NVIDIA A100 80GB SXM

What it does well

The A100 80GB SXM is the GPU that defined the modern LLM era — every model from GPT-3.5 through Llama 2 was trained or first deployed on this hardware. In 2026 it's a legacy SKU but still ubiquitous on cloud providers and still legitimately good for many production inference workloads. 80 GB HBM2e at 2.0 TB/s sits very close to H100 PCIe's bandwidth, which means inference performance on memory-bound workloads (the dominant case) is much closer to H100 than the 4-year-architecture-gap suggests. The full CUDA stack works — vLLM, SGLang, TRT-LLM all support sm_80 and many providers' default deployment images target A100 first. NVLink 600 GB/s between SXM cards enables genuine multi-card tensor parallelism at scale; an 8× A100 80GB DGX node with NVLink full-mesh remains a serious 70B–200B production setup. Cloud rental at ~$1.50–$2.50/hr SXM is roughly half the H100 SXM price, and the gap on inference $/throughput often makes A100 the right pick for budget-conscious production. Used-market A100 80GB SXM has settled around $14,000–$17,000 — still a serious cap-ex, but the lowest path to 80 GB HBM datacenter memory.

Where it breaks

• No FP8 native. Ampere is BF16/FP16/INT8 only — no FP8 native, no Transformer Engine. Modern inference frameworks that exploit FP8 (TRT-LLM, vLLM FP8 paths) lose substantial throughput here vs H100 / H200 / B200. Quantization at FP8 is software-emulated, not hardware.
• Bandwidth is good, not best. 2 TB/s vs H100's 3.35 TB/s vs H200's 4.8 TB/s vs B200's 8 TB/s. For long-context decode where bandwidth dominates, newer cards win cleanly.
• Architecture EOL is approaching. NVIDIA still supports sm_80 in CUDA 12.x but feature parity with newer architectures is fading. New optimizations (FP4, Blackwell-specific Transformer Engine 2, etc.) skip A100.
• Cap-ex is hard to justify in 2026. $14,000–$17,000 for used A100 80GB SXM with no warranty + 4-year-old architecture + no FP8 vs $25,000 for H100 PCIe or H200 PCIe NVL with full warranty + Hopper architecture + FP8. Buying A100 retail in 2026 is rarely the right call; renting is the dominant pattern.
• Power and cooling are datacenter-grade. 400 W TDP SXM, requires a SXM4 motherboard or DGX-class server. Not for any office workstation deployment.

Ideal model range

• Sweet spot: 70B Q4–Q5 production inference. A100 still serves this beautifully — 80 GB fits 70B Q5 with 32K context comfortably; 2 TB/s bandwidth keeps it fed.
• Sweet spot: 405B FP16 across 8× A100 NVLinked DGX node. The most-deployed 405B inference setup as of late 2025 / early 2026.
• Sweet spot: 32B–70B production multi-tenant serving via vLLM continuous batching with 16–32 concurrent users.
• Sweet spot: BF16 fine-tuning at 7B–70B QLoRA, 7B FP16 full fine-tuning. The proven training tier.
• Comfortable: Embedding models, classifiers, smaller LMs at very high concurrency.
• Stretch: 671B at Q3 across 8× A100 (640 GB combined). Workable, slower than H100 cluster.

Bad use cases

• Buying retail in 2026. Pick H200 for new datacenter cap-ex; rent A100 if your workload is intermittent.
• Workloads that need FP8 throughput. Pick H100 or newer.
• Anything that fits 48 GB. L40S at 1/4 the cap-ex (~$7,500) wins for 48 GB tier production serving.
• Frontier training. B200 is the right tier; H200 is the value-conscious pick.
• Single-user / hobbyist workloads. Rent for a few hours at $1.50–$2.50; don't buy.

Verdict

Use this (rental) if you're running production inference for 70B–200B models at moderate concurrency, your serving stack already targets sm_80 (most do), $/throughput at $1.50–$2.50/hr beats your H100/H200 rental rate, and you don't need FP8. A100 is still the silent workhorse of cloud LLM inference in 2026 — most providers default to A100 unless you specifically request newer.

Buy this (used) if you're building a 8× A100 DGX-class node for $80k–$120k all-in (vs $200k+ for an 8× H100 SXM node), you have steady-state utilization >70%, and a 3–4 year operational horizon. Hard to justify for smaller deployments.

Skip this if you're standing up new datacenter cap-ex (H200 is the right tier), you need FP8 throughput (Hopper or Blackwell), your workload fits L40S (better $/throughput at the 48 GB tier), you're a hobbyist (rent or buy consumer), or you're frontier-training (B200 cluster).

How it compares

• vs H100 SXM (80 GB) → H100 SXM has ~67% more bandwidth (3.35 TB/s vs 2 TB/s), FP8 native, Transformer Engine 1, and ~2× FP16 tensor compute. Both are 80 GB. Pick H100 SXM for new builds; pick A100 for cost-conscious rental or value used cap-ex. See /compare/nvidia-a100-80gb-sxm-vs-nvidia-h100-sxm.
• vs H200 (141 GB) → H200 has 76% more memory + 140% more bandwidth + FP8 + better architecture at higher rental ($3–$4.50/hr) and cap-ex ($31,000 retail). Pick H200 for new builds and frontier inference; A100 for cost-conscious 70B-class rental.
• vs A100 40GB → Same architecture, same bandwidth band, half the memory, ~$11,000 used vs ~$15,000 used. Pick 80GB SXM for serious production use; 40GB is a cost-floor pick that gets memory-constrained on 70B-class.
• vs L40S (48 GB) → L40S at $7,500 is roughly 1/2 the price + Ada-generation features but with 60% the memory ceiling and 43% the bandwidth. For 70B Q4 / 32B FP16 inference under 48 GB, L40S wins $/throughput. For >48 GB workloads, A100 80GB is the floor.
• vs renting on Runpod / Lambda / Together → A100 80GB SXM rents at ~$1.50–$2.50/hr on most providers — the most-available serious-LLM rental tier. For workloads under 50% utilization or short-horizon experiments, rent A100 first; only buy after sustained high utilization makes cap-ex pencil out.