Model reference · open weights
Ling-3.0-flash-dspark is an open-weight language model from inclusionAI, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Ling3-DSpark A DSpark speculator for Ling3. DSpark extends DFlash with target-model auxiliary features and a confidence head that dynamically chooses the number of draft tokens. The model was trained with SpecForge and is served with SGLang. Model specifications - Target model: Ling-3.0-flash - Draft parameters: 1,363,707,905 (1.36B) - Draft weight dtype: BF16 - Hidden size: 2,560 - Transformer layers: 5 full-attention layers - Attention: MHA with 32 query heads and 32 key/value heads - Target auxiliary feature layers: 1, 11, 23, 29, 35 - Confidence head: vanilla Markov head, rank 256 - DSpark block size: 8 draft tokens (verify width 9, including the target bonus token) - Maximum position embeddings: 262,144 Acceptance length Acceptance length is the mean number of tokens accepted per speculative verification step, including the target bonus token. The macro mean across the nine workload means is 5.29. Serving with SGLang Launch recipes for this draft on every supported hardware/quantization cell — including the required --linear-replayssm-cache-len sizing — with measured speed and accuracy, are in the SGLang Ling-3.0-flash cookbook. Use an SGLang version with DSPARK support. Replace the model paths and tensor-parallel size with values appropriate for your deployment: Serving with llama.cpp Use a llama.cpp build with DSpark support. Replace the model paths, quantization type, and GPU layer counts with values appropriate for your deployment. First convert and quantize the target model: Then generate the DSpark draft GGUF: Finally, launch the server with the DSpark draft as the speculative model:
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | inclusionAI |
|---|---|
| Type | Language models |
| Parameters (lead) | 1.4B |
| Context | 256k tokens |
| Variants | 1 |
| Runs with | transformers |
| Released | 2026-08-09 |
| Popularity | 2k downloads / month |
| Likes | 16 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| Ling-3.0-flash-dspark | 1.4B | BF16 | ~3.1 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys ling-3-0-flash-dspark for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ling-3-0-flash-dspark below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"ling-3-0-flash-dspark","messages":[{"role":"user","content":"Hello"}]}'
Details
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Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗