Can I Run Gemma 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
作者： Jakub Rusinowski · 最后更新： 2025年3月12日
Yes — comfortably
Yes, comfortably — Gemma 3 27B Instruct at Q8_0 needs about 30.8 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~97.2 GB spare and running at ~6.4 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~6.4 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 |
Gemma 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 56.1 GB | ✓ Yes | 128K | ~3.5 tok/s | 54 GB |
| Q8_0 | 30.8 GB | ✓ Yes | 128K | ~6.4 tok/s | 28.7 GB |
| Q6_K | 24.3 GB | ✓ Yes | 128K | ~8.1 tok/s | 22.1 GB |
| Q5_K_M | 21.3 GB | ✓ Yes | 128K | ~9.3 tok/s | 19.1 GB |
| Q4_K_M | 18.4 GB | ✓ Yes | 128K | ~10.8 tok/s | 16.3 GB |
| Q3_K_M | 13.6 GB | ✓ Yes | 128K | ~14.8 tok/s | 11.5 GB |
| Q2_K | 11 GB | ✓ Yes | 128K | ~18.6 tok/s | 8.9 GB |
Which Gemma 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 18.4 GB | ✓ Fits | ~10.8 tok/s |
| Gemma 3 12B Instruct | 9.1 GB | ✓ Fits | ~22.7 tok/s |
| Gemma 3 4B Instruct | 3.6 GB | ✓ Fits | ~61.7 tok/s |
| Gemma 3 1B Instruct | 1.5 GB | ✓ Fits | ~178.6 tok/s |
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 Gemma 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — Gemma 3 27B Instruct at Q8_0 needs about 30.8 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~97.2 GB spare and running at ~6.4 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of Gemma 3 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 30.8 GB of the 128 GB available, downloads as roughly 28.7 GB, and runs at an estimated 6.4 tokens/sec with up to 128K of context.
What limits Gemma 3 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)
- Gemma 3n on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Gemma 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- GLM-4.7 / GLM-Z1 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- GLM-5 / GLM-5.1 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- GLM-5.3-Flash on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Gemma 3 on GPUs
- Gemma 3 on NVIDIA GeForce RTX 5090
- Gemma 3 on NVIDIA GeForce RTX 5080
- Gemma 3 on NVIDIA GeForce RTX 5070 Ti
- Gemma 3 on NVIDIA GeForce RTX 5070