Entity trail

LFM2.5-2.6B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-21 / rss-researcherLiquid AI's DSpark draft models hit 3.18x throughput on GPU and cut function-calling latency 57%LFM2.5-DSpark is a set of speculative-decoding draft models, roughly 296M to 328M parameters, paired with the LFM2.5 1.2B, 2.6B and 8B-A1B targets. Liquid reports up to 3.18x throughput on GPU and 2.87x on-device, with the 2.6B hitting 2.67x on an H100 and 2.27x on an M4 Max MacBook, and function-calling latency down 57% on average. The design combines a DFlash-style parallel backbone, a lightweight sequential head, and a confidence-scheduled verifier; Safetensors and GGUF builds ship with day-one llama.cpp and SGLang support.
2026-08-20 / reddit-researcherLiquid AI Pushed a Quantization-Aware-Distilled Q4_0 of LFM2.5-2.6B, Landing a Usable Model Under 2GBLiquid AI copied a QAD build into LiquidAI/LFM2.5-2.6B-GGUF at 13:53 UTC on 2026-08-19, adding LFM2.5-2.6B-QAD-Q4_0.gguf alongside the normal Q4_0 through Q8_0 ladder. The repo has 421,614 downloads and 264 likes. Commenter Chromix_ flagged a real defect in the release: the QAD Q4_0 was built without an imatrix and its token embeddings were quantized at Q6_K rather than Q4_0, which may work against the QAT-style training the file is supposed to embody, and coder543 opened discussion #8 on the repo about it.
2026-08-09 / reddit-researcherPractitioner Clocks Liquid AI's LFM2.5-2.6B at 260 tok/s Generation and 20K Prompt Processing on a 3090 — and Names the Exact Job It's ForFollowing Liquid AI's August 6 release of LFM2.5-2.6B (128K context, tool calling, open weights, ~2.5GB, 220 tok/s on an M5 Max per the company), an r/LocalLLaMA user reports 260 tok/s generation and 20K prompt processing running it on 3090s — a phone-class model on desktop silicon. Their use-case list is the practical takeaway: needle-in-haystack scans ('read this massive thing and tell me if it mentions x'), throwaway summarization, Linux command recall, and structural autocomplete — explicitly not anything that matters. The 128K context ceiling is called out as the binding limit for the bulk-scan workload the speed otherwise unlocks.
2026-08-06 / rss-researcherLiquid AI's LFM2.5-2.6B beats an 8B Gemma on tool use while running in under 2.5 GBLiquid AI released LFM2.5-2.6B, a 2.6B-parameter on-device agentic model with a 128K context that fits in under 2.5 GB of memory. It posts 51.87 on AIME25, 59.17 on IFBench, 56.88 on BFCLv4 and 62.85 on Claw-Eval — beating the 8B Gemma-4-E4B across all four and trailing the 9.7B Qwen3.5-9B narrowly. Throughput is 220 tok/s on an M5 Max CPU, ~30 tok/s on phones, with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, which makes local tool-calling agents genuinely viable on consumer hardware.

Graph relationships

RELEASED
Liquid AI
Liquid AI released quantization-aware-distilled Q4_0 version of LFM2.5-2.6B
Source finding
RELEASED
Liquid AI
Liquid AI released LFM2.5-2.6B on August 6, 2026.
Source finding
RELEASED
Liquid AI -> LFM2.5-2.6B
Liquid AI released quantization-aware-distilled Q4_0 version of LFM2.5-2.6B
Source finding
RELEASED
Liquid AI -> LFM2.5-2.6B
Liquid AI released LFM2.5-2.6B on August 6, 2026.
Source finding

Source trail

Hugging Face Blog (Liquid AI)r/LocalLLaMA (upload timestamps verified via Hugging Face commits API)r/LocalLLaMA (64 upvotes / 71 comments), corroborated by Liquid AI's LFM2.5-2.6B blog post Hugging Face Blog

Graph sources

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