Can I Run BitNet b1.58 on RTX 4090 Laptop (16 GB VRAM, 32 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 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~10.8 GB spare and running at ~104 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~104 tok/s
RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
BitNet b1.58 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|
| F16 | 8.3 GB | ✓ Yes | — | ~64.9 tok/s | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~104 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~123.2 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~134.5 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~147.4 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~175.8 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~196.6 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 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
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.
- 16 GB of VRAM on the NVIDIA GeForce RTX 4080 at 717 GB/s.
- 32 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 Laptop (16 GB VRAM, 32 GB RAM)?
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~10.8 GB spare and running at ~104 tok/s (estimated).
Which quantization of BitNet b1.58 should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 5.2 GB of the 16 GB available, downloads as roughly 3.5 GB, and runs at an estimated 104 tokens/sec.
What limits BitNet b1.58 on RTX 4090 Laptop (16 GB VRAM, 32 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 Laptop (16 GB VRAM, 32 GB RAM)
BitNet b1.58 on GPUs
What This Model Is Good At
Model & Tools
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