Can I Run Gemma 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Written by Jakub Rusinowski · Last updated June 3, 2026

Yes — comfortably

Yes, comfortably — Gemma 4 31B at Q8_0 needs about 35.5 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~92.5 GB spare and running at ~5.6 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~5.6 tok/s

Framework Desktop (Ryzen AI Max+ 395, 128 GB) — what it gives a model

Usable memory for models128 GB
Memory bandwidth256 GB/s
Form factorMini PC
Operating systemWindows or Linux
Memory upgradeableNo — soldered

Gemma 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization

QuantMemory neededFits 128 GB?Max contextEst. speedDownload
F1664.6 GB✓ Yes128K~3 tok/s62 GB
Q8_035.5 GB✓ Yes128K~5.6 tok/s32.9 GB
Q6_K28 GB✓ Yes128K~7.2 tok/s25.4 GB
Q5_K_M24.6 GB✓ Yes128K~8.2 tok/s22 GB
Q4_K_M21.3 GB✓ Yes128K~9.6 tok/s18.7 GB
Q3_K_M15.8 GB✓ Yes128K~13.2 tok/s13.2 GB
Q2_K12.8 GB✓ Yes128K~16.7 tok/s10.2 GB

Which Gemma 4 sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Gemma 4 31B21.3 GB✓ Fits~9.6 tok/s
Gemma 4 26B-A4B18.2 GB✓ Fits~51.9 tok/s
Gemma 4 12B (Unified)9.4 GB✓ Fits~23 tok/s
Gemma 4 E4B6.8 GB✓ Fits~32.9 tok/s
Gemma 4 E2B4.9 GB✓ Fits~47.8 tok/s

What to watch out for

Framework Desktop 128 GB limitations

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

FAQ

Can I run Gemma 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Yes, comfortably — Gemma 4 31B at Q8_0 needs about 35.5 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~92.5 GB spare and running at ~5.6 tok/s (estimated), with room for about 131,072 tokens of context.

Which quantization of Gemma 4 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Q8_0 — it needs about 35.5 GB of the 128 GB available, downloads as roughly 32.9 GB, and runs at an estimated 5.6 tokens/sec with up to 128K of context.

What limits Gemma 4 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 4 on GPUs

What This Model Is Good At

Model & Tools

← Can I Run It? | Gemma 4 model page | Check your hardware