Can you run LFM2 24B-A2B Instruct on RTX 4060 Ti?
LFM2 24B-A2B Instruct Q4_K_M on the NVIDIA GeForce RTX 4060 Ti: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 4060 Ti runs LFM2 24B-A2B Instruct. 14 GB weights at Q4_K_M vs 14.4 GB usable VRAM; ~38 tok/s est. (ModelFit, 2026).
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, LFM2 24B-A2B Instruct on RTX 4060 Ti, https://modelfit.io/can-i-run/lfm2-24b-a2b-q4-on-rtx-4060-ti/, updated September 2026, CC BY 4.0.
Last updated: September 24, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for LFM2 24B-A2B Instruct on the RTX 4060 Ti, in order of what usually breaks first.
Where the RTX 4060 Ti sits for LFM2 24B-A2B Instruct
The RTX 4060 Ti is the cheapest tracked card that runs LFM2 24B-A2B Instruct fully in VRAM (~38 tok/s est.). Every cheaper card in the table forces partial offload. The next step up, the RTX 3090, reaches ~98 tok/s est. with a 16k context ceiling.
Same engine as the verdict above: 90% of VRAM usable, KV-cache at fp16, bandwidth-derived tok/s. Verdicts of the other cards are computed for LFM2 24B-A2B Instruct Q4_K_M exactly.
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 14.4 GB.
Workload verdicts
LFM2 24B-A2B Instruct on the RTX 4060 Ti, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 14 GB. Context costs extra KV-cache on top; this is where long-context sessions break on cards that technically fit the weights.
This configuration exceeds the comfortable VRAM budget by about 3.6 GB. Expect offload pressure or a shorter safe context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
See how fast it feels
A deterministic typing simulation for LFM2 24B-A2B Instruct: first token ~0.4s (instant prefill), then ~38 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
LFM2 24B-A2B Instruct on RTX 4060 Ti: FAQ
Can the NVIDIA GeForce RTX 4060 Ti run LFM2 24B-A2B Instruct?
Yes. LFM2 24B-A2B Instruct (Q4_K_M) loads in about 14 GB and the RTX 4060 Ti offers 14.4 GB of usable VRAM, leaving headroom for context. Expect at roughly 38 tokens/sec (est.).
How much VRAM does LFM2 24B-A2B Instruct need?
About 14 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 4.0 GB extra at 16k tokens. The RTX 4060 Ti budget is 14.4 GB (16 GB x 90%).
What is the best quantization of LFM2 24B-A2B Instruct for the RTX 4060 Ti?
Stick with the Q4_K_M build at 14 GB; every heavier quant exceeds the 14.4 GB usable VRAM.
What GPU do I need to run LFM2 24B-A2B Instruct comfortably?
The RTX 4060 Ti already runs LFM2 24B-A2B Instruct comfortably. Larger cards only buy you longer context or a heavier quant.
How much context can LFM2 24B-A2B Instruct use on the RTX 4060 Ti?
None worth having: the 14 GB of weights alone exceed the 14.4 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is LFM2 24B-A2B Instruct on the RTX 4060 Ti fast enough for coding agents?
Yes: the engine estimates ~38 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, and LFM2 24B-A2B Instruct is tuned for tool-calling.