SelfHostLLM
A simple calculator that estimates how many concurrent requests your GPU can handle for a given LLM, with shareable results.
SelfHostLLM launched on August 8, 2025 with 134 votes, #258 of 8,530 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Calculate GPU memory requirements and max concurrent requests for self-hosted LLM inference. Support for Llama, Qwen, DeepSeek, Mistral and more. Plan your AI infrastructure efficiently.
This memory is needed for each active request's attention cache. MoE models (like Mixtral, DeepSeek V3/R1, Qwen3 MoE, Kimi K2/K2.5, GLM-4.7) work differently: GPU memory bandwidth is the primary bottleneck for LLM inference. Higher bandwidth = faster tokenfrom selfhostllm.org
Does a similar job
all alternatives →- SSsllm – Split a GPU node with other developers, unlimited tokensApr 2026 · sllm.cloud · ▲188
Running DeepSeek V3 (685B) requires 8×H100 GPUs which is about $14k/month. Most developers only need 15-25 tok/s. sllm lets you join a cohort of developers sharing a dedicated node. You reserve a spot with your card, and nobody is charged until the cohort fills. Prices start at $5/mo for smaller models. The LLMs are completely private (we don't log any traffic). The API is OpenAI-compatible (we run vLLM), so you just swap the base URL. Currently offering a few models.
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