NVIDIA A100 80GB: Local AI & Business Fit
The datacenter workhorse of the LLM boom: 80GB HBM2e with strong tensor throughput, now widely available used and in the cloud.
Here’s what the NVIDIA A100 80GB means for a business that wants to run private AI on hardware it controls: which open LLMs fit, which agents it can power, the AI Business OS tier it suits, and whether to run local, cloud or hybrid.
Specs at a glance
- Memory
- 80 GB
- Memory type
- HBM2e
- Bandwidth
- 2,039 GB/s
- Approx FP16
- 312 TFLOPS
- Architecture
- Ampere
- Process
- TSMC 7nm
- Power
- 400 W
- Launch year
- 2020
Specs are approximate figures. TFLOPS shown is tensor FP16; figures vary by sparsity. Often deployed 4x/8x with NVLink/NVSwitch. Still very capable for serving and fine-tuning.
AI compatibility scores
Transparent 0–100 heuristics blending usable memory, bandwidth and compute — relative guidance, not benchmarks.
Compatible LLMs
Open-weight chat, coding and reasoning models from our catalog graded for the NVIDIA A100 80GB, best fit first.
- Qwen2.5 72BQwen · 72B · Qwen License
Fits at Q4_K_M (~44GB) with ~26.4GB headroom — about 1 concurrent instance.
Q4_K_M · ~44GBRuns well - Llama 3.1 70BLlama · 70B · Llama Community License
Fits at Q4_K_M (~42GB) with ~28.4GB headroom — about 1 concurrent instance.
Q4_K_M · ~42GBRuns well - Llama 3.3 70BLlama · 70B · Llama Community License
Fits at Q4_K_M (~42GB) with ~28.4GB headroom — about 1 concurrent instance.
Q4_K_M · ~42GBRuns well - DeepSeek-R1 Distill Llama 70BDeepSeek · 70B · MIT
Fits at Q4_K_M (~42GB) with ~28.4GB headroom — about 1 concurrent instance.
Q4_K_M · ~42GBRuns well - Mixtral 8x7B (MoE)Mistral · 47B · Apache-2.0
Fits at Q8_0 (~50GB) with ~20.4GB headroom — about 1 concurrent instance.
Q8_0 · ~50GBRuns well - CodeLlama 34BCodeLlama · 34B · Llama Community License
Fits at FP16 (~68GB) with ~2.4GB headroom — about 1 concurrent instance.
FP16 · ~68GBRuns well - Qwen2.5 32BQwen · 32B · Apache-2.0
Fits at FP16 (~64GB) with ~6.4GB headroom — about 1 concurrent instance.
FP16 · ~64GBRuns well - Qwen3 32BQwen · 32B · Apache-2.0
Fits at FP16 (~64GB) with ~6.4GB headroom — about 1 concurrent instance.
FP16 · ~64GBRuns well
Best models by business workload
Best for coding agents
Code completion, review and refactoring on private source.
- Qwen2.5 72BRuns well
- Llama 3.3 70BRuns well
- CodeLlama 34BRuns well
Best for RAG / search
Answering over your documents with citations.
- Qwen2.5 72BRuns well
- Llama 3.1 70BRuns well
- Llama 3.3 70BRuns well
Best for business automation
Document extraction and back-office workflows.
- Llama 3.1 70BRuns well
- Gemma 2 27BRuns well
- Gemma 3 27BRuns well
Good for a private AI Business OS?
Yes — this is a viable private AI Business OS host for a department-scale deployment, running models like Qwen2.5 72B on hardware you control.
Headline model it can host: Qwen2.5 72B.
Where it falls short
- ▸No major limitations for typical local AI workloads at this tier.
Business agents that make sense
How this machine fits the core AI Business OS agent archetypes:
- Strong fitCustomer Support Agent
Answers customers over your docs, drafts replies, triages tickets.
- Strong fitDocument / RAG Agent
Reads contracts, reports and wikis and answers with citations.
- Strong fitLegal Evidence Agent (DocMatch-style)
Searches case files and exhibits to surface and link evidence.
- Strong fitHotel / Hospitality Agent
Handles guest messaging, bookings and front-desk automation.
- Strong fitAccounting / Odoo Agent
Extracts invoices, reconciles data and drives ERP workflows.
- Strong fitCoding / Product Engineering Agent
Local code completion, review and refactoring on private source.
- CapableFounder Ops / Business Command Center
A fleet of cooperating agents running the whole business privately.
“Cloud-assist” means run it locally for light loads and burst to the cloud for heavier jobs. See business use cases for how each agent maps to hardware.
Frequently asked questions
Is the NVIDIA A100 80GB good for running local AI?+
It scores 72/100 on our Local AI Score (Strong tier), based on its 80GB of memory and available bandwidth/compute. That makes it suited to the Business AI Business OS tier.
Which LLMs can the NVIDIA A100 80GB run?+
Comfortably: Qwen2.5 72B (Q4_K_M), Llama 3.1 70B (Q4_K_M), Llama 3.3 70B (Q4_K_M). Larger models may run with heavier quantization or by splitting across devices.
Should I run AI locally or in the cloud on the NVIDIA A100 80GB?+
Local-first is recommended. Enough capability to host real agents locally for privacy and predictable cost; use cloud only to burst beyond peak demand.
Can I turn the NVIDIA A100 80GB into a private AI Business OS?+
Yes. AI Business OS can run on this machine at the Business tier, giving you private agents on your own hardware. See the call-to-action above to get started.
Turn the NVIDIA A100 80GB into a private AI Business OS
Run your own AI agents on hardware you control — private by design, no per-seat data leaving your premises. BrainOutput helps you pick the right machine and turn it into a working AI Business OS.
Get startedRelated hardware
NVIDIA H100 (80GB)
The defining datacenter accelerator for generative AI: 80GB HBM3, very high bandwidth, and transformer-optimized tensor cores.
- Memory
- 80 GB
- Architecture
- Hopper
NVIDIA H200 (141GB)
An H100 with a much larger, faster memory system: 141GB HBM3e and ~4.8 TB/s, ideal for long-context and very large models.
- Memory
- 141 GB
- Architecture
- Hopper
NVIDIA B200 (placeholder)
Blackwell-generation datacenter GPU reported around 192GB HBM3e. Placeholder until detailed specs are verified.
- Memory
- 192 GB
- Architecture
- Blackwell
NVIDIA L40S
A versatile 48GB datacenter card for inference and graphics — a popular, cost-effective cloud and on-prem serving option.
- Memory
- 48 GB
- Architecture
- Ada Lovelace