Can I Run BitNet b1.58 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
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 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~122.8 GB spare and running at ~43.7 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~43.7 tok/s
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Framework Desktop (Ryzen AI Max+ 395, 128 GB) — what it gives a model
| Usable memory for models | 128 GB |
| Memory bandwidth | 256 GB/s |
| Form factor | Mini PC |
| Operating system | Windows or Linux |
| Memory upgradeable | No — soldered |
BitNet b1.58 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 8.3 GB | ✓ Yes | — | ~25.6 tok/s | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~43.7 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~53.5 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~59.6 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~66.9 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~84.1 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~98 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.
- Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.
Framework Desktop 128 GB limitations
- Memory is soldered LPDDR5X — unusually for Framework, this is the one component that cannot be upgraded.
- ROCm/Vulkan support for Strix Halo is younger than CUDA; check your runtime supports it before buying.
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.
- 128 GB unified memory at 256 GB/s, shared between CPU and GPU.
- 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 Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~122.8 GB spare and running at ~43.7 tok/s (estimated).
Which quantization of BitNet b1.58 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 5.2 GB of the 128 GB available, downloads as roughly 3.5 GB, and runs at an estimated 43.7 tokens/sec.
What limits BitNet b1.58 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
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 Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Bonsai 27B on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Codestral on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Cogito v1 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Command R Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Cosmos 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
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
← Can I Run It? | BitNet b1.58 model page | Check your hardware