Dedicated model · Available as managed deployment
Z.ai's GLM-4.7-Flash — a 30B-class mixture-of-experts with a 200k-token context, tuned for fast agentic use and coding, released under MIT. Validated on AxForge hardware and deployed on a dedicated DGX Spark for your traffic only — an OpenAI-compatible endpoint on hardware only you use, operated by AxForge in the EU.
Why AxForge
| Flash means latency | A mixture-of-experts sized for speed: large-model answers at small-model latency, on a single dedicated machine. |
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| 200k context | Long documents, long transcripts and long agent runs in one request — 202,752 tokens of context. |
| MIT licence | No usage conditions, commercial use allowed, fine-tuning allowed. |
Specifications
| Model | GLM-4.7-Flash — zai-org |
|---|---|
| Modalities | Text |
| Sizes | 31.2B |
| Context window | 202,752 tokens |
| Licence | Open weights — mit; commercial use permitted |
| Hardware | NVIDIA DGX Spark (GB10, 128 GB unified memory) — owned and operated by AxForge |
| Rental term | Hour, week, month or year |
| Hardware pricing | €0.69/hour on demand · €0.66/hour by the week · €0.62/hour by the month · €0.55/hour by the year, excl. VAT |
| Managed service | Quoted per deployment |
| Region | Málaga, Spain (eu-es-1) |
Full details, benchmarks and FAQ on the GLM-4.7-Flash page. Prices exclude VAT.
How it works
| 1 | Request deployment — describe your traffic, context needs and rental term. |
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| 2 | You receive the configuration, hardware rental and managed-service price in writing before anything is billed. |
| 3 | AxForge deploys GLM-4.7-Flash on a dedicated DGX Spark reserved for you. |
| 4 | Point your OpenAI SDK at your own endpoint with the model name you receive. |
| 5 | Adjust the term — hour, week, month or year — as your workload settles. |
Request deployment or sign in to start.
FAQ
Not on the serverless API — it is available as a managed deployment: validated on AxForge hardware and deployed on a dedicated DGX Spark for your traffic only. The serverless API serves Qwen3.8 27B.
Yes — the 30B-class MoE runs on a dedicated DGX Spark; AxForge validates the build and the context length you need.
Agent workflows and coding at speed: tool calls, structured output and long context, where a fast MoE beats a slower dense model of similar quality.
AxForge publishes only numbers it measures itself, and has not benchmarked this model on its nodes yet. For quality benchmarks, see the official model card.
Hardware by the hour, week, month or year; the managed service is quoted per deployment — both confirmed in writing before anything is billed.