text model · LFM · Windows
Can I run LFM2.5 8B-A1B on Nvidia GeForce RTX 4080 (16GB)?
Yes. LFM2.5 8B-A1B runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~6.7 GB of ~15 GB usable).
Runs at Q4_K_M using ~6.7 GB of ~15 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4080 (16GB) leaves ~8.3 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~6.7 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run lfm2.5:8b-a1b llama-cli -hf LiquidAI/LFM2.5-8B-A1B-GGUF:Q4_K_M lms get LiquidAI/LFM2.5-8B-A1B-GGUF - Parameters
- 8.3B (MoE, 1.5B active)
- Q4_K_M size
- 5.2 GB
- Q8_0 size
- 9 GB
- Context
- 128k
- Ollama tag
- lfm2.5:8b-a1b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Power draw
- ~320 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run LFM2.5 8B-A1B on other hardware
FAQ
Can Nvidia GeForce RTX 4080 (16GB) run LFM2.5 8B-A1B?
Yes. LFM2.5 8B-A1B runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~6.7 GB of ~15 GB usable).
How much memory does LFM2.5 8B-A1B need?
Nvidia GeForce RTX 4080 (16GB) has room to spare. At Q4_K_M the weights are ~5.2 GB; with KV cache and runtime overhead, budget ~6.7 GB at a 4k context. It is a Mixture-of-Experts model (8.3B total / 1.5B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run LFM2.5 8B-A1B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Embed this
[](https://localmodel.run/can-i-run/lfm2.5-8b-a1b/nvidia-rtx-4080-16gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.