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SkyForge · model catalog

Every model we serve, specified.

Thirteen curated open-weights models — GLM, Qwen, DeepSeek, Llama 4, MiniMax, Kimi, Gemma, Mistral — each pinned to a versioned template and served with vLLM behind your own private, OpenAI-compatible /v1 endpoint. The specs below are the ones the launcher actually enforces: architecture, served precision, max context, and the minimum GPU shape.

13 curated models · served with vLLM · one key, every endpoint · per-second billing

01Launch it

Two self-serve on-ramps.

Everything in the catalog launches on one of two self-serve shapes — a single RTX-class GPU or a multi-GPU DGX Spark node. Pick a model below; the launcher puts it on the right shape.

RTXSelf-serve

On-demand · per-second

Single-GPU instances for inference, fine-tuning, and dev work. Launch an open-weights model from a template and pay by the second.

DGX SparkSelf-serve

On-demand · per-second

Multi-GPU DGX Spark nodes for larger open-weights models, higher-throughput serving, and multi-GPU training — billed per second by shape.

02Models

Pick what you're building.

Choose a category to see the models tuned for it — architecture, served precision, max context, and the minimum GPU shape the launcher enforces. Every row launches in one click.

ModelArchitectureServedMax contextMin GPUs
GLM-4 9B
Lightest and cheapest — everyday chat and quick tasks.
9B densebf16128K1× 24 GB
Qwen3.6 27B
Higher-quality answers, still on a single GPU.
27B densebf16256K1× 80 GB
Qwen3.6 35B-A3B
Fast mixture-of-experts — big-model quality at single-GPU cost.
35B MoE · 3B actbf16256K1× 96 GB

Served with vLLM behind your own private, OpenAI-compatible /v1 endpoint.

03Bring your own

Your code, your weights, same meter.

The GPU Dev / JupyterLab template is a notebook workspace with PyTorch and CUDA — not an inference endpoint. Bring your own code, weights, and experiments instead of serving a catalog model, billed by the second like everything else.

Launch a notebook

04Open-weights families

The families we serve.

Every model in the catalog ships open weights and runs on the same OpenAI-compatible serving path. Pick the family you trust; run it without sending your prompts to a closed frontier lab.

01

GLM

GLM-4 9B · GLM 5.2Zhipu's GLM line — from a light single-GPU chat model to a 744B mixture-of-experts with a 1M-token context for coding and agents.

02

Qwen

Qwen3.6 27B · 35B-A3B · Qwen3.5 397B-A17BAlibaba's Qwen family, spanning a single-GPU dense model, an efficient MoE, and a large frontier MoE for demanding analysis.

03

DeepSeek

R1-Distill 32B · V4 Flash · V4 ProDeepSeek reasoning and mixture-of-experts models, from affordable single-GPU R1 distillation up to the largest model in the catalog.

04

Llama

Llama 4 ScoutMeta's Llama 4 Scout, a mixture-of-experts general assistant. Gated on Hugging Face — launch with your own token.

05

MiniMax

MiniMax M3A mixture-of-experts model built for very long-context reasoning, with a window up to 1M tokens.

06

Kimi

Kimi K2.7-CodeMoonshot's code-specialized frontier model for the heaviest coding and agentic work.

07

Gemma

Gemma 3 27BGoogle's Gemma 3 Instruct. Gated on Hugging Face — launch with your own token.

08

Mistral

Mistral Large 3Mistral's frontier mixture-of-experts model. Gated on Hugging Face — launch with your own token.

05FAQ

Questions, answered.

06Get started

Launch a model, or tell us what you need next.

Every model above launches from the console today — one private OpenAI-compatible endpoint and one key, billed by the second. Want a model or family we don't list yet? Get on the list and tell us what you're building.

Ask for a model

Tell us the model or family you want to run and we'll follow up.

What's the use case?