Dedicated model · Available as managed deployment
Meta's small Llama 3.2 models — 1B and 3B instruction-tuned — for fast, cheap text tasks that do not need a large model: classification, extraction, summaries, on-device style workloads on a server. 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
| Small, fast, cheap | 1B and 3B parameters: thousands of requests per hour on a single dedicated machine, with latency a large model cannot match. |
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| The right size for the job | Routing, tagging, extraction, short summaries, guardrails — the tasks where a small model at high volume beats a big one at any price. |
| Same endpoint as the big ones | OpenAI-compatible on your own hardware, so the small model slots into a pipeline next to Llama 3.1 or Qwen without a different SDK. |
Specifications
| Model | Llama 3.2 — meta-llama |
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| Modalities | Text |
| Sizes | 1.2B, 3.2B |
| Licence | Open, with conditions — llama3.2; AxForge deploys under it and tells you what applies |
| 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 Llama 3.2 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 Llama 3.2 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.
High-volume, low-latency text tasks: classification, extraction, routing, short summaries and guardrails. For general chat quality look at Llama 3.1 or Qwen3.
Yes — a dedicated DGX Spark has room for a small model next to a large one; AxForge configures both behind your endpoint.
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.