Best GPU for Local AI Document analysis
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 7 października 2026
Ranked for document analysis and extraction: Pulling structured facts, tables and clauses out of long documents such as contracts and reports.
Best overall: NVIDIA GeForce RTX 5090
32 GB VRAM at 1792 GB/s. It runs 151 of the models that qualify for this workload; the strongest is Gemma 4 31B at an estimated 57.6 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 | 151 | Gemma 4 31B | ~57.6 tok/s |
| Best value | AMD Radeon RX 7900 XT | 20 GB | $899 | 138 | Gemma 4 31B | ~27.7 tok/s |
| Budget pick | Intel Arc B570 | 10 GB | $219 | 112 | Ternary Bonsai 27B | ~16 tok/s |
| Most memory | NVIDIA DGX Spark | 128 GB | $4,699 | 168 | Qwen3.8-Flash-Next | ~48.9 tok/s |
Full ranking for document analysis and extraction
| GPU | Score | VRAM | MSRP | Models fit | Est. speed | Tok/s per watt | Cost per model |
|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 | 78 | 32 GB | $1,999 | 151 | ~57.6 | 0.1 | $13 |
| AMD Radeon RX 7900 XTX | 73.4 | 24 GB | $999 | 151 | ~32.8 | 0.09 | $7 |
| NVIDIA GeForce RTX 4090 | 73 | 24 GB | $1,599 | 151 | ~34.4 | 0.08 | $11 |
| NVIDIA GeForce RTX 3090 Ti | 72.4 | 24 GB | $1,999 | 151 | ~34.4 | 0.08 | $13 |
| NVIDIA GeForce RTX 3090 | 71.1 | 24 GB | $1,499 | 151 | ~32.1 | 0.09 | $10 |
| NVIDIA RTX 6000 Ada Generation | 69.7 | 48 GB | $6,799 | 155 | ~32.8 | 0.11 | $44 |
| AMD Radeon RX 7900 XT | 68.6 | 20 GB | $899 | 138 | ~27.7 | 0.09 | $7 |
| NVIDIA L40S | 66.1 | 48 GB | $7,499 | 155 | ~29.8 | 0.09 | $48 |
| NVIDIA RTX A6000 | 63.4 | 48 GB | $4,649 | 155 | ~26.7 | 0.09 | $30 |
| Intel Arc Pro B70 | 62 | 32 GB | $949 | 151 | ~21.4 | 0.1 | $6 |
| AMD Radeon AI PRO R9700 | 61.3 | 32 GB | $1,299 | 151 | ~22.4 | 0.07 | $9 |
| NVIDIA DGX Spark | 43.7 | 128 GB | $4,699 | 168 | ~48.9 | 0.33 | $28 |
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.
- Same context profile as RAG but with a higher coding weight (20%), since extraction almost always ends in structured output — JSON, a table, or a schema the model must not break. Licence importance is 0.6: the documents are usually commercial.
FAQ
What is the best GPU for document analysis and extraction?
The NVIDIA GeForce RTX 5090 — 32 GB of VRAM runs 151 qualifying models, the strongest being Gemma 4.
What is the cheapest GPU that works for document analysis and extraction?
The Intel Arc B570 at $219, which runs 112 qualifying models.
How much VRAM do I need for document analysis and extraction?
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