Three Open-Weight Releases Just Changed Self-Hosting. Here Is What Fits on a VPS.

September was the month open weights went mainstream. Xiaomi shipped MiMo-V2.6 (a 1T-parameter sparse MoE that, per Chat LLM's coverage, rivals closed frontier models on agentic reasoning), Nex AGI released Nex-N2.5 (open-weights models with real computer-use agency), and IFM published K2 Horizon — a fleet spanning 0.9B to 375B parameters designed to match models to hardware constraints.

If you self-host, this is the best news all year. But every release thread on Reddit ends with the same confused question: "can my box run it?" This guide is the honest answer, sized for real hardware — including ours.

MiMo-V2.6: frontier intelligence, datacenter footprint

The headline model is a 1T-parameter sparse Mixture-of-Experts. Sparse MoE means only a fraction of parameters activate per token — efficient per query, but the full weight set still has to live somewhere. That "somewhere" is multi-hundred-GB of fast memory: GPU-server or serious multi-node territory.

Our honest sizing: the full MiMo-V2.6 Pro is a colocation conversation, not a VPS one. What IS VPS-sized today: quantized and distilled builds that ship alongside frontier releases (check the model card for official quant variants), and — more practically — the full supporting stack around a self-hosted LLM: chat UI, vector database, agent harness. That stack is exactly what our AI VPS images pre-install.

Nex-N2.5: agency changes WHERE you run AI

Nex-N2.5's headline capability is computer use: perceiving a screen, moving a cursor, executing workflows across applications. Pause on what that means for hosting. An agent that operates a computer needs that computer to be: always on, isolated from anything it could damage, and private — because a screen-level agent sees everything on the screen.

That is the exact specification of a KVM VPS. Your agent runs on its own machine, snapshotted before each session (so a runaway workflow is a 60-second rollback, not a disaster), on hardware only you control. Computer-use agents on a shared SaaS runtime are a privacy compromise; on your own VPS they are simply an operations decision.

K2 Horizon: the fleet that proves our pricing page

IFM's K2 Horizon spans 0.9B to 375B parameters across six models, explicitly so engineers can "deploy the exact level of reasoning required for their hardware constraints." We could not have written a better justification for tiered infrastructure.

The small tiers (0.9B-class) fly on a 1-vCPU box. Mid-tier models in quantized form are comfortable at 4-8 vCPU with 16-32 GB. The 375B flagship again belongs on dedicated hardware. The point is not that one box runs everything — it is that you can START at $5.75/mo with the right-sized model and scale the box, not your bill, as your workload grows.

Your September self-hosting starter

Pick the stack that matches what you read today: Ollama + Open WebUI for local chat and model pulling, OpenHands for agentic computer-use experiments, Jupyter PyTorch for fine-tuning work, Qdrant for the retrieval layer under any of them. All 79 pre-built AI VPS images ship with the stack installed and boot in about 5-10 minutes.

• AI VPS from $5.75/mo — 79 stacks, Tampa FL (US-East)
• KVM isolation, full root, snapshots — the agent sandbox done right
• Scale 1-16 vCPU / 1-64 GB / 1 TB Ceph NVMe without reinstalling