Written by Jakub Rusinowski · Last updated September 11, 2026
Yes — comfortably
Yes, comfortably — LFM2.5-8B-A1B at Q8_0 needs about 10.8 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.2 GB spare and running at ~114.5 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~114.5 tok/s
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| Usable memory for models | 16 GB |
| Memory bandwidth | 448 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 18.6 GB | ✗ No | — | — | 16.6 GB |
| Q8_0 | 10.8 GB | ✓ Yes | 32K | ~114.5 tok/s | 8.8 GB |
| Q6_K | 8.8 GB | ✓ Yes | 32K | ~130.6 tok/s | 6.8 GB |
| Q5_K_M | 7.9 GB | ✓ Yes | 32K | ~139.7 tok/s | 5.9 GB |
| Q4_K_M | 7 GB | ✓ Yes | 64K | ~149.5 tok/s | 5 GB |
| Q3_K_M | 5.5 GB | ✓ Yes | 64K | ~169.6 tok/s | 3.5 GB |
| Q2_K | 4.7 GB | ✓ Yes | 64K | ~183.1 tok/s | 2.7 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — LFM2.5-8B-A1B at Q8_0 needs about 10.8 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.2 GB spare and running at ~114.5 tok/s (estimated), with room for about 32,768 tokens of context.
Q8_0 — it needs about 10.8 GB of the 16 GB available, downloads as roughly 8.8 GB, and runs at an estimated 114.5 tokens/sec with up to 32K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving