jrandolf4d ago

Show HN: sllm – Split a GPU node with other developers, unlimited tokens

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.

https://sllm.cloud

sllm

Shared LLM access via cohort subscriptions

Show threadkaoD

How is the time sharing handled? I assume if I submit a unit of work it will load to VRAM and then run (sharing time? how many work units can run in parallel?)

How large is a full context window in MiB and how long does it take to load the buffer? I.e. how many seconds should I expect my worst case wait time to take until I get my first token?

Apr 4 at 5:08pmWeb
Show threadninjha3d ago

> how many work units can run in parallel

not original author but batching is one very important trick to make inference efficient, you can reasonably do tens to low hundreds in parallel (depending on model size and gpu size) with very little performance overhead

Show threadjrandolf3d ago

vLLM handles GPU scheduling, not sllm. The model weights stay resident in VRAM permanently so there's no loading/unloading per request. vLLM uses continuous batching, so incoming requests are dynamically added to the running batch every decode step and the GPU is always working on multiple requests simultaneously. There is no "load to VRAM and run" per request; it's more like joining an already-running batch.

TTFT is under 2 seconds average. Worst case is 10-30s.