Can you run LFM2 24B-A2B Instruct on RTX 5090?
LFM2 24B-A2B Instruct Q4_K_M on the NVIDIA GeForce RTX 5090: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 5090 runs LFM2 24B-A2B Instruct. 14 GB weights at Q4_K_M vs 28.8 GB usable VRAM; ~164 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 5090, https://modelfit.io/can-i-run/lfm2-24b-a2b-q4-on-rtx-5090/, 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 5090, in order of what usually breaks first.
Where the RTX 5090 sits for LFM2 24B-A2B Instruct
On the RTX 5090, LFM2 24B-A2B Instruct runs with room to spare (~164 tok/s est.). The cheapest card that also runs it comfortably is the RTX 4060 Ti (16 GB, ~38 tok/s est.). Context ceiling on this card: 32k; on the RTX 4060 Ti: none.
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: 28.8 GB.
Workload verdicts
LFM2 24B-A2B Instruct on the RTX 5090, 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.
The model leaves about 11 GB of the usable VRAM budget free after weights and 16k 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 ~0.3s (instant prefill), then ~164 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 5090: FAQ
Can the NVIDIA GeForce RTX 5090 run LFM2 24B-A2B Instruct?
Yes. LFM2 24B-A2B Instruct (Q4_K_M) loads in about 14 GB and the RTX 5090 offers 28.8 GB of usable VRAM, leaving headroom for context. Expect at roughly 164 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 5090 budget is 28.8 GB (32 GB x 90%).
What is the best quantization of LFM2 24B-A2B Instruct for the RTX 5090?
Stick with the Q4_K_M build at 14 GB; every heavier quant exceeds the 28.8 GB usable VRAM.
What GPU do I need to run LFM2 24B-A2B Instruct comfortably?
The RTX 5090 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 5090?
Up to 32k tokens stay fully in VRAM (22.0 GB total with the KV cache). At 64k tokens the total reaches 30.0 GB and tips over the budget. Quantizing the KV cache to q8_0 roughly halves the KV column and buys back about one context tier.
Is LFM2 24B-A2B Instruct on the RTX 5090 fast enough for coding agents?
Yes: the engine estimates ~164 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, and LFM2 24B-A2B Instruct is tuned for tool-calling.