Model reference · open weights

SuperApriel

Name: SuperApriel
Brand: AxForge

Available as managed deployment LLMs ServiceNow-AI Text gen 1 variants 126 dl/mo

SuperApriel is an open-weight language model from ServiceNow-AI. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

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Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Maker	ServiceNow-AI
Type	Language models
Task	Text gen
Runs with	transformers
Released	2026-04-07
Popularity	126 downloads / month
Licence	Open weights

About

What SuperApriel is

A 15B-parameter token-mixer supernet with 8 optimized deployment presets spanning 1.0× to 10.7× decode throughput at 32K sequence length, all from a single checkpoint. Derived from Apriel-1.6 through stochastic distillation and targeted supervised fine-tuning.

Model Size: 15B parameters
Layers: 48 decoder layers, each with 4 mixer variants
Context Length: 262K positions (runtime dependent)
Languages: English (best)

Highlights

Flexible deployment from a single checkpoint: multiple presets trading throughput for quality
Four mixer types per layer: Full Attention (FA), Sliding Window Attention (SWA), Gated DeltaNet (GDN), Kimi Delta Attention (KDA)
Instruction-tuned: targeted SFT with multiple Pareto-optimal placements
Speculative decoding support: use all-attention as target with efficient placements as drafts from the same checkpoint

See the report for detailed benchmarks, quality retention curves, and the full story.

Performance Overview

Pareto Frontier: Speedup vs Quality

Throughput Comparison

Deployment Presets

Each preset is a specific assignment of one mixer per layer. All presets share the same checkpoint—only mixer selection differs at inference time.

Preset	FA	SWA	KDA	GDN	Speedup @32k	Speedup @16k	Avg Acc.	Quality Retention
all-attention	48	0	0	0	1.0×	1.0×	74.2	100%
Reg\|Lklhd‑26	12	26	6	4	2.85×	1.5×	71.1	96%
Idealized\|All‑18	13	32	1	2	1.99×	1.1×	71.8	97%
Reg\|Lklhd‑18	3	25	4	16	4.76×	2.2×	69.7	94%
Idealized\|Lklhd‑6	0	30	5	13	6.2×	2.4×	66.8	90%
Idealized\|All‑6	0	30	5	13	6.13×	2.5×	65.3	88%
Reg\|Lklhd‑13	0	16	13	19	6.9×	2.7×	60.2	81%
Reg\|Lklhd‑10	0	10	5	33	10.69×	4.2×	57.2	77%

Benchmark Results

Full Results (S2: Targeted SFT with 8 Placements)

Config	@32k	AIME'24	AIME'25	MATH-500	GSM8K	FDA	SWDE	RULER	Tau2	MMLU-Pro	AIME(NV)	GPQA	HLE	LCB	IFBench	All
all-attention	1.0×	93.3	86.7	91.8	92.3	78.3	89.5	79.4	56.7	76.8	82.7	72.0	8.2	68.6	63.1	74.2
Reg\|Lklhd‑26	2.85×	86.7	83.3	92.0	91.1	79.9	88.2	74.4	30.7	76.2	80.0	70.7	10.0	69.2	63.1	71.1
Idealized\|All‑18	1.99×	90.0	86.7	92.0	92.1	78.0	86.6	67.1	52.6	76.3	82.2	68.2	6.9	67.3	58.8	71.8
Reg\|Lklhd‑18	4.76×	86.7	76.7	92.4	91.7	81.7	89.8	60.5	46.2	76.3	74.4	68.7	6.6	64.8	59.3	69.7
Idealized\|Lklhd‑6	6.2×	83.3	76.7	92.4	92.3	76.9	88.9	66.1	40.4	73.6	62.2	65.0	6.1	57.0	54.9	66.8
Idealized\|All‑6	6.13×	83.3	80.0	92.2	91.7	75.6	87.4	61.9	34.2	73.3	56.7	61.0	5.9	55.9	55.3	65.3
Reg\|Lklhd‑13	6.9×	76.7	73.3	90.4	91.2	68.6	85.1	57.0	28.6	69.3	26.7	61.2	5.5	52.6	57.1	60.2
Reg\|Lklhd‑10	10.69×	76.7	66.7	90.6	90.8	65.2	82.9	48.6	23.4	68.2	24.4	52.5	4.5	50.2	56.2	57.2

Comparison with Other Hybrid Models

Model	Speedup @32k	Math (Avg)	All Tasks
Super Apriel all-attention	1.0×	91.0	74.2
Super Apriel Reg\|Lklhd‑26	2.85×	88.3	71.1
Super Apriel Reg\|Lklhd‑18	4.76×	86.8	69.7
Super Apriel Idealized\|Lklhd‑6	6.2×	86.2	66.8
Apriel-H1 15B	1.97×	80.4	58.4
Nemotron-Nano 12B v2	5.85×	74.5	62.4
Falcon-H1R 7B	4.61×	78.6	64.9
Nemotron-3-Nano 30B	4.09×	89.0	72.6

Model Overview

SuperApriel-15b-Instruct is trained in two stages:

Stage 1 — Stochastic Distillation: All four mixer types trained simultaneously via distillation from frozen Apriel-1.6 teacher on 266B tokens. See SuperApriel-15b-Base.
Stage 2 — Targeted SFT: Supervised fine-tuning on 60B tokens with 8 Pareto-optimal placements identified via Bayesian placement optimization. Shared parameters (FFNs, embeddings, norms) remain frozen; only mixer weights are trained.

Architecture Details

Component	Details
Parameters	15B
Decoder layers	48
Query / KV heads	32 / 8 (grouped-query attention), d_h = 128
Hidden dimension	5,120
FFN width	14,336 (SiLU-gated)
Vocabulary	131,072 tokens
Vision encoder	Pixtral (16×16 patches)

Mixer Types

Mixer	Time	Memory	Description
Full Attention (FA)	O(n²)	O(n) KV cache	Standard grouped-query attention
Sliding Window (SWA)	O(w·n)	O(w)	Local window of 4,096 tokens
Gated DeltaNet (GDN)	O(n)	O(1) fixed state	Matrix-valued recurrent state with delta rule
Kimi Delta Attention (KDA)	O(n)	O(1) fixed state	Linear attention with channel-wise gating

How to Use

The recommended serving backend is vLLM with the Fast-LLM plugin, which supports preset selection and runtime switching. For simpler use cases, Transformers is also supported (see Use with Transformers below).

Use with vLLM

Preset selection and throughput-optimized serving require the vLLM plugin from Fast-LLM. Two serving modes are available:

Single-preset mode: Only the weights for the selected mixer placement are loaded (approx. 27 GiB in bf16). Unused mixer weights are never loaded, so the model fits comfortably on a single GPU wi

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys superapriel for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (superapriel 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":"superapriel","messages":[{"role":"user","content":"Hello"}]}'

Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.

Get API access

Sizes & precisions

SuperApriel-15B-Instruct

— · BF16

Weights ↗

Details