Open-weight models are dominating the AI conversation right now like never before. In July, r/LocalLLaMA published a major architecture survey covering 23 models between 20B and 500B parameters — a sign of just how dizzying the catalog has become in just a few months.
At the same time, a very different debate is playing out over the underlying principle behind open models, after Anthropic's official stance on open weights was published and met with both criticism and defense across the community.
The two discussions are connected in a way that rarely gets said out loud: the more open models we get, the more important the question becomes of who makes sure they're run responsibly.
The model map is about architecture, not just size
The most valuable takeaway from the survey thread isn't really the list of models — it's the recognition that architecture matters more than parameter count. A MoE model with 235B parameters can have fewer active parameters than a dense 30B model at inference time, which upends most of our intuitions about "size."
A genuinely useful overview should therefore distinguish between architecture types rather than simply sorting by number of zeros:
| Model | Architecture | Size | Approximate VRAM requirement* | License |
|---|---|---|---|---|
| Mistral Shieldstral | Dense vision/text classifier | 3B | ~16 GB | Apache 2.0 |
| Gemma 4 9B | Dense text generation | 9B | ~20 GB | Gemma Terms |
| Gemma 4 27B/31B | Dense text generation | 27-31B | 60-80 GB | Gemma Terms |
| Kimi K3 | MoE generation (2.8B active) | ~40-80 GB** | Custom open | |
| Llama 4 Scout | MoE generation (17B x 16 experts) | ~40-80 GB** | Llama 4 license | |
| Llama 4 Maverick | MoE generation (400B total) | ~200 GB+** | Llama 4 license | |
| Qwen3-235B-A22B | MoE generation (22B active) | ~80-120 GB** | Qwen license | |
| GLM-5.2 | MoE generation (744B total, 1M context) | ~200 GB+** | Custom open | |
| DeepSeek-V3.1 | MoE generation (671B total) | ~200 GB+** | DeepSeek license |
* Approximate VRAM at FP16/quantization; context length and inference engine significantly affect real-world usage. ** MoE models typically only load active experts at inference time; overhead varies widely by implementation.
Another point from the community deserves attention: the difference between genuinely open models and those that merely call themselves open without releasing the weights — the community has suggested tagging the latter [no weights]. When choosing a model for self-hosting, this is the first thing to check: can you actually download the weights?
From choosing a model to the question of responsibility
That brings us to the second debate thread — and here the most interesting observation is buried inside the criticism of Anthropic's position: you can declare yourself against a ban while simultaneously setting requirements that make one impossible in practice. That kind of regulation quickly becomes a de facto ban delivered by a thousand cuts rather than one dramatic no.
There's also a logical tension worth holding onto: how do you reconcile an anti-distillation stance with training on enormous amounts of data yourself? That kind of inconsistency undermines trust regardless of the substance of the argument.
But buried in the criticism is also a point self-hosters should take seriously: an open model has zero built-in safety mechanisms. You can't design misuse out of a downloadable weights file — so responsibility inevitably shifts to whoever actually deploys the model.
And this is where hosting location suddenly becomes strategic rather than merely technical: if cloud providers can potentially be compelled not to allow certain open models on their infrastructure, then where your workload runs becomes just as important as what it runs on.
Managed self-hosting as the middle ground
That ties the threads together neatly into one argument:
Open weights grant freedom — but that freedom comes with three concrete requirements:
- You need to control where the model runs (jurisdiction)
- You need to manage access to input/output yourself
- You need to build your own guardrails, since no filter comes included
That's the essence of managed self-hosting at Liviate Cloud: you retain ownership of the model and its data, while isolation, egress control, and operations sit within a professional, EU-hosted framework — so you get the freedom of openness without inheriting all the risk alone.
Two debates are dominating right now: one about which model to choose, the other about whether openness is even safe enough in the first place. On the surface they don't have much in common — but they converge in one place: with the person or company that actually has to host the model responsibly.
Choosing a model is only the first step; the architecture dictates the hardware you'll need, the [no weights] test sets the boundaries of what you can actually download — and the debate about responsibility defines how securely the environment around the model needs to be built.