Can I Run GLM-6 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated June 26, 2026
Yes — comfortably
Yes, comfortably — GLM-6 9B at Q8_0 needs about 11.6 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~116.4 GB spare and running at ~18.1 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~18.1 tok/s
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 |
GLM-6 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|
| F16 | 20 GB | ✓ Yes | 64K | ~10.1 tok/s | 18 GB |
| Q8_0 | 11.6 GB | ✓ Yes | 64K | ~18.1 tok/s | 9.6 GB |
| Q6_K | 9.4 GB | ✓ Yes | 64K | ~22.8 tok/s | 7.4 GB |
| Q5_K_M | 8.4 GB | ✓ Yes | 64K | ~25.9 tok/s | 6.4 GB |
| Q4_K_M | 7.4 GB | ✓ Yes | 64K | ~29.7 tok/s | 5.4 GB |
| Q3_K_M | 5.8 GB | ✓ Yes | 64K | ~39.4 tok/s | 3.8 GB |
| Q2_K | 5 GB | ✓ Yes | 64K | ~48.1 tok/s | 3 GB |
Which GLM-6 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| GLM-6 355B-A32B | 219.2 GB | ✗ Too large | — |
| GLM-6 9B | 7.4 GB | ✓ Fits | ~29.7 tok/s |
What to watch out for
- 1 larger variant of GLM-6 does not fit and would need CPU offload or different hardware.
- 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 GLM-6 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — GLM-6 9B at Q8_0 needs about 11.6 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~116.4 GB spare and running at ~18.1 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of GLM-6 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 11.6 GB of the 128 GB available, downloads as roughly 9.6 GB, and runs at an estimated 18.1 tokens/sec with up to 64K of context.
What limits GLM-6 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)
GLM-6 on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | GLM-6 model page | Check your hardware