Can I Run Llama 3.1 Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated July 23, 2024
Yes — comfortably
Yes, comfortably — Llama 3.1 8B Instruct at Q8_0 needs about 10.4 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~117.6 GB spare and running at ~20.3 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~20.3 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 |
Llama 3.1 Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 17.9 GB | ✓ Yes | 128K | ~11.3 tok/s | 16 GB |
| Q8_0 | 10.4 GB | ✓ Yes | 128K | ~20.3 tok/s | 8.5 GB |
| Q6_K | 8.4 GB | ✓ Yes | 128K | ~25.5 tok/s | 6.6 GB |
| Q5_K_M | 7.5 GB | ✓ Yes | 128K | ~28.9 tok/s | 5.7 GB |
| Q4_K_M | 6.7 GB | ✓ Yes | 128K | ~33.1 tok/s | 4.8 GB |
| Q3_K_M | 5.3 GB | ✓ Yes | 128K | ~43.9 tok/s | 3.4 GB |
| Q2_K | 4.5 GB | ✓ Yes | 128K | ~53.4 tok/s | 2.6 GB |
What to watch out for
- 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 computed from this model's published attention configuration.
FAQ
Can I run Llama 3.1 Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — Llama 3.1 8B Instruct at Q8_0 needs about 10.4 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~117.6 GB spare and running at ~20.3 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of Llama 3.1 Family should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 10.4 GB of the 128 GB available, downloads as roughly 8.5 GB, and runs at an estimated 20.3 tokens/sec with up to 128K of context.
What limits Llama 3.1 Family 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)
- Llama 3.2 Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Llama 3.2 Vision on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Llama 3.3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Llama 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Llama 4.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Llama 3.1 Family on GPUs
- Llama 3.1 Family on NVIDIA GeForce RTX 5070
- Llama 3.1 Family on NVIDIA GeForce RTX 5060 Ti 8GB
- Llama 3.1 Family on NVIDIA GeForce RTX 5060
- Llama 3.1 Family on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
← Can I Run It? | Llama 3.1 Family model page | Check your hardware