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NVIDIA A100 PCIe 80GB
NVIDIA · GPU · Ampere

NVIDIA A100 PCIe 80GB — Benchmarks & Specs

80 GB VRAM300W TDP$15,000 MSRP2021

Short answer: the NVIDIA A100 PCIe 80GB generates 22.1 tokens/sec on llama3:70b (q4_K_M) under llama.cpp in a single cited run, per XiongjieDai/GPU-Benchmarks-on-LLM-Inference; there are too few comparable runs on file for a median. Its 80 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 $10,999.00, checked (listings can end at any time).

View used on eBay — $10,999.00 → Search Amazon for the NVIDIA A100 PCIe 80GB →

Who should buy this instead: the NVIDIA A100 PCIe 80GB's 80 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 80 GB, start at the 128 GB unified pick.

Bottom line: how fast is the NVIDIA A100 PCIe 80GB?

For local LLM inference it generates 22.1 tokens/sec running llama3:70b at q4_K_M under llama.cpp, per XiongjieDai/GPU-Benchmarks-on-LLM-Inference. It has 80 GB of VRAM, and the llama3:70b run above (70B parameters) is the largest model on file at 4-bit quantization on this card. Larger models have no sourced run on file.

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.

eBay listing (Used): $10,999.00, checked . eBay listings are third-party and can end at any time; as an eBay Partner Network member, SpecPicks may earn a commission.

Check what fits in 80 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 A100 PCIe 80GB's 80 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 37.5 GB free. Check a model in the Will-It-Run matrix →

What fits in the NVIDIA A100 PCIe 80GB's 80 GB, and what doesn't

7 of these 7 model files load whole into 80 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 80 GB?Cited speed on this card
Llama 3.1 8B 4.9 GB fileQ4_K_MFits~75.1 GB left for context142 tok/s1 cited run, vllm, per Markaicode
Qwen3 14B 9 GB fileQ4_K_MFits~71 GB left for contextNo public measurement on file
gpt-oss-20b 21B-A3.6B · 12.1 GB fileMXFP4Fits~67.9 GB left for contextNo public measurement on file
Qwen3 30B-A3B 18.6 GB fileQ4_K_MFits~61.4 GB left for contextNo public measurement on file
Qwen3 32B 19.8 GB fileQ4_K_MFits~60.2 GB left for contextNo public measurement on file
Gemma 3 27B 28.7 GB fileQ8_0Fits~51.3 GB left for contextNo public measurement on file
Llama 3.3 70B 42.5 GB fileQ4_K_MFits~37.5 GB left for contextNo public measurement on file

The NVIDIA A100 PCIe 80GB is a datacenter accelerator from the Ampere family released in 2021 from NVIDIA. Key on-paper specs include 80 GB of HBM2e memory, 300W TDP. It launched with a $15,000 MSRP, though street prices typically diverge meaningfully from launch pricing. Data on this page draws on 26 community AI inference reports (top 12 shown), compiled from XiongjieDai/GPU-Benchmarks-on-LLM-Inference, DatabaseMart, Qwen official docs, BentoML, llama.cpp GitHub, Markaicode; each table row links to its source. Read this page when shopping the NVIDIA A100 PCIe 80GB, comparing it against other datacenter accelerators for your server or cluster, or sizing it for a specific workload (LLM inference and serving, fine-tuning, or HPC compute).

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 A100 PCIe 80GB, 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 200.9 tok/s — llama.cpp GitHub 2024-06
llama3.1:8b Q4_K_M vllm 142.0 tok/s 6.0 GB Markaicode 2024-10
llama3:8b q4_K_M llama.cpp 138.3 tok/s — XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub) 2024-05
llama3:8b FP16 llama.cpp 54.6 tok/s — XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub) 2024-05
llama3:70b q4_K_M llama.cpp 22.1 tok/s — XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub) 2024-05
qwen2.5:32b-instruct BF16 transformers 17.5 tok/s 61.6 GB Qwen official docs (speed benchmark) 2024-11
qwen2.5:72b AWQ transformers 11.5 tok/s — Qwen Official Documentation 2024-09
qwen2.5:72b-instruct GPTQ-Int4 transformers 11.1 tok/s 39.9 GB Qwen official docs (speed benchmark) 2024-11

Batched serving throughput

Aggregate output tokens per second on the NVIDIA A100 PCIe 80GB summed across many 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-r1:14b FP16 vllm 50 concurrent requests 1133.6 tok/s aggregate 28.0 GB DatabaseMart 2026-08-25
llama3:70b AWQ-4bit other 100 concurrent requests 700.0 tok/s aggregate — BentoML 2024-06-05
deepseek-r1-distill-qwen:32b BF16 vllm 50 concurrent requests 577.2 tok/s aggregate — Database Mart (GPU Mart) blog 2026-10-07
qwq:32b FP16 vllm 50 concurrent requests 522.2 tok/s aggregate 62.0 GB DatabaseMart 2026-08-25

Full Specifications

TDP300 W
VRAM80 GB
CUDA cores6912
Memory typeHBM2e

NVIDIA A100 PCIe 80GB — Frequently Asked Questions

What is the NVIDIA A100 PCIe 80GB best used for?
NVIDIA A100 PCIe 80GB is an 80 GB VRAM Ampere-family graphics card. High-end 4K gaming and local LLM inference are the headline use cases; the benchmark tables on this page place it within its generation.
When was the NVIDIA A100 PCIe 80GB released, and what was its launch MSRP?
NVIDIA A100 PCIe 80GB launched in 2021 at a $15,000 MSRP. Street prices drift from launch pricing over a product's life, so check the current listing before buying.
Where do the NVIDIA A100 PCIe 80GB 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 A100 PCIe 80GB run local LLMs?
Yes — AI inference results for the NVIDIA A100 PCIe 80GB are on file; the AI Inference Performance section on this page lists model, quantization and tokens-per-second for each run. With 80 GB VRAM, 32B-parameter open-weight models fit at Q4 quantization.
Where can I buy the NVIDIA A100 PCIe 80GB?
SpecPicks has no verified listing of the NVIDIA A100 PCIe 80GB 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.

Buying guides that rank the NVIDIA A100 PCIe 80GB's class

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