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flint-int4

RecursiveSI

MLX-format INT4 weights for Recursive Flint, primarily intended for local inference on Apple Silicon. Projections and experts use **group-64 affine INT4**; embeddings use INT8. The weights occupy approximately **22.6 GB**. Flint’s training data included publicly available and crawled web documents, text and code, commercially licensed data, and AI-generated synthetic data. Post-training included instruction tuning, reinforcement learning and human-preference data. See the base Flint model card for shared model and training information.

Text generation

Input Text→ Output Text

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flint-int4-gguf

RecursiveSI

**Q4_1 GGUF** weights for Recursive Flint, primarily intended for Apple Silicon using llama.cpp with Metal. The **26.0 GB** file includes weights, tokenizer, and chat template. See Flint’s model card for model details, benchmarks, chat conventions, and limitations. Follow llama.cpp’s Metal installation/build guide, using a release with Flint support, and put `llama-server` on your PATH. Use Metal GPU offload with `-ngl all`.

Text generation

Input Text→ Output Text

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flint-nvfp4-gguf

RecursiveSI

NVFP4 GGUF weights for Recursive Flint, primarily intended for NVIDIA GPUs using llama.cpp with CUDA. The **22.9 GB** file includes weights, tokenizer, and chat template. See Flint’s model card for model details, benchmarks, chat conventions, and limitations. Follow llama.cpp’s CUDA installation/build guide, using a release with Flint support, and put `llama-server` on your PATH. The runtime has been tested on NVIDIA B300, RTX PRO 6000 Blackwell, RTX Spark, and DGX Spark. We recommend at least **48 GB of GPU memory**, or unified memory on Spark.

Text generation

Input Text→ Output Text

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Explore AI models 3

flint-int4

flint-int4-gguf

flint-nvfp4-gguf

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