Can I Run BitNet b1.58 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
作者： Jakub Rusinowski · 最后更新： 2024年3月1日
Yes — comfortably
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.8 GB spare and running at ~133.5 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~133.5 tok/s
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RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
BitNet b1.58 on RTX 4090 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 | — | ~86.2 tok/s | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~133.5 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~155.7 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~168.5 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~182.7 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~213 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~234.4 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 4090 desktop limitations
- 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second 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 4090 at 1008 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 4090 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 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.8 GB spare and running at ~133.5 tok/s (estimated).
Which quantization of BitNet b1.58 should I use on RTX 4090 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 133.5 tokens/sec.
What limits BitNet b1.58 on RTX 4090 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 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Bonsai 27B on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Codestral on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Cogito v1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Command R Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Cosmos 3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
BitNet b1.58 on GPUs
- BitNet b1.58 on NVIDIA GeForce RTX 5060 Ti 8GB
- BitNet b1.58 on NVIDIA GeForce RTX 5060
- BitNet b1.58 on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
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