NVIDIA RTX 4000 SFF Ada Generation — Benchmarks & Specs
Amazon listing: $2,096.96
*Price sourced from Amazon.com. Last updated 2026-10-05. Price and availability subject to change.
Bottom line: how fast is the RTX 4000 SFF Ada Generation?
For local LLM inference it generates 24.7 tokens/sec running qwen2.5:14b at q4_K_M under llama.cpp, per LocalScore.ai. In Geekbench 5 OpenCL it scores 124,926 points, per technical.city. Its 20 GB of VRAM is the binding constraint for local inference: the largest model on file at 4-bit quantization on this card is qwen2.5:32b (32B parameters, IQ4_XS), generating 16.4 tokens/sec under llama.cpp, per Hardware Corner.
Every figure above is a row in the tables below, and each row links out to the review or public benchmark database the number was taken from. SpecPicks aggregates published measurements; it does not report first-party benchmark runs.
The RTX 4000 SFF Ada Generation is a graphics card from the Ada Lovelace family from NVIDIA. Key on-paper specs include 20 GB of VRAM. Data on this page draws on 3 linked listings, 9 synthetic benchmark results, 10 community AI inference reports, compiled from Hardware Corner, LocalScore.ai, LocalLLaMA, PassMark, technical.city, Zimaspace Docs, 3DMark, UL Benchmarks; each table row links to its source. Read this page when shopping the RTX 4000 SFF Ada Generation, comparing it against other graphics cards in your build, or sizing it for a specific workload (synthetic benchmark scores or local LLM inference).
AI Inference Performance
Tokens per second under each model + quantization. Higher = faster generation. Bars compare runs across the same model.
| Model | Quantization | Relative | Tokens/sec | VRAM used | Source |
|---|---|---|---|---|---|
| qwen3:30b | Q4 llama.cpp | 768.0 tok/s | — | LocalLLaMA 2026-02-25 | |
| llama3.2:1b | q4_K_M llama.cpp | 189.0 tok/s | — | LocalScore.ai 2024-01-01 | |
| qwen3.6:35b-a3b | Q3_K_S llama.cpp | 83.3 tok/s | 15.6 GB | Zimaspace Docs 2026-09-22 | |
| qwen3.6:35b-a3b | UD-IQ3_XXS llama.cpp | 74.5 tok/s | 14.9 GB | Zimaspace Docs 2026-09-22 | |
| llama3.1:8b | q4_K_M llama.cpp | 58.6 tok/s | — | Hardware Corner 2024-09-29 | |
| llama3.1:8b | q4_K_M llama.cpp | 44.4 tok/s | — | LocalScore.ai 2024-01-01 | |
| qwen2.5:14b | q4_K_M llama.cpp | 24.7 tok/s | — | LocalScore.ai 2024-01-01 | |
| llama3.1:8b | FP16 llama.cpp | 20.9 tok/s | — | Hardware Corner 2024-09-29 | |
| qwen2.5:32b | IQ4_XS llama.cpp | 16.4 tok/s | — | Hardware Corner 2024-09-29 | |
| qwen3:30b | — ollama | 13.0 tok/s | — | LocalLLaMA 2026-04-17 |
Synthetic Benchmarks
Higher is better. Bars are scaled within each benchmark family (multi-thread, single-thread, etc.) so you can compare like-with-like at a glance.
| Benchmark | Relative | Score | Source |
|---|---|---|---|
| Geekbench 5 OpenCL | 124,926 points | technical.city 2023-06-01 | |
| Geekbench 5 Vulkan | 110,912 points | technical.city 2023-06-01 | |
| PassMark G3D Mark | 20,605 points | PassMark VideoCardBenchmark 2023-04-21 | |
| PassMark G3D Mark | 20,492 points | PassMark Software 2023-04-21 | |
| PassMark G3D Mark | 20,492 pts | PassMark 2026-04-20 | |
| 3DMark Time Spy | 13,990 points | 3DMark 2023-06-01 | |
| 3DMark Steel Nomad | 2,206 points | UL Benchmarks (3DMark) 2025-01-01 | |
| PassMark G2D Mark | 1,081 pts | PassMark 2026-04-20 | |
| PassMark G2D Mark | 1,063 points | PassMark Software 2026-09-25 |
Products Featuring the RTX 4000 SFF Ada Generation
Systems with the RTX 4000 SFF Ada Generation
Laptops and desktops that ship with the RTX 4000 SFF Ada Generation — whole systems, listed separately from the part itself.
Full Specifications
| vram gb | 20 |
|---|
RTX 4000 SFF Ada Generation — Frequently Asked Questions
What is the RTX 4000 SFF Ada Generation best used for?
When was the RTX 4000 SFF Ada Generation released, and what was its launch MSRP?
Where do the RTX 4000 SFF Ada Generation benchmark numbers come from?
Can the RTX 4000 SFF Ada Generation run local LLMs?
Where can I buy the RTX 4000 SFF Ada Generation?
Buying guides that rank the RTX 4000 SFF Ada Generation's class
This page is the raw performance data. The guides below turn it into a ranked pick for a specific build.
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