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NVIDIA RTX A6000 48GB
NVIDIA · GPU · Ampere Pro

NVIDIA RTX A6000 48GB — Benchmarks & Specs

48 GB VRAM300W TDP$4,650 MSRP2020

Short answer: the NVIDIA RTX A6000 48GB generates a median 102 tokens/sec on 7-9B dense models at 4-bit (Q4) under llama.cpp, across 3 cited runs from llama.cpp GitHub Discussion #15013, GPU-Benchmarks-on-LLM-Inference and OpenLLMBenchmarks. Its 48 GB of VRAM holds up to DeepSeek-R1-Distill-Llama 70B at Q4_K_M with an ~8K context, by the Will-It-Run matrix's sizing (27 of 27 models fit). A used eBay listing for it is $4,349.00, checked (listings can end at any time).

Who should buy this instead: the NVIDIA RTX A6000 48GB's 48 GB puts it in the 48 GB+ tier of the local-LLM GPU picks by VRAM (one card per tier, each with cited tok/s). If the models you run fit in 24 GB, compare the 24 GB pick. If they need more than 48 GB, start at the 128 GB unified pick.

NVIDIA RTX A6000 48GB: verdict by use case

Each verdict quotes a figure from a linked source or a row in the tables below. Where no cited figure exists for a use case, there is no verdict line for it.

Bottom line: how fast is the NVIDIA RTX A6000 48GB?

SpecPicks median: 102 tokens/sec across 3 cited single-user runs of 7-9B dense models at 4-bit (Q4) under llama.cpp (range 102–145 tok/s), from llama.cpp GitHub Discussion #15013, GPU-Benchmarks-on-LLM-Inference, OpenLLMBenchmarks. For larger models, the SpecPicks median is 14.6 tokens/sec across 4 cited single-user runs of 65-73B dense models at 4-bit (Q4) under llama.cpp (range 14.1–14.6 tok/s), from OpenLLMBenchmarks (2 runs), GPU-Benchmarks-on-LLM-Inference, LocalScore. For local LLM inference it generates 14.5 tokens/sec running qwen2.5:72b at q4_K_M under ollama, per DatabaseMart. In 3DMark Fire Strike Graphics it scores 27,511 points, per technical.city. Its 48 GB of VRAM is the binding constraint for local inference: the largest models on file at 4-bit quantization on this card are 72B-class: the qwen2.5:72b run above and qwen:72b (q4_0), which generates 14.5 tokens/sec under ollama, per DatabaseMart. That run's source does not report a context length; the Will-It-Run matrix sizes each model at its listed quantization with an ~8K context, and by that measure DeepSeek-R1-Distill-Llama 70B at Q4_K_M is the largest that fits in 48 GB.

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; the SpecPicks median is computed here from the runs it names, each a row in the AI table. SpecPicks aggregates published measurements; it does not report first-party benchmark runs.

eBay listing (Used): $4,349.00, checked . eBay listings are third-party and can end at any time; as an eBay Partner Network member, SpecPicks may earn a commission.

Read the full comparison → Radeon Pro W7900 48GB vs RTX A6000 48GB for Local 70B Inference (2026)

Check what fits in 48 GB: by file size at each listed quantization, 27 of the 27 open-weight model files in the SpecPicks Will-It-Run matrix load entirely into the NVIDIA RTX A6000 48GB's 48 GB while keeping at least 1.5 GB reserved for an ~8K context; the largest, DeepSeek-R1-Distill-Llama 70B (a 42.5 GB Q4_K_M file per its Hugging Face listing), leaves roughly 5.5 GB free. Will DeepSeek-R1-Distill-Llama 70B run on the RTX A6000 48GB? → · Full Will-It-Run matrix

What fits in the NVIDIA RTX A6000 48GB's 48 GB, and what doesn't

7 of these 7 model files load whole into 48 GB with room for an ~8K context. A file fits when the card keeps at least 1.5 GB free after the weights; file sizes are the GGUF downloads on Hugging Face. Speeds are this card's single-user generation speed from cited community runs at the Q4-class file listed; a row with no cited run says so rather than estimating one.

ModelQuantFits in 48 GB?Cited speed on this card
Llama 3.1 8B 4.9 GB fileQ4_K_MFits~43.1 GB left for contextNo public measurement on file
Qwen3 14B 9 GB fileQ4_K_MFits~39 GB left for context40.7 tok/s1 cited run, llama.cpp, per hardware-corner.net
gpt-oss-20b 21B-A3.6B · 12.1 GB fileMXFP4Fits~35.9 GB left for contextNo public measurement on file
Qwen3 30B-A3B 18.6 GB fileQ4_K_MFits~29.4 GB left for contextNo public measurement on file
Qwen3 32B 19.8 GB fileQ4_K_MFits~28.2 GB left for context27.5 tok/smedian of 3 cited runs, per DatabaseMart, LocalScore (Mozilla Builders), hardware-corner.net
Gemma 3 27B 28.7 GB fileQ8_0Fits~19.3 GB left for contextNo public measurement on file
Llama 3.3 70B 42.5 GB fileQ4_K_MFits~5.5 GB left for context13.6 tok/s1 cited run, ollama, per DatabaseMart (Ollama GPU benchmark)

The NVIDIA RTX A6000 48GB is a graphics card from the Ampere Pro family released in 2020 from NVIDIA. Key on-paper specs include 48 GB of GDDR6 VRAM, 300W TDP. It launched with a $4,650 MSRP, though street prices typically diverge meaningfully from launch pricing. Data on this page draws on 27 synthetic benchmark results (top 10 shown), 35 community AI inference reports (top 17 shown), compiled from DatabaseMart, OpenLLMBenchmarks, GPU-Benchmarks-on-LLM-Inference, llama.cpp GitHub Discussion #15013, LocalScore, StorageReview, 3DMark, CpuTronic, llama.cpp GitHub, Notebookcheck, PassMark, technical.city, TopCPU.net; each table row links to its source. Read this page when shopping the NVIDIA RTX A6000 48GB, 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.

Local LLM inference throughput on the NVIDIA RTX A6000 48GB, 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. Batched serving runs are listed separately below.
Model Quantization Relative Tokens/sec VRAM used Source
llama2:7b q4_0 llama.cpp 144.9 tok/s — llama.cpp GitHub Discussion #15013 2025-08
llama3:8b q4_K_M llama.cpp 102.2 tok/s — GPU-Benchmarks-on-LLM-Inference (GitHub, XiongjieDai) 2024-05
llama3:8b q4_K_M llama.cpp 102.2 tok/s — OpenLLMBenchmarks 2024-09-30
llama2:13b — ollama 63.6 tok/s 7.4 GB DatabaseMart 2025-03
phi4:14b q4_K_M ollama 52.6 tok/s — DatabaseMart (Ollama GPU benchmark) 2024-12-17
Mixtral 8x7B Q4_K_M llama.cpp 52.0 tok/s 27.8 GB llama.cpp GitHub 2025-11-20
qwen2.5:14b q4_K_M ollama 50.3 tok/s — DatabaseMart (Ollama GPU benchmark) 2024-12-17
deepseek-r1:14b q4_0 ollama 48.4 tok/s — DatabaseMart 2025-02
Llama 3.1 70B Q4_K_M llama.cpp 14.6 tok/s 42.5 GB OpenLLMBenchmarks 2025-12
llama3:70b q4_K_M llama.cpp 14.6 tok/s — GPU-Benchmarks-on-LLM-Inference (GitHub, XiongjieDai) 2024-05
qwen2.5:72b q4_K_M ollama 14.5 tok/s 43.7 GB DatabaseMart 2026-08-25
qwen:72b q4_K_M ollama 14.5 tok/s — DatabaseMart (Ollama GPU benchmark) 2024-12-17
DeepSeek-R1 70B Distill Q4_K_M llama.cpp 14.1 tok/s 43.2 GB LocalScore (Mozilla Builders) retrieved 2026-05-06

Batched serving throughput

Aggregate output tokens per second on the NVIDIA RTX A6000 48GB summed across 50 concurrent requests (serving runtimes such as vLLM). This is total server throughput, not one user's generation speed — each request generates far slower — so these rows are not comparable with the single-stream table above.
Model Quantization Relative Tokens/sec (aggregate) VRAM used Source
deepseek-r1-distill-llama:8b BF16 vllm 50 concurrent requests 1643.9 tok/s aggregate 15.0 GB DatabaseMart 2026-08-25
deepseek-r1-distill-llama:8b FP16 vllm 50 concurrent requests 1385.8 tok/s aggregate 15.0 GB DatabaseMart 2025-03
llama3.1:8b FP16 vllm 50 concurrent requests 1218.6 tok/s aggregate 15.0 GB DatabaseMart 2025-03
qwen2.5:14b FP16 vllm 50 concurrent requests 695.5 tok/s aggregate 28.0 GB DatabaseMart 2025-03

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 NVIDIA RTX A6000 48GB — higher is better. Each row links to the public database the number was taken from. A benchmark listed more than once is separate published results, shown as published rather than averaged.
Benchmark Relative Score Source
3DMark Fire Strike Graphics 27,511 points technical.city retrieved 2026-05-01Same result reported by Notebookcheck
3DMark Fire Strike 25,280 points Notebookcheck retrieved 2026-06-09
PassMark G3D Mark 24,800 PassMark 2026-05-06
PassMark G3D Mark 22,767 points PassMark Software retrieved 2026-05-01
PassMark G3D Mark 22,657 points PassMark PerformanceTest retrieved 2026-06-09
3DMark Fire Strike Extreme 22,185 points StorageReview 2023-10-19
3DMark Time Spynvidia rtx 6000 ada vs rtx a6000 review 18,451 points StorageReview 2023-10-19
3DMark Time Spy 18,152 points CpuTronic retrieved 2026-06-09
3DMark Time Spy 17,796 points 3DMark 2022-01
3DMark Time Spy 17,742 points TopCPU.net retrieved 2026-05-01

Full Specifications

TDP300 W
VRAM48 GB
CUDA cores10752
Memory typeGDDR6

NVIDIA RTX A6000 48GB — Frequently Asked Questions

What is the NVIDIA RTX A6000 48GB best used for?
NVIDIA RTX A6000 48GB is a 48 GB VRAM Ampere Pro-family workstation graphics card. Professional 3D modeling, CAD, rendering and simulation on certified drivers, plus local LLM inference in its large VRAM pool, are the headline use cases; the benchmark tables on this page place it within its generation.
When was the NVIDIA RTX A6000 48GB released, and what was its launch MSRP?
NVIDIA RTX A6000 48GB launched in 2020 at a $4,650 MSRP. Street prices drift from launch pricing over a product's life, so check the current listing before buying.
Where do the NVIDIA RTX A6000 48GB 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 NVIDIA RTX A6000 48GB run local LLMs?
Yes — AI inference results for the NVIDIA RTX A6000 48GB are on file; the AI Inference Performance section on this page lists model, quantization and tokens-per-second for each run. With 48 GB VRAM, 32B-parameter open-weight models fit at Q4 quantization.
Where can I buy the NVIDIA RTX A6000 48GB?
SpecPicks has no verified listing of the NVIDIA RTX A6000 48GB on file yet; the Amazon search link at the top of this page shows its current offers. SpecPicks earns a small affiliate commission on qualifying purchases.

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