NVIDIA A100 80GB SXM4 Cloud Pricing & Rental Rates
The A100 80GB remains the cost-effective choice for mid-size LLM fine-tuning and computer vision. Its 80 GB HBM2e and 2 TB/s bandwidth handle 13B-30B parameter models efficiently.
Target Workload: Cost-effective mid-size LLM fine-tuning & computer vision
Live Cloud Pricing for NVIDIA A100 80GB SXM4
Spot and reserved rates refreshed from provider APIs. Click a provider to deploy.
| Provider | GPU & VRAM | Interconnect | Spot Rate | On-Demand | Monthly | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Dedicated | N/A | $1.59 / hr | $3.98 / hr | $973 / mo | Instant |
Models That Fit NVIDIA A100 80GB SXM4 (80GB HBM2e)
Deterministic VRAM calculation (FP8) via entity graph. Only editorial and enriched models shown.
| MODEL | PARAMS | CONTEXT | FP8 VRAM | FIT? | CALCULATOR |
|---|---|---|---|---|---|
| DeepSeek R1 Distill Qwen 32B | 32B | 128K | 42.9 GB | ✓ fits | Pre-filled → |
| Llama 3.1 8B Instruct | 8.03B | 128K | 10.8 GB | ✓ fits | Pre-filled → |
| Llama 3.2 3B Instruct | 3.2B | 128K | 4.3 GB | ✓ fits | Pre-filled → |
| Llama 3.2 1B Instruct | 1B | 128K | 1.3 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 32B | 32.5B | 128K | 43.6 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 14B | 14.7B | 128K | 19.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 7B | 7.61B | 128K | 10.2 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 14B Instruct | 14.7B | 128K | 19.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 7B Instruct | 7.61B | 128K | 10.2 GB | ✓ fits | Pre-filled → |
| Mistral NeMo 12B | 12B | 128K | 16.1 GB | ✓ fits | Pre-filled → |
| Gemma 2 27B | 27B | 8K | 35.7 GB | ✓ fits | Pre-filled → |
| Gemma 2 9B | 9.24B | 8K | 12.2 GB | ✓ fits | Pre-filled → |
Inference & Serving Capacity
Practical model feasibility, max batch sizes, and KV-cache retention limits for NVIDIA A100 80GB SXM4.
Llama 3.3 70B
FEASIBLE4-way tensor parallelism, INT8 quantization
DeepSeek 671B
OOMRequires 8-way NVLink cluster minimum
Qwen 2.5 32B
FEASIBLEGPTQ/AWQ quantization recommended
vLLM Throughput (FP8)
~45 tok/s (vLLM, Llama 70B INT8, batch=1, 2-GPU TP)
Estimated tokens/second, single GPU, Llama-class model
Max Context Window (Llama 70B)
8k tokens (FP16) — 70B requires 4-way tensor parallelism
Maximum context length before KV-cache eviction
Hardware Bottleneck Analysis
Whether NVIDIA A100 80GB SXM4 is compute-bound (TFLOPS) or memory-bandwidth bound (GB/s) across workloads.
Bottleneck Classification
Memory-bandwidth bound — 2.0 TB/s limits batch scaling
Recommended Quantization
INT8 / INT4 (GPTQ/AWQ) — no FP8 support
Best Cluster Topology
4-way or 8-way NVLink 3.0 Baseboard
Deep Analysis
The A100 is memory-bandwidth bound across all batch sizes. Its 2.0 TB/s HBM2e delivers only 312 FP16 TFLOPS worth of bandwidth headroom — at batch≥32, the tensor cores idle waiting for weight data. For Llama 70B, the 80 GB VRAM fits the model in FP16 with ~8 GB remaining for KV-cache (limited to 8k context at batch=1). Tensor parallelism across 2-4 GPUs is mandatory for 70B models. The A100's strength is cost efficiency: at $1.59/hr on Lambda Labs, it delivers the best $/token for 13B-30B models.
Architecture & Die Breakdown
Architecture
Ampere GA100 — 7nm TSMC
TDP
400W
Memory Subsystem
80GB HBM2e at 2.0 TB/s bandwidth. High Bandwidth Memory provides the throughput needed to keep tensor cores fed during large batch inference.
Interconnect
NVLink 3.0 (600 GB/s). Enables multi-GPU tensor parallelism with high-bandwidth, low-latency GPU-to-GPU communication.
Precision Performance
| Precision | TFLOPS | Use Case |
|---|---|---|
| FP8 | 624 | Training & inference with mixed-precision |
| FP16 | 312 | Full-precision training, fine-tuning, evaluation |
Break-Even ROI Calculator
Monthly hours where reserved pricing beats spot for NVIDIA A100 80GB SXM4. Above the break-even point, reserve commits save money.
Spheron
RunPod
Lambda Labs
Break-even at 280 hours/month: if you run NVIDIA A100 80GB SXM4 more than 280 hours per month, reserved pricing on all three providers saves money. At 720 hours/month (24/7), reserved saves $430+/mo vs on-demand.
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Frequently Asked Questions
How much does it cost to rent an NVIDIA A100 80GB SXM4 per hour?▾
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Can an NVIDIA A100 80GB SXM4 run 70B parameter LLMs?▾
Is spot pricing reliable for distributed NVIDIA A100 80GB SXM4 training?▾
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