LFM2.5-2.6B
2recorded runs
305.6best tok/s
2verified
What the LFM2.5-2.6B numbers mean
The fastest verified result on our board is 305.6 output tok/s on an RTX 3090 at Q4_K_M with a 24576-token context. That is about 3.3 ms per generated token — 500 tokens takes roughly 1.6 seconds. Prompt processing on the same record runs at 7609.8 tok/s, so a 2,000-token prompt is read in about 0.3 seconds. Peak VRAM was measured at 2.3 GB. Across the 2 records we hold for this model the range is 132.4–305.6 tok/s on 2 GPUs, because quantisation, context length and the engine build all move the number.
How much VRAM does LFM2.5-2.6B need?
LFM2.5-2.6B has 3.0B parameters. These estimates use the same formula as the VRAM calculator: weights, plus about 15% for a moderate-context KV cache and runtime overhead, plus a small engine floor.
| Quantisation | Estimated VRAM | Smallest card that fits |
|---|---|---|
| Q4_K_M (4-bit) | 2.7 GB | 8 GB |
| Q8_0 (8-bit) | 4.5 GB | 8 GB |
| FP16 (16-bit) | 7.7 GB | 12 GB |
Fastest GPUs for this model (best tok/s)
- RTX 3090305.6
- Intel Arc Pro B70132.4
Models with a similar footprint
| Model | Params | Q4_K_M weights |
|---|---|---|
| Llama-3.2-3B-Instruct | 3B | 1.7 GB |
| Ministral-3-3B-Instruct-2512 | 3B | 1.7 GB |
| OpenLLaMA-3B | 3B | 1.7 GB |
| VibeThinker-3B | 3B | 1.7 GB |
| gemma-3n-E4B-it-text | 4B | 2.2 GB |
| NVIDIA-Nemotron-3-Nano-4B | 4B | 2.2 GB |
| GPU | Model | Quant | Backend | Ctx | tok/s | prompt tok/s | VRAM GB | Source |
|---|---|---|---|---|---|---|---|---|
| RTX 3090 · 24 GB | LFM2.5-2.6B | Q4_K_M | llama.cpp 0.3.0-dev (build 1, commit d7bd3bf) | 24576 | 305.6 ✓ | 7609.8 | 2.3 | src |
| Intel Arc Pro B70 · 32 GB | LFM2.5-2.6B | Q8_0 | llama.cpp 9fee29e9435f; oneAPI 2026.1.1 | 8192 | 132.4 ✓ | — | — | src |