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Models / LFM2.5 24B-A2B / VRAM

How much VRAM do you need for LFM2.5 24B-A2B?

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Weight math from the model card, plus every measured RigBase result for LFM2.5 24B-A2B. Leave extra room for context (KV cache).

Quick answer: at Q4_K_M the weights are about 14.7 GB (23.97B params × 4.89 bits ÷ 8, llama.cpp quantize table). only about 2.45B parameters are active per token (MoE). the smallest-memory build with a measured result here is RTX 3060 (12 GB) at 125 tok/s. Open the VRAM calculator →

Weight size by quant

QuantBits/weightWeights only
Q8_08.5025.5 GB
Q6_K6.5619.7 GB
Q5_K_M5.7017.1 GB
Q4_K_M4.8914.7 GB
Q3_K_M4.0012.0 GB
Q2_K3.169.5 GB

Bits-per-weight for GGUF rows: llama.cpp quantize README. Parameter count: via llamabench.ai. We advise staying at Q4 or above for chat quality — see Is Q4 good enough?.

Measured speeds for LFM2.5 24B-A2B

HardwareQuantEnginetok/s
RTX 4090 · 24 GBllama-bench tg128 (quant per result)llama.cpp261-339
NVIDIA TITAN RTX · 24 GBUnsloth UD-Q4_K_XLllama.cpp b11222 (CUDA)190.7
RTX 4060 Ti 16GB · 16 GBllama-bench tg128 (quant per result)llama.cpp131-150
RTX 3060 · 12 GBllama-bench tg128 (quant per result)llama.cpp125
AMD Radeon 8060S (Strix Halo, unified) · 128 GBUnsloth UD-Q4_K_XL (13.9 GiB)llama.cpp (Vulkan)100.2

⚡ = speculative decoding (MTP, DFlash…). Full list: best home rig for LFM2.5 24B-A2B.

Related

All LFM2.5 24B-A2B builds VRAM + KV calculator By VRAM size GGUF vs AWQ vs FP8 vs NVFP4 How much system RAM?

FAQ

How much VRAM do you need for LFM2.5 24B-A2B?

Weights alone are about 14.7 GB at Q4_K_M (23.97B × 4.89 bits ÷ 8). Add memory for the KV cache (context) and the runtime. The smallest-memory measured result in RigBase is 12 GB (RTX 3060) at 125 tok/s. Going below Q4 usually hurts quality for chat; see the Q4 guide.

What is the fastest measured speed for LFM2.5 24B-A2B?

RTX 4090: 261-339 tok/s plain decode (llama-bench tg128 (quant per result), llama.cpp).

Does system RAM replace VRAM?

Only for overflow. When the whole model fits in GPU or unified memory, system RAM barely matters for decode speed. See the system-RAM guide.

Not enough VRAM? Rent before you buy. Run LFM2.5 24B-A2B on a rented 24–96 GB cloud GPU by the hour and see the real speed before spending €1,000+. Try it on Runpod
Referral link: if you sign up with Google and add $10, Runpod gives you and RigBase bonus credit (terms). It never affects our data.