Can I Run BitNet b1.58 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated March 1, 2024
Yes — comfortably
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.8 GB spare and running at ~126.7 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~126.7 tok/s
RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
BitNet b1.58 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|
| F16 | 8.3 GB | ✓ Yes | — | ~81.1 tok/s | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~126.7 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~148.2 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~160.8 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~174.7 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~204.8 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~226.1 tok/s | 1.1 GB |
What to watch out for
- 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.
RTX 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current card.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 24 GB of VRAM on the NVIDIA GeForce RTX 3090 at 936 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- 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 BitNet b1.58 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.8 GB spare and running at ~126.7 tok/s (estimated).
Which quantization of BitNet b1.58 should I use on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 5.2 GB of the 24 GB available, downloads as roughly 3.5 GB, and runs at an estimated 126.7 tokens/sec.
What limits BitNet b1.58 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
BitNet b1.58 on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | BitNet b1.58 model page | Check your hardware