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Shanghai AI Laboratory · Text

InternLM models: Versions, specs, GPU requirements & hosting

InternLM provides an established open-weight language-model line with official checkpoints suitable for reference and private inference.

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Technical deployment reference illustration for InternLM
Reference deployment flow; exact architecture facts are listed separately.

Overview and history

InternLM is an official Shanghai AI Laboratory model family represented here through independently verified release, checkpoint, license, runtime and hardware evidence. InternLM provides an established open-weight language-model line with official checkpoints suitable for reference and private inference. Current public releases are grouped by the General tracks so sibling sizes and specialist branches are not presented as false chronological generations.

FusionPointAI currently tracks 1 certified public InternLM releases. The catalogue preserves release dates and evaluates Current, Recent, Previous and Legacy status within each model track rather than by database insertion order.

Official open checkpoints
Long-context tooling
Mature deployment path

Architecture and model range

The verified public range spans 8B to 8B total parameters and context windows up to 131,072 tokens. Each release states whether it is dense or mixture-of-experts and records active parameters separately when the official source publishes them.

Public releases1
Model tracks1
Current generationInternLM3 8B
Last verified2026-08-26T00:00:00.000000Z

Generations, tracks and variants

General

InternLM3

Legacy generation
InternLM3 8B

8.0B parameters · 131,072 context

Dense Dense Transformer

Deployment ecosystem

Choose a specific InternLM release and certified checkpoint before sizing hardware. FusionPointAI rejects unknown runtime configurations, calculates resident memory from total model weights, then distinguishes globally compatible GPUs from currently rentable inventory.

Validate the exact checkpoint license, runtime version, precision kernel, context length, concurrency and multi-GPU topology. Active MoE parameters affect compute, but resident model memory continues to use total stored weights.

Common use cases

Private assistants
Model evaluation
Research deployment

License overview

Licenses are certified per release, not inferred from the provider. Review the release page before commercial deployment because terms can differ between generations and variants.

Commercial-use status is a source-backed catalogue classification, not legal advice. Follow the linked official license and attribution requirements.

Release timeline

internlm/internlm/internlm3-8b

Source details

Official source

Source revision: 58930c5e0f2b0b2653149106bcb51a314d82a23711fe4dabdfd5d90f3cd33cb6

Frequently asked questions

Which InternLM release is current?

Current releases are The manufacturer has not published this information., evaluated within their respective tracks.

Can InternLM be self-hosted?

Only releases with a verified official checkpoint and supported runtime configuration are presented as deployable.

Which GPU should run InternLM?

GPU selection depends on the exact variant, checkpoint precision, context length and concurrency; use the release hardware recommendations rather than the family name alone.

Official references

Last verified: 2026-08-26