Облачные GPU с пропускной способностью памяти свыше 5 ТБ/с — June 2026

What “5 TB/s and up” actually means in memory bandwidth

Filtering for a minimum memory bandwidth of 5,000 GB/s (5 TB/s) draws a hard line under the GPU market: it excludes essentially every GDDR-based card and most older datacenter parts, leaving only modern accelerators built on stacked High Bandwidth Memory (HBM). Memory bandwidth is the rate at which a GPU can move data between its on-package memory and its compute cores. For large-model AI and many HPC kernels, this number — not raw FLOPS — is the real ceiling on throughput, because the chip spends most of its time waiting on weights and activations rather than doing math.

To put the threshold in context, mainstream gaming and workstation cards using GDDR6 or GDDR6X typically land in the hundreds of GB/s up to roughly 1 TB/s. The first datacenter HBM parts pushed past 1.5–2 TB/s. Crossing 5 TB/s means you are looking at the current generation of HBM3 and HBM3E accelerators, where the memory stacks and ultra-wide buses deliver several times the bandwidth of anything in the consumer tier. In other words, this facet is a proxy for “newest-generation, top-of-stack training silicon.”

What hardware clears the 5 TB/s bar

Cards that meet or exceed 5 TB/s of memory bandwidth share a recognizable profile, and the comparison above reflects that class:

• HBM3 or HBM3E memory stacked directly on the GPU package, rather than GDDR soldered around the board, which is what makes multi-terabyte-per-second figures physically possible.
• Large VRAM capacity, commonly in the range of tens of gigabytes to well over a hundred GB per GPU, so high bandwidth is paired with enough room to hold large model shards and big activation batches.
• Modern tensor/matrix engines supporting low-precision formats such as FP16, BF16, FP8 and INT8, which let you trade precision for throughput on both training and inference.
• High-speed interconnect — typically a vendor fabric (NVLink-class on NVIDIA, Infinity Fabric on AMD) plus fast host links — so multiple of these GPUs can be pooled into one logical accelerator for models too large for a single card.
• A high power and thermal class, generally several hundred watts per GPU, which is why they live in dense, well-cooled cloud racks rather than desktops.

Because these are flagship parts, they sit at the premium end of the rental cost spectrum. They are the most expensive GPUs you can rent per hour, and they are also the most supply-constrained: on-demand capacity can be scarce during peak demand, and spot or interruptible pools — where available — fluctuate. Use the table above for live pricing and availability, since both move frequently and differ by provider and region.

Workloads that justify 5 TB/s+

This much bandwidth is genuinely necessary for a specific set of jobs, and overkill for others. It earns its keep when the bottleneck is data movement at scale:

• Pretraining and large-scale fine-tuning of multi-billion-parameter language and multimodal models, where every step streams enormous weight and gradient tensors.
• High-throughput, large-context LLM inference and batch serving, where decode speed is dominated by how fast weights can be read from memory each token.
• Memory-bound HPC and scientific kernels — large sparse solvers, CFD, molecular dynamics, FFT-heavy pipelines — that are limited by bandwidth rather than arithmetic.
• Multi-GPU sharded models (tensor or pipeline parallel) where slow per-GPU memory would stall the whole cluster.

By contrast, if you are running small-model inference, classical rendering, light fine-tuning of compact models, or development and prototyping, paying for 5 TB/s+ silicon is usually wasted money — a mid-tier GDDR or lower-bandwidth HBM card will keep up and rent for far less. The honest test is whether your job is memory-bandwidth-bound; if profiling shows your GPU compute units sitting idle waiting on memory, this tier pays off, and if they are already saturated, it will not.

What to check before you rent at this tier

Bandwidth alone does not guarantee performance. When comparing entries that clear the threshold, look beyond the headline number:

• VRAM per GPU and per node — high bandwidth with too little capacity still forces sharding or offloading, which can erase the speed advantage.
• Interconnect quality and topology — whether GPUs in a node are linked by a full-bandwidth fabric or merely PCIe, and whether multi-node training has fast networking such as InfiniBand or high-rate Ethernet.
• Billing granularity and interruption policy — per-second versus per-hour billing, and whether spot capacity can be reclaimed mid-job, both materially change the effective cost of these expensive instances.
• Storage and egress — fast local NVMe to feed the GPUs, plus realistic data-egress terms if you move large checkpoints out.

Frequently asked questions

Why does a 5,000 GB/s minimum filter out most GPUs?

Because 5 TB/s is only achievable with stacked HBM3/HBM3E memory and very wide buses. GDDR-based consumer and workstation cards top out around 1 TB/s, and even earlier datacenter HBM parts sit below 5 TB/s, so this threshold isolates the newest top-tier accelerators.

Is higher memory bandwidth always faster for my workload?

Only if your workload is memory-bound. Large-model training and high-throughput LLM inference usually are, so they benefit directly. Compute-bound or small-model jobs may see little gain, meaning you would pay a premium for bandwidth you never use.

Will 5 TB/s+ GPUs cost more to rent?

Yes. These are flagship, supply-constrained parts and sit at the top of the rental cost spectrum. Exact rates vary by provider, region, and on-demand versus spot availability, so check the live comparison above rather than budgeting from a fixed figure.

How does bandwidth relate to VRAM capacity at this tier?

They are separate specs that work together. Bandwidth is how fast memory is read or written; capacity is how much fits. A 5 TB/s GPU with too little VRAM still forces you to shard a large model across cards, so confirm both numbers — and the interconnect linking the cards — before committing.