Other · GPU

GRID RTX6000P-2B — Benchmarks & Specs

Short answer: the GRID RTX6000P-2B generates 131 tokens/sec on Llama 3.2 (q4_K_M) under llama.cpp in a single cited run, per GitHub: XiongjieDai/GPU-Benchmarks-on-LLM-Inference; there are too few comparable runs on file for a median.

Bottom line: how fast is the GRID RTX6000P-2B?

At 1080p Ultra with DLSS Quality upscaling, the GRID RTX6000P-2B averages 60 fps in Cyberpunk 2077, per PCGameBenchmarks. For local LLM inference it generates 131.0 tokens/sec running Llama 3.2 at q4_K_M under llama.cpp, per GitHub: XiongjieDai/GPU-Benchmarks-on-LLM-Inference. In PassMark G3D Mark it scores 3,343 points, per PassMark Software.

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 GRID RTX6000P-2B is a graphics card from Other. Data on this page draws on 2 synthetic benchmark results, 2 community AI inference reports, 1 measured game frame-rate result, compiled from GitHub: XiongjieDai/GPU-Benchmarks-on-LLM-Inference, PassMark Software, PCGameBenchmarks, DatabaseMart; each table row links to its source. Read this page when shopping the GRID RTX6000P-2B, comparing it against other graphics cards in your build, or sizing it for a specific workload (gaming at 1080p/1440p/4K, synthetic benchmark scores, or local LLM inference).

Gaming Performance (measured FPS)

Average and 1% low frame rates by game, resolution, and quality preset. Bars are scaled against the fastest result on this page.

Measured gaming frame rates for the GRID RTX6000P-2B by game, resolution, and quality preset. “1% low” is the frame-time floor that determines perceived smoothness. Each row links to its original review or benchmark database.
Game	Resolution	Settings	Relative	Avg FPS	1% low	Source
Cyberpunk 2077	1080p	Ultra DLSS Quality		60 fps	—	PCGameBenchmarks 2023-10

AI Inference Performance

Tokens per second under each model + quantization. Higher = faster generation. Bars compare runs across the same model.

Local LLM inference throughput on the GRID RTX6000P-2B, in generated tokens per second for a single request (one user's generation speed). Higher is better; each row links to the community report or benchmark database it came from.
Model	Quantization	Relative	Tokens/sec	VRAM used	Source
Llama 3.2	q4_K_M llama.cpp		131.0 tok/s	39.6 GB	GitHub 2024-05
Qwen 3	q8_0 ollama		56.0 tok/s	20.0 GB	DatabaseMart 2024-05

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.

Synthetic benchmark scores for the GRID RTX6000P-2B — higher is better. Each row links to the public database the number was taken from.
Benchmark	Relative	Score	Source
PassMark G3D Mark		3,343 points	PassMark Software retrieved 2026-06-12
PassMark G2D Mark		62 points	PassMark Software retrieved 2026-09-26

GRID RTX6000P-2B — Frequently Asked Questions

Where do the GRID RTX6000P-2B benchmark numbers come from?

Each benchmark row on this page names its source (public benchmark databases such as TechPowerUp, PassMark, Geekbench and Cinebench, and community reports such as r/LocalLLaMA threads) and links to it, so every number can be checked against the original.

Can the GRID RTX6000P-2B run local LLMs?

Yes — AI inference results for the GRID RTX6000P-2B are on file; the AI Inference Performance section on this page lists model, quantization and tokens-per-second for each run.

Where can I buy the GRID RTX6000P-2B?

No verified listing for this exact part is on file yet. The Compare tool and the /benchmarks index cover adjacent parts that are in stock.

Buying guides that rank the GRID RTX6000P-2B's class

This page is the raw performance data. The guides below turn it into a ranked pick for a specific build.

Editorial guides covering the GRID RTX6000P-2B

In-depth SpecPicks reviews, build guides, and head-to-heads referencing this graphics card.

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