VRAM
80 GB
TDP
400 W
MSRP
$15k
Perf/W
0.12 emb/s/W
Cost/1K tok
$0.0033/k
Tested
2024-08-28
NVIDIA A100 80GB SXM produces approximately 48.0 tok/s on Llama 3 70B at Q4_K_M quantization (batch 1, 4096-token context, 80GB VRAM, 400W TDP). That figure comes from 1 measured run on llama.cpp. The 70B model needs roughly 40GB of VRAM at Q4, so headroom and KV cache budget matter as much as raw throughput.
NVIDIA A100 SXM4 80GB — Ampere-architecture datacenter GPU from 2020. 80GB HBM2e at 2 TB/s, 400W TDP. The workhorse that trained the GPT-3 generation of models. Still widely deployed in cloud and on-prem.
80GB VRAM hosts 70B FP16 with context headroom. The most widely available datacenter GPU for inference — every cloud provider has A100 instances. ~145 tok/s on 12B Q4. Mature software support across every engine and framework. Best for: cloud-based inference, training runs up to 70B, and organizations with existing A100 infrastructure. Being displaced by H100/H200 for new deployments but remains the most accessible datacenter tier.
NVIDIA A100 80GB SXM with 80GB VRAM at 400W TDP, scored across 7 workloads with 8 benchmark records.
Best workload
Embedding throughput
9500 emb/s
Quantization
FP16
1K context · batch 1
| Workload | Score | Quant | Context | σ | Status | Tested |
|---|---|---|---|---|---|---|
| Llama 3 70B Q4 llm | 48.0tok/s | Q4_K_M | 4K | , | Curated Aggregate | 2024-08-28 |
| Llama 3 8B FP16 llm | 215.0tok/s | FP16 | 4K | , | Curated Aggregate | 2024-09-14 |
| Embedding throughput embedding | 9500.0emb/s | FP16 | 1K | , | Curated Aggregate | 2024-05-21 |
| Mistral 7B llm | 168.0tok/s | Q4_K_M | 4K | , | Curated Aggregate | 2024-04-15 |
| Gemma 2 9B llm | 138.0tok/s | Q4_K_M | 8K | , | Curated Aggregate | 2024-07-09 |
| Qwen 2.5 14B llm | 108.0tok/s | Q4_K_M | 8K | , | Curated Aggregate | 2024-10-30 |
| Asure 12B llm | 145.0tok/s | Q4_K_M | 8K | ±5.8 | Curated Aggregate | 2025-05-01 |
NVIDIA A100 80GB SXM clears a 9.0/10 based on workload-normalized throughput, memory headroom, efficiency, value, trust, and coverage.
What models fit this 80GB card at different quantization levels.
Trust score
6/10
MyAI rating
9.0
Runs
1
Freshness
Stale
Source-linked row with explicit verification status.
Tested on 2024-05-21; 828 days old.
Enterprise buying note
Retailer we'd check first
Amazon search plus reseller quotes
For datacenter and accelerator parts, Amazon is useful for spotting live listings, accessories, or used pulls, but serious procurement usually happens through integrators, brokers, or cloud partners.
Enterprise procurement, not retail
This silicon is typically acquired through an authorized OEM partner, system integrator, or hyperscaler reseller. For on-demand access, compare hourly rates at RunPod, Vast.ai, Lambda Labs, or your existing cloud provider before committing to capital expenditure.
This is one of the highest-scoring parts in our database, so it is worth tracking when inventory lands near fair-market pricing.
Affiliate note: we do not have a device-level ASIN yet, so this opens tagged Amazon search results for the exact product name.