NVIDIA A100 80GB GPU
From $1.49/hr — billed by the minute
The premium A100 variant built for large model training. 80GB HBM2e with 2 TB/s memory bandwidth handles 70B parameter models and massive batch sizes. Train LLaMA, Mistral, and custom architectures at 70% less than AWS.
Powering teams that push boundaries
Trusted by companies including: Tesla, Hugging Face, Kaggle, Zoho, Weights & Biases, upGrad, Saama
Maximum memory for maximum scale
The A100 80GB delivers the memory and bandwidth needed for the largest AI training and inference workloads.
80GB HBM2e Memory
Double the standard A100. Fit 70B models in FP16, fine-tune 30B with full batch sizes. No quantization compromises, no gradient checkpointing trade-offs.
2 TB/s Memory Bandwidth
31% faster than A100 40GB (2,039 vs 1,555 GB/s). Keeps 432 Tensor Cores saturated. Up to 1.3x higher throughput on memory-bound workloads.
Multi-GPU NVLink Scaling
600 GB/s NVLink for up to 4 GPUs per instance. 320GB unified GPU memory — enough for full fine-tuning of 70B models with DeepSpeed ZeRO Stage 3.
MIG for Multi-Tenant Serving
Up to 7 isolated instances, each with 10GB dedicated memory (double the 40GB's 5GB MIG slices). Hardware-level isolation for multi-model production serving.
Key specs at a glance
70% less than AWS — rent a single GPU, not eight
Transparent, per-minute billing with no hidden fees. Pause anytime — only pay for active minutes.
| Provider | A100 80GB $/hr | Billing | Min. GPUs | Pre-configured |
|---|---|---|---|---|
| Jarvislabs | $1.49 | Per minute | 1 | |
| AWS (p4de.24xlarge) | ~$5.12/GPU | Per second | 8 (bundled) | — |
| Azure (ND96amsr) | ~$4.10/GPU | Per second | 8 (bundled) | — |
| Google Cloud | ~$3.67 | Per second | 1 | — |
| RunPod (Secure) | $1.49 | Per millisecond | 1 | — |
| Lambda | $1.79–2.06 | Per hour | 1 | — |
AWS charges $40.97/hr for 8x A100 80GB (p4de.24xlarge). On Jarvislabs, rent 1 GPU for $1.49/hr or 4 for $5.96/hr. Save 70% per GPU-hour vs. AWS.
What developers build on the A100 80GB
From training 70B LLMs to production inference at scale.
Train & Fine-tune Large LLMs
Only GPU under $2/hr that fits 70B models in FP16. Fine-tune LLaMA 3 70B, Mixtral 8x7B, CodeLlama 34B. 4x multi-GPU = 320GB.
Production Inference at Scale
Serve 70B models with vLLM/TGI at ~850 tokens/sec. No model sharding needed. Larger batch sizes, longer context windows.
RLHF & Preference Tuning
RLHF requires policy + reference + reward model simultaneously. 80GB (or 320GB across 4 GPUs) handles DPO, PPO, ORPO pipelines.
Large-Scale Data Processing
Millions of documents through embedding models, batch classification, synthetic data generation. Bigger batches = 2–3x higher throughput.
Technical specifications
Complete hardware specifications for the NVIDIA A100 80GB data center GPU.
| Specification | Value | Great for |
|---|---|---|
| Architecture | NVIDIA Ampere | 20x AI performance vs. prior gen |
| CUDA Cores | 6,912 | General-purpose GPU compute |
| Tensor Cores | 432 (3rd gen) | TF32/FP16/BF16/INT8/FP64 |
| VRAM | 80 GB HBM2e (ECC) | Models up to 70B in FP16 |
| Memory Bandwidth | 2,039 GB/s | 31% faster than A100 40GB |
| FP32 Performance | 19.5 TFLOPS | Traditional compute |
| TF32 Tensor | 156 TFLOPS | Auto mixed-precision training |
| FP16 Tensor | 312 TFLOPS | Mixed-precision training |
| BF16 Tensor | 312 TFLOPS | LLM training (preferred) |
| FP64 Tensor | 19.5 TFLOPS | Scientific / HPC |
| INT8 Tensor | 624 TOPS | Quantized inference |
| MIG Support | Up to 7 instances (10GB each) | Multi-model serving |
| NVLink | 600 GB/s bidirectional | Multi-GPU training |
| PCIe | Gen4 x16 | Host data transfer |
| TDP | 400W (SXM) | Maximum performance |
| Multi-GPU | Up to 4x per instance | 320GB unified memory |
Launch your A100 80GB instance in seconds
Three simple steps from sign-up to a running GPU instance.
Choose Template
PyTorch 2.x (with Transformers, DeepSpeed, PEFT, Accelerate), TensorFlow, JAX, or clean CUDA.
Configure & Launch
Select A100 80GB, 1–4 GPUs, allocate storage. Templates ready in seconds, VMs in under a minute.
Train at Scale
DeepSpeed and PyTorch DDP pre-configured for multi-GPU. Pause when idle, resume from checkpoint.
Frequently asked questions
Everything you need to know about renting the NVIDIA A100 80GB on Jarvislabs.
Fine-tune up to 70B parameters in FP16/BF16 on a single GPU. 4x A100 80GB (320GB) handles full fine-tuning with DeepSpeed ZeRO Stage 3. Common: LLaMA 3 70B, Mixtral 8x7B, SDXL training, RLHF/DPO.
Start training on the NVIDIA A100 80GB in seconds
$1.49/hr with per-minute billing. 80GB HBM2e. Up to 4 GPUs. No commitments.