Dual A100 40GB vs H100 80GB: where to train LLMs?

Just last week, on June 3rd, 2026, we were staring down an LLM training job that needed 70GB of VRAM and a tight budget. The usual suspects offered either a pair of A100 40GBs or a single H100 80GB, with hourly rates that looked similar enough to be deceptive. The choice wasn’t just about raw dollars; it was about how much friction we were willing to absorb for the sake of a few percentage points of performance.

The llm training dilemma: a100 40gb x2 or h100 80gb x1?

This isn’t just an academic exercise. When you’re trying to fine-tune a Llama 3 70B model or pre-train something custom, the VRAM requirements quickly become the primary constraint. Seventy gigabytes of VRAM is a lot. The question then becomes: do you get that capacity by chaining together two slightly older, less expensive cards, or by paying a premium for a single, newer, more powerful one? Both approaches have their advocates, and frankly, both have their hidden gotchas. For LLM training, the devil is always in the details of memory locality, interconnect bandwidth, and raw compute cycles, not just the sticker price.

Nvidia a100 40gb: specifications and cloud pricing

The A100 40GB has been the workhorse of serious AI research for a few years now, and for good reason. It packs 40GB of HBM2 VRAM, which is ample for many mid-sized LLMs or for sharding larger ones. Performance-wise, a single A100 40GB typically delivers around 19.5 TFLOPS of FP32 precision and a solid 312 TFLOPS (TF32) for deep learning operations. The cards connect via NVLink, offering up to 600 GB/s of peer-to-peer bandwidth between GPUs in the same node. This is a critical factor for multi-GPU training, as it determines how fast gradients and model updates can be synchronized.

When it comes to cloud pricing, the A100 40GB remains a competitive option for many workloads. As of June 2026, here’s a snapshot of typical on-demand hourly rates from a couple of providers:

Provider	GPU Configuration	On-demand Hourly Rate (approx.)
Runpod	A100 40GB	~$1.39
Vultr	A100 40GB	~$1.49

Prices are vendor-published as of June 2026 for Secure Cloud on Runpod source and on-demand on Vultr source. For a broader look at what these cards cost in the wild, check out our recent dive into A100 cloud pricing. Two of these, then, typically set you back around $2.80 to $3.00 per hour, giving you 80GB of distributed VRAM and roughly 624 TFLOPS of TF32 compute.

Nvidia h100 80gb: specifications and cloud pricing

The H100 80GB, based on Nvidia’s newer Hopper architecture, is a different beast entirely. It boasts a full 80GB of faster HBM3 VRAM, which is a significant step up for memory-hungry LLMs. Raw compute power is also substantially higher, with a single H100 80GB pushing around 33.5 TFLOPS FP32 and an impressive 669 TFLOPS (TF32) for deep learning. The NVLink interconnect on the H100 is also upgraded to the 4th generation, providing up to 900 GB/s per GPU link, meaning even faster communication when multiple H100s are ganged together.

This card is designed for large-scale AI, and its pricing reflects that. While you get a lot more horsepower, you also pay a premium. Here are some representative on-demand hourly rates we tracked recently:

Provider	GPU Configuration	On-demand Hourly Rate (approx.)
Runpod	H100 80GB	~$2.49
Lambda Labs	H100 80GB	~$2.69

Prices are vendor-published as of June 2026 for Secure Cloud on Runpod source and on-demand on Lambda Labs source. For a detailed breakdown of the difference between the H100 40GB and its 80GB sibling, including how VRAM capacity impacts real-world LLM workloads, we’ve covered that extensively in our H100 40GB vs 80GB comparison.

Performance implications for llm training: vram, nvlink, and scaling

The raw specifications tell only part of the story; how these differences manifest in actual LLM training is where the rubber meets the road. The most immediate impact is on VRAM utilization. While two A100 40GBs give you 80GB total VRAM, it’s split across two physical devices. This means a model that requires, say, 70GB of unified VRAM (like a Llama 3 70B with larger batch sizes or sequence lengths) might struggle or necessitate complex model parallelism techniques to fit. A single H100 80GB, by contrast, offers that full 80GB in one contiguous block, simplifying memory management and often enabling larger models or batch sizes without sharding overhead.

NVLink bandwidth is another critical differentiator. While the A100’s 600 GB/s per GPU link is robust, the H100’s 900 GB/s per GPU link offers a substantial boost for inter-GPU communication. For data-parallel training (e.g., DistributedDataParallel in PyTorch), where copies of the model reside on each GPU and gradients are synchronized, this faster interconnect can reduce overhead and improve scaling efficiency. However, for true model parallelism, where layers or parts of a model are sharded across GPUs, the unified VRAM of a single H100 often outperforms distributed A100s, simply because the internal communication paths are more optimized and the latency between