Rent an A100 80GB for the cheapest HBM + NVLink path in 2026. 80 GB HBM2e at 1,935 GB/s — pretrain 7B–13B from scratch on 8-GPU nodes at ~3,800 tok/s per card, fine-tune 70B with FSDP across 16 cards, serve 70B FP16 across two cards via tensor parallel. Standard FSDP + DeepSpeed pipelines run unmodified on this silicon. Billed per-minute, paid in BTC, USDT/USDC or CLORE.
A100 80GBs are what you rent when minutes matter. FP8 cuts memory and doubles throughput on Transformer workloads; HBM2e keeps the SMs fed; NVLink lets eight cards train as one.
When you need 80 GB of HBM and NVLink fabric for 70B FSDP fine-tuning or two-card 70B FP16 serving, A100 80GB is typically 50–60% the rental of an H100 with the same FSDP + DeepSpeed pipelines unchanged. The default training card for budget-conscious ML teams.
TensorRT-LLM, vLLM, SGLang — all tuned for Ampere. Serve 70B FP8 from a single card with margin to spare for KV-cache.
80 GB HBM2e + FlashAttention-3 means 128k-token contexts run without offload. Ideal for long-doc RAG and agent loops with tool use.
Ampere is the architecture the 70B and 405B foundation models were trained on. Specs from Nvidia's SXM5 datasheet; pricing reflects the lowest live on-demand floor.
// prices are spot-market lows · refreshed every 60 s
Every server is priced by its host. These are the live floors across the marketplace — you'll see hundreds of variants once you're in.
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Filter the marketplace by A100 80GB 80GB, country, GPU count, reliability score, network speed.
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Yes — it's typically 50–60% of H100 rental price with 80 GB HBM2e and supports the same FSDP + DeepSpeed pipelines. For training without FP8 / TransformerEngine, A100 80GB remains the cheapest way to get HBM and NVLink in 2026.
Single-card, no - 70B pretraining needs an 8-GPU node minimum. CLORE.AI lists 8x H100, 8x H200, and 8x B200 pods with NVLink fabric for exactly this. A100 80GB pods run 70B FSDP training but at lower throughput than Hopper-class. For multi-week training, contact host operators for reserved-instance terms - listed in the marketplace under 'Reserved'.
A100 80GB has no FP8 - peak is BF16/TF32. H100 introduces FP8 with TransformerEngine and roughly 4x the BF16 training throughput at 2x the rental price - so ~2x perf-per-dollar on FP8-eligible workloads. H200 matches H100 compute but adds 141 GB HBM3e. B200 doubles H100 FP8 again with 192 GB HBM3e. Pick by VRAM and bandwidth ceiling, not just sticker FLOPS.
8-GPU H100 SXM, H200 SXM, and B200 nodes ship with NVSwitch fabric - 900 GB/s peer bandwidth on H100/H200, 1.8 TB/s 5th-gen NVLink on B200. PCIe variants (H100 PCIe, A100 PCIe) have NVLink Bridge in pairs only. Multi-node fabric (NVLink-Switch across racks) is available on B200 hyperscale pods - filter by 'NVSwitch' in the marketplace.
Yes. Multi-GPU listings expose all cards in a single rental as a coherent node with NVSwitch (where present), shared NVMe scratch, and InfiniBand or 100 GbE fabric for multi-node training. The standard PyTorch torchrun, DeepSpeed, and Megatron-LM launchers run unmodified. Filter the marketplace by GPU count to find 8x A100, 8x H100, 8x H200 nodes.
V100 (HBM2, 900 GB/s) -> A100 40GB (HBM2e, 1,555 GB/s) -> A100 80GB (HBM2e, 1,935 GB/s) -> H100 (HBM3, 3,350 GB/s) -> H200 (HBM3e, 4,800 GB/s) -> B200 (HBM3e, 8,000 GB/s). Each generation roughly doubles bandwidth or VRAM; KV-cache-bound serving and bandwidth-bound training scale almost linearly with this number.
80 GB HBM2e + 1.93 TB/s — the 2022–2024 training silicon and still the cheapest HBM + NVLink path in 2026.
8× A100 80GB pod is the de facto reference for 7B-13B pretraining — ~50% the rental of an H100 node.
Read the guide →70B SFT across 16 cards (2 nodes via NVLink + IB) — standard reference for 70B fine-tunes in 2026.
Read the guide →Two 80 GB A100s fit 70B FP16 + 16-request KV cache — the cheapest FP16 70B serving setup with NVLink.
Read the guide →Side-by-side specs across the datacenter tier. Click any row to see that GPU.
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