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

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.

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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).

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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?

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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.