LFM2.5 24B-A2B
24B-A2BLiquid AI LFM2.5
Liquid AI's largest LFM2.5 model, released in October: a 24B mixture-of-experts reasoning model with about 2.45B parameters active per token. It is built for on-device agents and tool calling, and always thinks before it answers. Only 10 of its 40 layers keep a growing KV cache, so long context is cheap: at Q4_K_M it needs about 15.3 GB at 4K, 15.9 GB at 32K and 17.9 GB across the whole 128K window. That is tight on a 16 GB card and comfortable on 24 GB at any context; Q8_0 (about 26.7 GB) needs a 32 GB card. Text only. Sizes come from Liquid's own GGUF files, and no per-level quality loss has been published.
131K
Max Context
2
Quant Variants
GGUF Q8_0
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
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Dense 36B with a native 512K context. Q4 weights are ~22GB, which lands on a 24GB card or a 2×16GB split — but the context is the real cost: 512K of KV cache is roughly 128GB on its own, so budget context first and weights second.
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Hybrid attention: only 16 of its 64 layers keep a KV cache, which is why a 27B model holds a 262K context in about 16GB of cache rather than 64GB. Sizing it as a conventional stack overstates the cache fourfold — the estimates here count the 16 and match published measurements at 8K, 32K and 262K. Quality loss per level has not been published.
GLM-4.7-Flash
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Zhipu's lightweight member of GLM-4.7: 30B total, about 3B active per token, MIT licence. Its attention is MLA, which caches one compressed 576-value vector per token per layer instead of full keys and values — so at 32K context the cache is under 2 GB where a conventional 47-layer model would need several times that. llama.cpp runs it as a DeepSeek-2-style model. Sizes below use the calculator's generic Q4_K_M/Q8_0 rates: no GGUF file size could be checked, and quality loss per level has not been published.
Running LFM2.5 24B-A2B locally
At Q4_K_M and 4K of context, LFM2.5 24B-A2B needs about 15.3 GB — 13.8 GB of weights, 0.08 GB of KV cache and a 1.4 GB activation buffer. The smallest card in this index that clears that comfortably is the Radeon RX 7900 XT at 20 GB, and 34 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 0.55 GB, taking the total to 15.9 GB. That is gentler than the parameter count suggests: only 10 of 40 layers keep a KV cache that grows with context, so the cache scales at a fraction of the usual rate. The model's native window is 128K; holding all of it at Q4_K_M would need about 18 GB.
Which build to download
This index only tracks LFM2.5 24B-A2B in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does LFM2.5 24B-A2B need?
- About 15.3 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 15.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will LFM2.5 24B-A2B run on a 20GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 15.3 GB, which leaves 4.7 GB spare on a Radeon RX 7900 XT. That is the smallest card in this index that clears it comfortably; 34 of 78 do.
- Which quantization of LFM2.5 24B-A2B should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- Radeon RX 7900 XT20GB · +4.7Mac M4 24G24GB · +2.7Mac M4 Pro 24G24GB · +2.7RTX 309024GB · +8.7Radeon RX 7900 XTX24GB · +8.7
- Tight but possible
- RTX 4060 Ti 16G16GBRadeon RX 7600 XT16GBRadeon RX 9060 XT 16G16GB
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-14 RSS → /feed.xml