Why pay $500K/month if you can pay $60K/month?
Open models like Kimi K3, GLM-5.2, DeepSeek-V4-Pro, and MiniMax M3 make private token factories practical. Break-even in months, not years.
#octopuscs#nvidia#tokenfactory#ai#onpremise#TCO
Enterprise AI can fail in five places: latency, configuration, data leakage, shadow AI, and missing fallback or monitoring. A working inference system needs more than a model. DM me to learn how we build token factories.
Qwen3 may beat Kimi K2.7 in practice when you have fewer GPUs and developers. Select for the workload: chat, code, RAG, or ETL. Evaluate speed, quality, memory, and throughput on real tasks, then size the GPUs.
Orgs that do not use LLMs will fade out. AI copies of old workflows are not the answer. Build new workflows with token factories. DM me if you want to learn more.
On-prem AI is not a model on a GPU. It is OpenShift, vLLM, LiteLLM, KV cache-aware routing, LMCache/KVBM, evolving open-source model selection, evals, and observability for context degradation and hangs. Platform > demo. #AI
One AI service cannot serve every team. Some need GPU VMs or containers. Data scientists need notebooks. AI teams may need BYOM serving. App teams may only need a governed LLM API. The value comes from clear service boundaries. #EnterpriseAI#PrivateAI
A lot of organizations ask me: Claude or private inference? Start with the workload. Data location decides whether cloud is possible. Then evaluate latency, model quality, utilization, and exit requirements before sizing DGX or HGX. #AI
While everyone is hyped about the Apple Event and the new products, I want to remind you that you donโt need a fancy MacBook to earn a great living online.
I built and scaled my freelancing business with a crappy laptop.
Donโt get distracted.
Itโs a luxury, not a necessity.