LLM customization, simplified.

PulseBench is currently in private beta. You can request access through the form below. We'll review your application and schedule an introductory call to discuss your use case, infrastructure, and timeline.

PulseBench is an on-premises platform for fine-tuning and deploying open-source large language models. It's built for teams that want to experiment with or productionize LLMs on their own infrastructure, particularly in environments where data sensitivity prevents sharing information with external providers.

PulseBench doesn't provide compute infrastructure - it deploys onto your existing environment. If you own GPUs (NVIDIA A100, H100, or similar), we configure PulseBench to run directly on them. Minimum practical setup is a single node with 80GB VRAM for 7-8B parameter models; multi-node clusters for 70B+ models. If you don't have dedicated hardware, we can help you evaluate GPU cloud options. We walk through the right setup for your model and dataset size during onboarding.

At minimum, you'll need a dataset of labeled examples for supervised fine-tuning. If your approach involves RLHF, we can discuss reward model setup during onboarding. Our team can assist with dataset preparation and structuring if needed. We recommend starting with anonymized or de-identified data during initial experimentation - it reduces compliance friction while you validate your approach.

We currently support 22+ models across five open-source providers: Qwen, Meta Llama, GPT-OSS, DeepSeek, and Moonshot (Kimi). You can browse the full list of supported models above, with direct links to each model on HuggingFace. New models are added regularly based on community demand and enterprise requests.

If the model is commercial or proprietary (e.g., GPT-4, Claude, Gemini), it isn't supported - PulseBench only works with open-source models that can run on your infrastructure. If the model is open-source and text-based but not listed, contact us. Adding support is usually straightforward and may require minimal configuration. Note that we currently focus on text and vision-language LLMs and don't support image generation models (e.g., Stable Diffusion) or video models.

We don't. PulseBench is deployed entirely on your premises. Your data, your models, your weights - none of it passes through our servers or any third-party infrastructure. There's no telemetry, no usage analytics sent externally, and no data access from our side. That's exactly why teams in regulated industries choose us.

PulseBench is a subscription-based platform. We typically start with a paid pilot to validate the fit for your environment, then discuss longer-term options based on your team size and usage. We'll walk you through pricing on our first call.