Can I Run LFM2.5 on 8 GB system RAM?
Written by Jakub Rusinowski · Last updated October 15, 2026
Yes — comfortably
Yes, comfortably — LFM2.5 2.6B at Q8_0 needs about 4.5 GB of the 6.4 GB usable on 8 GB system RAM, leaving ~1.9 GB spare and running at ~19.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~19.7 tok/s
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8 GB system RAM — what it gives a model
| Usable memory for models | 6.4 GB |
| Memory bandwidth | 90 GB/s |
LFM2.5 on 8 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 6.4 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 7 GB | ✗ No | — | — | 5.4 GB |
| Q8_0 | 4.5 GB | ✓ Yes | 16K | ~19.7 tok/s | 2.9 GB |
| Q6_K | 3.8 GB | ✓ Yes | 32K | ~24.4 tok/s | 2.2 GB |
| Q5_K_M | 3.5 GB | ✓ Yes | 32K | ~27.3 tok/s | 1.9 GB |
| Q4_K_M | 3.2 GB | ✓ Yes | 32K | ~30.9 tok/s | 1.6 GB |
| Q3_K_M | 2.8 GB | ✓ Yes | 32K | ~39.5 tok/s | 1.1 GB |
| Q2_K | 2.5 GB | ✓ Yes | 32K | ~46.7 tok/s | 0.9 GB |
Which LFM2.5 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| LFM2.5-24B-A2B | 15.5 GB | ✗ Too large | — |
| LFM2.5-8B-A1B | 7 GB | ✗ Too large | — |
| LFM2.5 2.6B | 3.2 GB | ✓ Fits | ~30.9 tok/s |
What to watch out for
- 2 larger variants of LFM2.5 do not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 6.4 GB of the 8 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run LFM2.5 on 8 GB system RAM?
Yes, comfortably — LFM2.5 2.6B at Q8_0 needs about 4.5 GB of the 6.4 GB usable on 8 GB system RAM, leaving ~1.9 GB spare and running at ~19.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of LFM2.5 should I use on 8 GB system RAM?
Q8_0 — it needs about 4.5 GB of the 6.4 GB available, downloads as roughly 2.9 GB, and runs at an estimated 19.7 tokens/sec with up to 16K of context.
What limits LFM2.5 on 8 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 8 GB system RAM
- Llama 3.1 Family on 8 GB system RAM
- Llama 3.2 Family on 8 GB system RAM
- Llama 3.2 Vision on 8 GB system RAM
- MiniCPM-V on 8 GB system RAM
- Ministral on 8 GB system RAM
LFM2.5 on GPUs
- LFM2.5 on NVIDIA GeForce RTX 5070
- LFM2.5 on NVIDIA GeForce RTX 5060 Ti 8GB
- LFM2.5 on NVIDIA GeForce RTX 5060
- LFM2.5 on NVIDIA GeForce RTX 4070 Ti