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.

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.