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Pain points in business: what people struggle with
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Self-Hosted LLM Serving
Teams struggle to run local LLMs that are fast, reliable, multi-user, and cost-effective without cloud dependence or expensive GPUs.
In their words
With Codex and Claude code limits getting nerfed heavily, I'm beginning to question when local inference begins to make sense.
requests are starting to queue, since the GPU serves us one at a time and the 4th person waits behind the rest
We generate large amounts of simple creative thats usually under 30 characters at scales of on average between hundreds of thousands to several million records daily
When I do some basic math on the RunPod pricing even with a low memory GPU, that is insane pricing.
I would probably save a lot of money if I was on the subscription only, by copy pasting the outputs from one LLM to another, but I would be spending my days doing that.
We spent 1/3rd of that on a local machine that can easily serve 1-2 users at a time, one time cost - electricity
I spent all day trying to just extract a few specific data fields from about 160 PowerPoints using these models and just ran into issue after issue.
It is so stupid and keeps breaking things that work and seems to be incapable of following the simplest instructions.
My bandwidth-bound math says two of these options wildly outperform a DGX Spark for the same money, but I know the calc is idealized
the moment 2-3 engineers resume sessions with large contexts (~200-300k), decode speeds absolutely tank to <10 tok/s for everyone else
N8n ingests from the cluster, hits my model running on my bare metal server, and then has a variety of tools
quando hanno provato a usare lo stesso modello per validazioni real-time, la latenza è salita a >120s: niente da fare
it's just too slow for live chat replies (which is a shame, as I'd really love to use it there)
Been dealing with inference costs creeping up on us for months. We route classification and extraction at volume - things that don't need GPT-5
Developers, IT teams, homelabbers
Where they talk
- r/LocalLLM
- r/LocalLLaMA
- r/hermesagent
- r/LLMDevs
- r/LLM
- r/LocalAIServers
- r/unsloth
- r/ollama
Tools they use today
- Qwen
- vLLM
- llama.cpp
- Ollama
- Claude
- ChatGPT
- DeepSeek
- Gemma
Why those fall short
- smaller models fall apart on complex tasks; tool calling buggy, blank responses
- slow decode makes iteration and course-correction impractical
- GLM 5.1 too huge for local, slow on API; Air not upgraded
- examples scattered, no structured per-hardware results
- llama.cpp too slow and OOMs; vLLM models too dumb to auto-config
Map of ideas
Drawing the map…