We'll benchmark an Open weights LLM on any GPU you choose — drop your model + hardware and we'll run it. [D](reddit.com)

We run HexGrid Cloud, a platform for deploying open-source models on GPUs, and we're heads-down optimizing our serving/deployment layer. To pressure-test it we're benchmarking real models under real concurrency — and instead of guessing, we'd rather run what you actually want to see. --- Models available for benchmarking: Nemotron-3 Super 120B-A12B (only NVFP4) Nemotron-3 Nano 30B A3B Qwen-3.6 27B Llama 3.3 70B Instruct Gemma-4 31B Devstral-Small-2-24B-Instruct-2512 ?? (you suggest a model to us) We're focused on chat/instruct models for now (that's what most of our users deploy), so pick one from the list above — or suggest another open-weight chat model that fits on a single H200 (141GB). --- Hardware & quant choices: GPU (up to H200 for this round): RTX PRO 6000 · L40S · H100 · H200 Quant: FP8 / AWQ / BF16 Context length: (8K, 32K, 64K, 128K) What you want measured: max throughput? single-stream speed? long-context prefill? --- We'll run the top picks and post full results — tokens/sec, TTFT, TPOT, throughput under concurrency, and cost-per-million-tokens — config and flags included so it's reproducible. Let us know in comments. submitted by /u/Temporary-Owl1725 [link] [Kommentare]