--- title: "H200 rental in Uzbekistan: early access | UZ Dynamics" description: "NVIDIA H200: early access by request; availability, form factor pending. 141 GB HBM3e · 4.8 TB/s · NVLink. LLMs up to 70B in FP8 on one GPU (estimate)." url: "https://uzdynamics.ai/en/gpus/h200" lang: "en" image: "https://uzdynamics.ai/en/gpus/h200/opengraph-image/card?f08c4f789845417d" alternates: ru: "https://uzdynamics.ai/ru/gpus/h200" uz: "https://uzdynamics.ai/uz/gpus/h200" en: "https://uzdynamics.ai/en/gpus/h200" --- > For an index of this site's pages, see [llms.txt](https://uzdynamics.ai/llms.txt). A markdown copy of each page is at its address with `.md` appended. # NVIDIA H200 rental: early access Hopper · SXM (being confirmed) We’re collecting early-access requests. We’re placing our capacity in Uzbekistan. We don’t publish prices yet. We’ll confirm availability in our reply. H200 [Request H200](https://uzdynamics.ai/en/request?source=gpu-page%3Ah200&gpu=h200) We’ll reply with terms for your workload. Nothing to pay before the contract. - **Memory per GPU:** 141 GB HBM3e - **Memory bandwidth:** 4.8 TB/s - **GPU interconnect:** NVLink 900 GB/s H200 diagram: 141 GB HBM3e; NVLink 900 GB/s Simplified diagram, not to scale ## NVIDIA H200 specifications - **Architecture:** Hopper - **Announced:** 2023 - **Memory:** 141 GB - **Memory type:** HBM3e - **Memory bandwidth:** 4.8 TB/s - **FP32:** 67 TFLOPS - **FP16/BF16, dense:** 989.4 TFLOPS - **FP8, dense:** 1,978.9 TFLOPS Dense FP16/BF16 and FP8 from the H100 SXM5 column of NVIDIA’s whitepaper: for the H200, NVIDIA lists only with-sparsity figures, the same as the H100 SXM’s. - **FP4, dense:** none - **FP64:** 34 TFLOPS - **FP64 Tensor Core:** 67 TFLOPS - **RT cores:** no - **NVENC/NVDEC:** NVENC 0, NVDEC 7 NVENC from the H100 row of NVIDIA’s encode and decode matrix, which has no H200 row; the H200 page lists decoders only. - **MIG:** up to 7 instances - **GPU interconnect:** NVLink 900 GB/s - **Power (TDP/TGP):** up to 700 W - **Form factor:** SXM (being confirmed) Memory is specified per GPU. Multiple GPUs have separate memory: using a larger model across them requires compatible software and model partitioning. NVLink does not make them one GPU. Other variants: [H200 NVL](https://www.nvidia.com/en-us/data-center/h200/) The H200 form factor is still being confirmed. Specs from NVIDIA’s official materials, checked 26 September 2026. ## The H200 by workload - Inference: LLMs up to 70B in FP8 on one GPU - Fine-tuning: QLoRA up to 123B, LoRA up to 32B on one GPU - Training from scratch: models up to 3B on one GPU - Rendering: no RT cores - FP64 computing: 34 TFLOPS, Tensor Core 67 TFLOPS An estimate from open formulas, not a benchmark or an invoice. ## Servers with the NVIDIA H200 Server configuration (CPU, RAM, disk, network) is being confirmed. Sizing assumption: up to 8 GPUs per server. We’ll confirm the actual configuration in our reply. [More than 8 GPUs? Discuss a cluster](https://uzdynamics.ai/en/enterprise?source=gpu-page-cluster%3Ah200&gpu=h200&count=more) ## Sources - [NVIDIA H200](https://www.nvidia.com/en-us/data-center/h200/) - [NVIDIA H100 Tensor Core GPU Architecture (whitepaper V1.04)](https://dam-cdn.nvd.orangelogic.com/AssetLink/705n6ur546g0uk43w0117r17n8042d73.pdf) - [NVIDIA Video Encode and Decode Support Matrix](https://developer.nvidia.com/video-encode-decode-support-matrix) - [NVIDIA Omniverse Developer Guide: Technical Requirements](https://docs.omniverse.nvidia.com/dev-guide/latest/common/technical-requirements.html) - [NVIDIA Newsroom, November 13, 2023](https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform) ## Similar GPUs - [NVIDIA H100](https://uzdynamics.ai/en/gpus/h100) 80 GB HBM3 · 3.35 TB/s · NVLink H200 leads on 2 of 7 metrics. H200 has more memory (141 vs 80 GB) and memory bandwidth (4.8 vs 3.35 TB/s). Equal: FP32, FP16/BF16, FP8, FP64 Tensor Core, NVLink. - [NVIDIA B200](https://uzdynamics.ai/en/gpus/b200) 180 GB HBM3e · 7.7 TB/s · NVLink B200 leads on 6 of 7 metrics, H200 on 1. B200 has more FP16/BF16 (2,250 vs 989.4 TFLOPS) and FP8 (4,500 vs 1,978.9 TFLOPS). But H200 has more FP64 Tensor Core throughput (67 vs 37 TFLOPS). Without Tensor Cores, B200 has more FP64: 37 vs 34 TFLOPS. - [NVIDIA RTX PRO 6000 Blackwell](https://uzdynamics.ai/en/gpus/rtx-pro-6000) 96 GB GDDR7 · 1.6 TB/s · PCIe 5 H200 leads on 4 of 5 metrics, RTX PRO 6000 on 1. H200 has more memory bandwidth (4.8 vs 1.6 TB/s) and FP8 (1,978.9 vs 935.6 TFLOPS). But RTX PRO 6000 has more FP32 (120 vs 67 TFLOPS). FP64: 34 TFLOPS on H200; not in NVIDIA’s card specs for RTX PRO 6000 (NVIDIA’s whitepaper gives 1/64 of FP32 for the GB202 chip). Dense FP16/BF16 and FP8 from the H100 SXM5 column of NVIDIA’s whitepaper: for the H200, NVIDIA lists only with-sparsity figures, the same as the H100 SXM’s. The RTX PRO 6000’s FP16, FP8 and FP4 are derived figures. Derived: NVIDIA’s whitepaper per-SM Tensor Core rate × the 2,430 MHz boost clock in the product brief. For the Server Edition, NVIDIA gives FP16/FP8/FP4 only as rounded 1/2/4 PFLOPS peaks, the level of sparse figures; it publishes no dense ones. ## All GPUs in the catalogue - [B300](https://uzdynamics.ai/en/gpus/b300) - [B200](https://uzdynamics.ai/en/gpus/b200) - [H100](https://uzdynamics.ai/en/gpus/h100) - [A100](https://uzdynamics.ai/en/gpus/a100) - [RTX PRO 6000](https://uzdynamics.ai/en/gpus/rtx-pro-6000) - [L40S](https://uzdynamics.ai/en/gpus/l40s) - [L4](https://uzdynamics.ai/en/gpus/l4) - [RTX 6000 Ada](https://uzdynamics.ai/en/gpus/rtx-6000-ada) - [RTX A6000](https://uzdynamics.ai/en/gpus/rtx-a6000) - [RTX A5000](https://uzdynamics.ai/en/gpus/rtx-a5000) [Request H200](https://uzdynamics.ai/en/request?source=gpu-page-end%3Ah200&gpu=h200) We’ll reply with terms for your workload. 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