Twice the VRAM doesn't mean twice the performance — and in many cases, the 40GB version does everything you need at a lower price. But there are specific situations where the 80GB is the only option that works. Let's cut through the spec sheet and talk about what matters for your workload.
Quick rule of thumb: If your model fits in 40GB with room for batch processing, rent the 40GB ($1.20/hr on io.net). If you're running 13B+ models in full precision, doing full fine-tuning on 7B+ models, or need large batch sizes for throughput, go with the 80GB ($1.49/hr on io.net). The price difference is only $0.29/hr — about $209/month if running 24/7 — so when in doubt, get the 80GB.
The Specs That Actually Matter
| Spec | A100 40GB | A100 80GB | Why It Matters |
|---|---|---|---|
| VRAM | 40GB HBM2e | 80GB HBM2e | Determines max model size |
| Memory bandwidth | 1,555 GB/s | 2,039 GB/s | 31% faster — affects inference speed |
| NVLink bandwidth | 600 GB/s | 600 GB/s | Same — multi-GPU identical |
| FP16 TFLOPS | 312 | 312 | Same compute — training speed identical for same batch |
| TF32 TFLOPS | 156 | 156 | Same |
| Price (io.net) | $1.20/hr | $1.49/hr | 24% more expensive |
The compute is identical. The 80GB version has more memory and faster memory bandwidth. That's the entire difference. So the question is really: do you need the memory?
When 40GB Is Enough
You'd be surprised how much fits in 40GB:
Inference:
- Llama 3 8B in FP16: ~16GB — fits easily, room for batching
- Mistral 7B in FP16: ~14GB — plenty of headroom
- Stable Diffusion XL: ~7GB — massive batch headroom
- Any model under 20B parameters in 4-bit quantization
- Whisper large-v3: ~3GB — use 40GB and laugh
Fine-tuning (LoRA/QLoRA):
- 7B model LoRA: ~18-22GB — fits with standard configs
- 13B model QLoRA (4-bit base): ~24-28GB — tight but workable
- Any model under 7B with full fine-tuning
Training from scratch:
- Models up to ~3B parameters with standard batch sizes
A good heuristic: if your model weights (in the precision you're using) take less than 20GB, the 40GB card works. The remaining 20GB handles optimizer states, activations, and batch data.
When You Need 80GB
Here's where the 40GB runs out of room:
Large model inference without quantization:
- Llama 3 13B in FP16 needs ~26GB for weights alone. Add KV cache for batched inference, and you're at 35-45GB. The 40GB can do it with batch size 1, but production batching pushes past the limit.
- Llama 3 70B in 4-bit quantization needs ~35GB. It technically fits in 40GB but leaves almost no room for KV cache, making it impractical for real throughput.
Full fine-tuning of 7B+ models:
Full fine-tuning (not LoRA) stores the model, optimizer states (2x model size for AdamW), gradients, and activations. A 7B model in FP16 needs roughly 56-70GB during training:
- Model: 14GB
- Optimizer states: 28GB (2x for AdamW momentum + variance)
- Gradients: 14GB
- Activations: variable, 5-15GB depending on batch size
That's 61-71GB. The 80GB handles it; the 40GB does not.
Large batch inference for throughput:
Even with a model that fits in 40GB, if you're optimizing for tokens-per-second on an inference API, larger KV caches (more concurrent requests) mean more memory. The 80GB card can handle 3-4x more concurrent requests than the 40GB for the same model.
The Memory Bandwidth Angle
Here's something people overlook: the 80GB has 31% more memory bandwidth (2,039 vs 1,555 GB/s). For inference workloads — which are almost always memory-bandwidth-bound — this translates directly into faster generation.
Measured impact on Llama 3 8B inference:
- A100 40GB: 78 tokens/sec
- A100 80GB: 98 tokens/sec (26% faster)
That 26% speed boost means 26% more throughput per dollar for inference. At $1.49 vs $1.20, the 80GB costs 24% more but delivers 26% more inference throughput — making it slightly cheaper per token.
Decision Matrix
| Your Workload | Recommended | Why |
|---|---|---|
| 7B model inference | 40GB | Saves $0.29/hr, model fits easily |
| 7B model LoRA fine-tuning | 40GB | Standard LoRA fits in 22GB |
| 13B model inference (batched) | 80GB | KV cache pushes past 40GB |
| 7B model full fine-tuning | 80GB | Optimizer states need 60GB+ |
| 70B model (quantized) inference | 80GB | 35GB weights + KV cache |
| Stable Diffusion / image gen | 40GB | Models are small (3-7GB) |
| High-throughput inference API | 80GB | Bandwidth advantage matters |
| Embedding models at scale | 40GB | Tiny models, batch is king |
| Budget-constrained training | 40GB + gradient checkpointing | Trade speed for memory |
The Budget Play: 40GB + Gradient Checkpointing
If the 80GB is outside your budget and you need to fine-tune larger models, gradient checkpointing can fit workloads into the 40GB card at the cost of 20-30% slower training:
model.gradient_checkpointing_enable() # HuggingFace models
This recomputes activations during the backward pass instead of storing them, cutting activation memory by 60-80%. It won't help with optimizer state memory, but for many workloads it's the difference between "fits" and "doesn't fit."
Rent A100 GPUs on io.net — 40GB from $1.20/hr, 80GB from $1.49/hr. Choose your GPU
