VRAM
80 GB
TDP
400 W
MSRP
$15k
Perf/W
0.12 tok/s/W
Cost/1K tok
$0.0033/k
Tested
2024-08-28
NVIDIA A100 80GB SXM has a source-attributed result of 48.0 tok/s on Llama 3 70B Q4 (batch 1, 4096-token context, Q4_K_M; runtime not documented). This is a reference report, not an independently verified lab result. A 70B Q4_K_M artifact needs roughly 40GB for weights alone; smaller memory configurations require explicit offload or model splitting and do not establish full-GPU residency.
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 supports many 70B quantized configurations. 70B FP16 weights need about 140GB before cache and runtime overhead, so they require more than one device or explicit offload. Use matched model settings to compare published throughput.
NVIDIA A100 80GB SXM with 80GB VRAM at 400W TDP, scored across 7 workloads with 8 benchmark records.
Reference workload
Asure 12B
145 tok/s
Quantization
Q4_K_M
8K 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 | 9100.0emb/s | FP16 | 1K | , | Curated Aggregate | 2024-09-25 |
| 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 | , | Curated Aggregate | 2025-05-01 |
There is insufficient comparable evidence to score NVIDIA A100 80GB SXM. A score needs results for multiple devices with matching workload, runtime version, quantization, context and batch size.
Planning estimates for 80GB: weights plus at least 2GB or 10% overhead. Actual KV cache depends on model, context and cache format; confirm the artifact before buying.
Trust score
6/10
MyAI rating
Not scored
Runs
Not documented
Freshness
Stale
Source-linked row with explicit verification status.
Record date: 2025-05-01; 496 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.
There is insufficient comparable evidence for a purchase recommendation. Check model fit, software support and a current seller quote.
Affiliate note: we do not have a device-level ASIN yet, so this opens tagged Amazon search results for the exact product name.