LFM2.5-24B-A2B-DSpark
LFM2.5-24B-A2B-DSpark is a speculative-decoding draft model for LiquidAI/LFM2.5-24B-A2B. It accelerates generation by proposing tokens that the target model verifies.
On NVIDIA B300 with the FP8 target in SGLang, output throughput averages 2.4× the target-only baseline at concurrency 1 (up to 3.1×), and 1.7× at concurrency 256.
Find more information about LFM2.5-DSpark in our blog post.
🗒️ Model Details
- Target model:
LiquidAI/LFM2.5-24B-A2B - Draft parameters: 327.7M (BF16)
- Backbone: 5 full attention layers,
hidden_size=2048,intermediate_size=6144with SiLU/SwiGLU, GQA withnum_attention_heads=32/num_key_value_heads=8,head_dim=64 - Extra heads: Markov head (rank 256) + confidence head
- Block size: 9
- Vocabulary: 128,000
Other models in the LFM2.5-DSpark family:
| Drafter | Target |
|---|---|
| LFM2.5-1.2B-Instruct-DSpark | LFM2.5-1.2B-Instruct |
| LFM2.5-2.6B-DSpark | LFM2.5-2.6B |
| LFM2.5-VL-3B-DSpark | LFM2.5-VL-3B |
| LFM2.5-8B-A1B-DSpark | LFM2.5-8B-A1B |
| LFM2.5-24B-A2B-DSpark | LFM2.5-24B-A2B |
📊 Performance
The target verifies every proposed token. Floating-point differences between batched verification and plain decoding can produce different text, including under greedy sampling. These measurements assess performance, not task accuracy. See LiquidAI/LFM2.5-24B-A2B for quality benchmarks.
Output throughput with DSpark relative to the target alone, on the full evaluation split of each dataset:
| Dataset | Tokens / step | Concurrency 1 | Concurrency 32 | Concurrency 256 |
|---|---|---|---|---|
| MATH-500 | 5.61 | 2.63× | 3.04× | 1.79× |
| GSM8K | 4.46 | 1.71× | 2.56× | 2.15× |
| HumanEval | 4.61 | 2.23× | 2.63× | 1.64× |
| MBPP | 6.70 | 3.08× | 3.28× | 1.64× |
| MT-Bench | 5.77 | 2.66× | 2.47× | 1.81× |
| SimpleQA | 5.22 | 2.48× | 2.91× | 1.95× |
| LiveCodeBench | 4.02 | 1.87× | 2.20× | 1.34× |
| AIME | 4.90 | 2.35× | 2.45× | 1.36× |
| Arithmetic mean | 5.16 | 2.38× | 2.69× | 1.71× |
Each measured shard uses one NVIDIA B300, the FP8 target, a BF16 KV cache, and temperature 0. Throughput is reported per GPU; sharded results pool output tokens and measured shard seconds. Tokens per step are measured at concurrency 1 and include the target's bonus token (the ceiling is 10). The raw results and full protocol are in benchmarks/.
🏃 How to run (SGLang)
Requires a recent SGLang build with LFM2-MoE DSpark support (PR #31041) and native Hugging Face configuration support (PR #43939). Launch the target with the draft attached:
python -m sglang.launch_server \
--model-path LiquidAI/LFM2.5-24B-A2B-FP8 \
--speculative-algorithm DSPARK \
--speculative-draft-model-path LiquidAI/LFM2.5-24B-A2B-DSpark \
--speculative-draft-attention-backend flashinfer \
--speculative-dspark-block-size 9 \
--speculative-num-draft-tokens 10 \
--mem-fraction-static 0.8 --port 30000
Then query the OpenAI-compatible endpoint at http://localhost:30000/v1. The baseline is the same command without the --speculative-* flags.
📬 Contact
- Got questions or want to connect? Join our Discord community
- If you are interested in custom solutions with edge deployment, please contact our sales team.
Citation
@article{liquidAI202624BA2B,
author = {Liquid AI},
title = {LFM2.5-24B-A2B: Built for Fast On-Device Agents},
journal = {Liquid AI Blog},
year = {2026},
note = {www.liquid.ai/blog/lfm2-5-24b-a2b},
}
@article{liquidAI2026dspark,
author = {Liquid AI},
title = {LFM2.5-DSpark: Up to 3.2x Faster Inference from H100 to MacBook},
journal = {Liquid AI Blog},
year = {2026},
note = {www.liquid.ai/blog/lfm2.5-dspark},
}
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