Best GPU for Local AI Document analysis
Written by Jakub Rusinowski · Last updated October 7, 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 117 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 | 117 | Gemma 4 31B | ~59.4 tok/s |
| Best value | Intel Arc B580 | 12 GB | $249 | 82 | Qwen 3 14B | ~33 tok/s |
| Budget pick | Intel Arc B570 | 10 GB | $219 | 81 | Qwen 3 14B | ~27.8 tok/s |
| Most memory | NVIDIA DGX Spark | 128 GB | $4,699 | 131 | 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 | 117 | ~59.4 | 0.1 | $17 |
| AMD Radeon RX 7900 XTX | 74.8 | 24 GB | $999 | 117 | ~33.9 | 0.1 | $9 |
| NVIDIA GeForce RTX 4090 | 74.2 | 24 GB | $1,599 | 117 | ~35.5 | 0.08 | $14 |
| NVIDIA GeForce RTX 3090 Ti | 73.6 | 24 GB | $1,999 | 117 | ~35.5 | 0.08 | $17 |
| NVIDIA GeForce RTX 3090 | 72.2 | 24 GB | $1,499 | 117 | ~33.1 | 0.09 | $13 |
| NVIDIA RTX 6000 Ada Generation | 70.6 | 48 GB | $6,799 | 121 | ~33.9 | 0.11 | $56 |
| AMD Radeon RX 7900 XT | 69.9 | 20 GB | $899 | 106 | ~28.6 | 0.09 | $8 |
| NVIDIA GeForce RTX 5070 Ti | 68.8 | 16 GB | $749 | 92 | ~40.3 | 0.13 | $8 |
| NVIDIA DGX Spark | 68.1 | 128 GB | $4,699 | 131 | ~48.9 | 0.33 | $36 |
| NVIDIA L40S | 66.9 | 48 GB | $7,499 | 121 | ~30.7 | 0.09 | $62 |
| NVIDIA GeForce RTX 5080 | 66.8 | 16 GB | $999 | 92 | ~42.9 | 0.12 | $11 |
| NVIDIA RTX A6000 | 64.1 | 48 GB | $4,649 | 121 | ~27.5 | 0.09 | $38 |
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 117 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 81 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 Intel Arc B580
- 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