NVIDIA H100 (80GB): Local AI & Business Fit
The defining datacenter accelerator for generative AI: 80GB HBM3, very high bandwidth, and transformer-optimized tensor cores.
Here’s what the NVIDIA H100 (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
- HBM3
- Bandwidth
- 3,350 GB/s
- Approx FP16
- 990 TFLOPS
- Architecture
- Hopper
- Process
- TSMC 4N
- Power
- 700 W
- Launch year
- 2022
Specs are approximate figures. Figures reflect the SXM variant; PCIe is lower (~2 TB/s, ~350-400W). FP16 TFLOPS is the marketing tensor figure with sparsity assumptions — treat as relative.
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 H100 (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 an org-wide, multi-agent 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.
- Strong fitFounder 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 H100 (80GB) good for running local AI?+
It scores 91/100 on our Local AI Score (Elite tier), based on its 80GB of memory and available bandwidth/compute. That makes it suited to the Enterprise AI Business OS tier.
Which LLMs can the NVIDIA H100 (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 H100 (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 H100 (80GB) into a private AI Business OS?+
Yes. AI Business OS can run on this machine at the Enterprise tier, giving you private agents on your own hardware. See the call-to-action above to get started.
Turn the NVIDIA H100 (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.
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- Memory
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