Can I Run EXAONE 3.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated February 10, 2026
Yes — comfortably
Yes, comfortably — EXAONE 3.5 32B at Q8_0 needs about 36.9 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~91.1 GB spare and running at ~5.4 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~5.4 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 |
EXAONE 3.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.9 GB | ✓ Yes | 32K | ~2.9 tok/s | 64 GB |
| Q8_0 | 36.9 GB | ✓ Yes | 32K | ~5.4 tok/s | 34 GB |
| Q6_K | 29.2 GB | ✓ Yes | 32K | ~6.9 tok/s | 26.2 GB |
| Q5_K_M | 25.6 GB | ✓ Yes | 32K | ~7.9 tok/s | 22.7 GB |
| Q4_K_M | 22.3 GB | ✓ Yes | 32K | ~9.2 tok/s | 19.3 GB |
| Q3_K_M | 16.6 GB | ✓ Yes | 32K | ~12.7 tok/s | 13.6 GB |
| Q2_K | 13.5 GB | ✓ Yes | 32K | ~16 tok/s | 10.5 GB |
Which EXAONE 3.5 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| EXAONE 3.5 32B | 22.3 GB | ✓ Fits | ~9.2 tok/s |
| EXAONE 3.5 7.8B | 6.6 GB | ✓ Fits | ~33.8 tok/s |
| EXAONE 3.5 2.4B | 2.9 GB | ✓ Fits | ~87.5 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 EXAONE 3.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — EXAONE 3.5 32B at Q8_0 needs about 36.9 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~91.1 GB spare and running at ~5.4 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of EXAONE 3.5 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 36.9 GB of the 128 GB available, downloads as roughly 34 GB, and runs at an estimated 5.4 tokens/sec with up to 32K of context.
What limits EXAONE 3.5 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)
- Falcon 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Gemma 2 Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Gemma 3 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)
EXAONE 3.5 on GPUs
- EXAONE 3.5 on NVIDIA GeForce RTX 5090
- EXAONE 3.5 on NVIDIA GeForce RTX 5060 Ti 8GB
- EXAONE 3.5 on NVIDIA GeForce RTX 5060
- EXAONE 3.5 on NVIDIA GeForce RTX 4090
What This Model Is Good At
Model & Tools
← Can I Run It? | EXAONE 3.5 model page | Check your hardware