Ling-3.0 Tiny vs Flash: Specs, Hardware, and Evaluations

InclusionAI publishes two post-trained text models in its Ling-3.0 family: Ling-3.0-tiny and Ling-3.0-flash. They share a hybrid attention and sparse mixture-of-experts design, but their total size, active parameters, and deployment requirements differ. This guide uses the provider's model cards to compare the published facts and flags the limits of the associated benchmark claims.

The Ling-3.0-tiny model card lists 7.9 billion total parameters and 1.3 billion activated per token, an MIT license, and BF16, FP8, and INT4 weight formats. InclusionAI says it validated local deployment on DGX Spark and Apple Silicon hardware, with results that depend on the quantization, device, and context length. The model card's example FP8 measurements are vendor-reported; reproduce them on your own hardware before planning capacity.

The same card shows the official Hugging Face repository has no hosted Inference Provider deployment at the time of its current listing. The weights can be downloaded and run locally with compatible software, but that still uses storage, memory, power, and hardware. A local model is not a zero-cost hosted API.

InclusionAI reports an Artificial Analysis Intelligence Index v4.1.1 score of 25 and Agentic Index score of 16 for Tiny. It also reports over 160 output tokens per second in its Artificial Analysis evaluation, with about 18 seconds end-to-end latency for a 500-token response including reasoning. The model card is the source for these figures and gives the test context; treat them as a snapshot, not a guarantee for another runtime or workload.

The Ling-3.0-flash model card lists 124 billion total parameters and 5.1 billion activated per token. The model is released under MIT and exposes text-generation weights in several formats. Its published serving examples target multi-GPU deployments: the SGLang recipe describes four 141-GB-class GPUs or a four-GPU Blackwell node, with additional configurations for 80-GB H100/H800 hardware.

The model card documents a training context schedule up to 256K and evaluation methods for several coding, agentic, and reasoning tasks. Many listed results are produced with specialized harnesses, prompts, reasoning parsers, or task limits. The model card labels some agent benchmarks as internal and specifies different evaluators for others, so avoid reducing the table to a single overall rank.

For either model, create a small evaluation set from the tasks your users perform. Use the same prompts and tools across model versions, run enough trials to see variability, and score task completion separately from latency and token use. If a provider supplies the hosted inference, benchmark that full route rather than assuming the published local or lab setup transfers directly.