28.6 tok/s |
| Budget pick | Intel Arc B570 | 10 GB | $219 | 94 | Ternary Bonsai 27B | ~16 tok/s |
| Most memory | NVIDIA DGX Spark | 128 GB | $4,699 | 145 | Llama 4 Scout 17B | ~17.8 tok/s |
Full ranking for retrieval-augmented generation over your own documents
| GPU | Score | VRAM | MSRP | Models fit | Est. speed | Tok/s per watt | Cost per model |
|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 | 79 | 32 GB | $1,999 | 130 | ~59.4 | 0.1 | $15 |
| AMD Radeon RX 7900 XTX | 76 | 24 GB | $999 | 130 | ~33.9 | 0.1 | $8 |
| NVIDIA GeForce RTX 4090 | 75.3 | 24 GB | $1,599 | 130 | ~35.5 | 0.08 | $12 |
| NVIDIA GeForce RTX 3090 Ti | 74.7 | 24 GB | $1,999 | 130 | ~35.5 | 0.08 | $15 |
| NVIDIA GeForce RTX 3090 | 73.5 | 24 GB | $1,499 | 130 | ~33.1 | 0.09 | $12 |
| NVIDIA RTX 6000 Ada Generation | 72.5 | 48 GB | $6,799 | 134 | ~33.9 | 0.11 | $51 |
| AMD Radeon RX 7900 XT | 70.9 | 20 GB | $899 | 118 | ~28.6 | 0.09 | $8 |
| NVIDIA L40S | 68.3 | 48 GB | $7,499 | 134 | ~30.7 | 0.09 | $56 |
| NVIDIA RTX A6000 | 65.5 | 48 GB | $4,649 | 134 | ~27.5 | 0.09 | $35 |
| Intel Arc Pro B70 | 64.2 | 32 GB | $949 | 130 | ~22.1 | 0.1 | $7 |
| AMD Radeon AI PRO R9700 | 63.3 | 32 GB | $1,299 | 130 | ~23.2 | 0.08 | $10 |
| NVIDIA GeForce RTX 3080 Ti | 46.7 | 12 GB | $1,199 | 95 | ~36.6 | 0.1 | $13 |
How these numbers are calculated
- GPUs are scored on four axes: capability (45%), speed (30%), value (20%) and efficiency (5%), each normalised across the whole ranking.
- Capability means the intrinsic strength of the best model the card can hold — not how many models fit, and not how fast it streams a small one.
- Speed is capped at 40 tok/s: past that, more throughput does not change how the model feels to use.
- Apple Silicon is excluded from this ranking. Those entries price a whole computer and rate a chip's package power, so on price-per-capability and performance-per-watt they would beat every add-in card by construction. Apple hardware is covered on the macOS platform page instead.
- 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.
FAQ
What is the best GPU for retrieval-augmented generation over your own documents?
The NVIDIA GeForce RTX 5090 — 32 GB of VRAM runs 130 qualifying models, the strongest being Gemma 4.
What is the cheapest GPU that works for retrieval-augmented generation over your own documents?
The Intel Arc B570 at $219, which runs 94 qualifying models.
How much VRAM do I need for retrieval-augmented generation over your own documents?
8 GB is the entry point at which a model for this workload will run at all. More memory buys a stronger model, not just a faster one.
What These GPUs Run
- Best models for the NVIDIA GeForce RTX 5090
- Best models for the AMD Radeon RX 7900 XT
- Best models for the Intel Arc B570
- Best models for the NVIDIA DGX Spark
GPU Reviews
- NVIDIA GeForce RTX 5090 review
- AMD Radeon RX 7900 XTX review
- NVIDIA GeForce RTX 4090 review
- NVIDIA GeForce RTX 3090 Ti review