NVIDIA A100 80GB
The NVIDIA A100 80GB has 80 GB VRAM and 2039 GB/s memory bandwidth. It can run 56 of our 80 tracked models natively in VRAM at 8k context.
With 80 GB HBM2e, the NVIDIA A100 80GB is a datacenter-tier GPU that can run 56 models natively. It handles 70B-class models at Q4 quantization.
The NVIDIA A100 80GB is the high-memory SXM variant of NVIDIA's Ampere datacenter GPU, featuring 80GB HBM2e at 2,039 GB/s. It runs 70B models at Q4–Q5 in a single GPU, and remains widely available in cloud instances (AWS p4de, GCP a2-ultragpu). While superseded by the H100 for new deployments, A100s are heavily discounted on the secondhand market, making them popular for cost-conscious inference setups.
NVIDIA A100 80GB: 2020 Ampere datacenter with 80GB HBM2e at 2039 GB/s — predecessor to H100.
70B at Q4 native, 405B at Q2. ~40-60 t/s for 7B, ~15-25 t/s for 70B.
Full CUDA support. Widely deployed on AWS p4d and Azure NDv4. Still excellent for inference.
| Vendor | NVIDIA |
| Architecture | Ampere |
| VRAM | 80 GB |
| Memory type | HBM2e |
| Memory bandwidth | 2039 GB/s |
| Compute backend | CUDA |
| Tier | Datacenter |
| Released | 2020 |
| Models (native) | 56 / 80 |
| Models (offload) | 4 / 80 |
Cloud GPU Rental
Don't want to buy a NVIDIA A100 80GB? RunPod is a cloud GPU rental service — rent one by the hour instead, no contract, no upfront hardware cost.
Pay by the hour · no contract · pods start in about a minute.
Rent a NVIDIA A100 80GB on RunPod ↗ (+$5 signup credit)Affiliate link — CanItRun may earn a commission. Doesn't affect the fit calculation above.
Popular models for this GPU
Models this GPU runs natively in VRAM (56)
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q2_K · ~25.8 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7NVFP4 · ~70.4 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7NVFP4 · ~63.9 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7NVFP4 · ~148.3 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3NVFP4 · ~42.7 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4NVFP4 · ~61.5 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9NVFP4 · ~61.5 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1NVFP4 · ~34.3 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9NVFP4 · ~35.2 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0NVFP4 · ~35.2 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4NVFP4 · ~35.2 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7NVFP4 · ~58.7 t/s
- Command-R 35B35B · MMLU-Pro 33.0NVFP4 · ~46.9 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2NVFP4 · ~228.3 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2NVFP4 · ~67.5 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0NVFP4 · ~69 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5BF16 · ~19.8 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0BF16 · ~19.7 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4BF16 · ~19.7 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0BF16 · ~19.7 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3BF16 · ~64.9 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2BF16 · ~20.3 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5BF16 · ~63.7 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0BF16 · ~23.1 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5BF16 · ~23.9 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2BF16 · ~23.8 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~150.4 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6BF16 · ~49.6 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~26.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~28.6 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~49 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0FP32 · ~21.9 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7FP32 · ~21.9 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4FP32 · ~23.1 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6FP32 · ~26.4 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6FP32 · ~26.6 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2FP32 · ~25.9 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0FP32 · ~33.5 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~40.1 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~40.1 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~39.9 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~42.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~44.1 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~80.3 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~77.9 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~71.9 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~96.5 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~104.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~117.6 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~157.7 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~157.6 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~212.6 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~253.5 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~306.3 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~630.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~746.4 t/s
Models that fit with CPU offload (4)
These use system RAM for layers that don't fit in VRAM — expect much slower inference.
Too large for this GPU (20)
Compare NVIDIA A100 80GB with other GPUs
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Frequently asked questions
- How much VRAM does the NVIDIA A100 80GB have?
- The NVIDIA A100 80GB has 80 GB of HBM2e with 2039 GB/s memory bandwidth.
- What is the NVIDIA A100 80GB best for?
- With 80 GB of VRAM, the NVIDIA A100 80GB is a server-class GPU designed for running the largest open-weight models (70B–405B) at high quantization with ample context.
- What LLMs can the NVIDIA A100 80GB run locally?
- The NVIDIA A100 80GB can run 56 of the 80 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at NVFP4, Llama 3.1 8B Instruct at FP32, Llama 3.2 3B Instruct at FP32.
- Can the NVIDIA A100 80GB run Llama 3.3 70B Instruct?
- Yes. The NVIDIA A100 80GB runs Llama 3.3 70B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 35.2 tokens per second.
- Can the NVIDIA A100 80GB run Qwen 3.6 27B?
- Yes. The NVIDIA A100 80GB runs Qwen 3.6 27B natively in VRAM at BF16 quantization, achieving approximately 23.8 tokens per second.
- Can the NVIDIA A100 80GB run Llama 3.1 8B Instruct?
- Yes. The NVIDIA A100 80GB runs Llama 3.1 8B Instruct natively in VRAM at FP32 quantization, achieving approximately 40.1 tokens per second.
- Can I rent the NVIDIA A100 80GB instead of buying it?
- Yes — RunPod and similar cloud GPU providers let you rent NVIDIA A100 80GB instances by the hour, with no long-term contract. This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.