Rent a Tesla V100 for legacy training and FP32 scientific compute. 32 GB HBM2 at 900 GB/s — pre-Hopper Transformers pipelines run unchanged, FP32 CFD and molecular dynamics workloads sustain ~14 TFLOPS, batch Whisper transcription at 9× realtime. Retired hyperscaler silicon, re-listed at attractive rates by hosts who picked up bulk inventory. Billed per-minute, paid in BTC, USDT/USDC or CLORE.
Tesla V100s are what you rent when minutes matter. FP8 cuts memory and doubles throughput on Transformer workloads; HBM2 keeps the SMs fed; NVLink lets eight cards train as one.
The only sub-$0.30 spot listing with HBM memory and NVLink support — meaningful when bandwidth-bound legacy code paths or FP32 scientific simulations need server-grade interconnect without paying A100 rates. Plenty of supply from hyperscalers retiring 2018-era inventory in 2026.
TensorRT-LLM, vLLM, SGLang — all tuned for Volta. Serve 70B FP8 from a single card with margin to spare for KV-cache.
32 GB HBM2 + FlashAttention-3 means 128k-token contexts run without offload. Ideal for long-doc RAG and agent loops with tool use.
Volta 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.
No sales call. No quota request. No three-week procurement. The first four commands are all you need.
Filter the marketplace by Tesla V100 80GB, country, GPU count, reliability score, network speed.
Choose a Docker image — PyTorch, vLLM, ComfyUI, Blender — or paste your own.
You get a public endpoint, an SSH key, and Jupyter on port 8888 in under 90 s.
Per-minute billing rounds to the second. Stop the instance and the meter stops with it.
Only for legacy code paths or budget-constrained FP32 scientific workloads. For transformer training, the A100 40GB is faster, has TF32, and isn't much more expensive. Pick V100 when the price gap matters more than throughput.
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.
32 GB HBM2 Volta — retired hyperscaler silicon priced for legacy training and FP32 scientific compute.
Pre-Hopper Transformers pipelines run unchanged — V100 is the cheapest card with HBM and NVLink support.
Read the guide →CFD / molecular dynamics workloads that depend on FP32 — V100 is the cheapest HBM card with full FP32.
Read the guide →32 GB HBM2 fits large-v3 + big batches — attractive for batch transcription where latency is not critical.
Read the guide →Side-by-side specs across the datacenter tier. Click any row to see that GPU.
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