Migliori GPU Cloud con VRAM 288+ GB — June 2026

What “288 GB+ VRAM” actually filters for

The 288 GB threshold is not an arbitrary round number. It maps to a specific point on the hardware roadmap: the per-GPU memory capacity of NVIDIA’s latest Blackwell-class accelerators, where a single GPU package carries roughly 288 GB of HBM3e stacked memory. When you filter the comparison above to 288 GB or more of VRAM per device, you are deliberately excluding the previous-generation 80 GB and 141 GB cards and asking only for the densest single-GPU memory available for rent. That makes this a frontier-tier filter — the instances that surface here are among the most capable, and typically the most scarce and expensive, in any provider’s fleet.

Two things matter when reading this number. First, “288 GB” usually refers to memory per GPU, which is different from the aggregate memory of an 8-GPU node (where total HBM can run into the low terabytes). Second, this is High-Bandwidth Memory, not GDDR — the bandwidth that feeds those 288 GB is in the multi-terabyte-per-second range per GPU, which is the whole point of paying for it.

Why per-GPU memory at this scale matters

The reason large-model practitioners chase capacity rather than just raw FLOPS is that memory is what determines whether a model fits at all. With 288 GB on a single device, far more of a large model lives in one memory space without being split across GPUs, which changes the economics and engineering of a workload in concrete ways:

• Fewer GPUs per model — a model that previously needed tensor-parallel sharding across multiple 80 GB cards may now fit on one or two devices, cutting the communication overhead that sharding introduces.
• Larger KV caches for inference — long-context and high-concurrency serving is bottlenecked by the key/value cache, which grows with context length and batch size. More VRAM directly translates to longer context windows or more simultaneous requests per GPU.
• Bigger batches and less recomputation — training and fine-tuning can use larger micro-batches and keep more activations resident, reducing the need for gradient checkpointing and improving throughput.
• Headroom for full-precision or mixed-precision states — optimizer states, gradients, and master weights for very large models are memory-hungry; the extra capacity reduces how aggressively you must offload to CPU or NVMe.

This tier also pairs the capacity with modern tensor cores supporting FP16, BF16, and FP8 (and lower-precision formats for inference), so the memory is matched to compute that can actually exploit it for transformer workloads.

Interconnect is part of the spec

At 288 GB per GPU you are almost always renting nodes designed to scale beyond one device. The instances in the list above generally use high-bandwidth NVLink between GPUs inside a node and fast fabric (such as InfiniBand or equivalent) between nodes. That interconnect matters as much as the VRAM figure itself:

• For training and fine-tuning that still spans multiple GPUs, the speed of the GPU-to-GPU link sets how well your job scales — a fast NVLink mesh keeps all-reduce and all-gather operations from becoming the bottleneck.
• For multi-node jobs, the per-node fabric bandwidth and topology (and whether the provider exposes it cleanly) decide whether you get near-linear scaling or diminishing returns.
• For single-GPU inference that fits in 288 GB, interconnect matters less — but you are still paying for a node built around it, which is part of why this tier sits at the top of the cost spectrum.

Which workloads justify this tier

This filter is the right one when memory is genuinely your constraint. It genuinely fits:

• Training or fine-tuning very large models where parameter, gradient, and optimizer state simply will not fit on smaller cards.
• Serving frontier-size models for inference with long context windows or high request concurrency, where the KV cache dominates memory use.
• Memory-bound HPC and scientific workloads that process large working sets and benefit from HBM bandwidth.

It is overkill — and a waste of money — for small-to-mid model fine-tuning, most rendering pipelines, prototyping, and real-time inference of compact models that fit comfortably in 24-80 GB. If your model and its serving cache fit on a cheaper card with room to spare, paying for 288 GB buys you idle memory.

Rental and availability reality

Because this is the newest, densest memory tier, expect it to sit at the high end of the price spectrum and to be the most supply-constrained option in any fleet. Practical implications when comparing the instances above:

• On-demand capacity can be waitlisted or region-limited — frontier GPUs are released into specific regions first.
• Spot/interruptible discounts may be thin or unavailable for the newest hardware, because demand keeps utilization high.
• Per-second or per-minute billing matters more here than on cheap cards — at this tier, idle minutes are costly, so confirm billing granularity and whether you can pause or checkpoint cleanly.
• Verify the figure is per GPU versus per node before committing, and check that the listed VRAM is HBM, not a misreported aggregate.

For live per-hour pricing, exact regions, and current availability, use the comparison above rather than any fixed number quoted in prose — these move quickly at the frontier.

Frequently asked questions

Is 288 GB the memory per GPU or for the whole instance?

In this tier it almost always refers to memory per individual GPU. Multi-GPU nodes built from these accelerators can carry several times that in aggregate HBM, so always confirm whether a listing quotes per-device or per-node capacity before you size your workload.

What kind of memory is in a 288 GB GPU?

It is High-Bandwidth Memory (HBM3e), stacked next to the GPU die, delivering multi-terabyte-per-second bandwidth per device. That bandwidth, not just the capacity, is what makes this tier suited to large-model training and high-throughput inference rather than GDDR-based cards aimed at rendering or smaller models.

Do I need 288 GB per GPU or can I shard across smaller cards?

You can often shard a large model across several smaller GPUs using tensor or pipeline parallelism, but that adds communication overhead and engineering complexity. Fitting more of the model in one 288 GB device reduces or eliminates that sharding, which can simplify deployment and improve throughput — though it usually costs more per hour.

Why is availability for this tier so limited?

This is the newest, densest memory class, so supply is tight and demand is high. Providers roll it out region by region, on-demand slots can be waitlisted, and deep spot discounts are less common than on older hardware. Check the live list above for which regions currently have capacity.