作者： Jakub Rusinowski · 最后更新： 2026年9月11日
Yes — comfortably
Yes, comfortably — LFM2.5-8B-A1B at Q8_0 needs about 10.8 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~21.2 GB spare and running at ~258.5 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~258.5 tok/s
or compare on Vast.ai from $0.35/hr (typical low · varies)
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 18.6 GB | ✓ Yes | 64K | ~203.5 tok/s | 16.6 GB |
| Q8_0 | 10.8 GB | ✓ Yes | 128K | ~258.5 tok/s | 8.8 GB |
| Q6_K | 8.8 GB | ✓ Yes | 128K | ~277.9 tok/s | 6.8 GB |
| Q5_K_M | 7.9 GB | ✓ Yes | 128K | ~287.9 tok/s | 5.9 GB |
| Q4_K_M | 7 GB | ✓ Yes | 128K | ~297.9 tok/s | 5 GB |
| Q3_K_M | 5.5 GB | ✓ Yes | 128K | ~316.6 tok/s | 3.5 GB |
| Q2_K | 4.7 GB | ✓ Yes | 128K | ~327.9 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 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~21.2 GB spare and running at ~258.5 tok/s (estimated), with room for about 131,072 tokens of context.
Q8_0 — it needs about 10.8 GB of the 32 GB available, downloads as roughly 8.8 GB, and runs at an estimated 258.5 tokens/sec with up to 128K 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