Yes — with 26.7 GB to spare
LLM-jp 4 33B Thinking at Q4_K_M fits your Ryzen AI Max+ 395 · 64 GB entirely in unified memory at 8K context, at an estimated 7.7 tokens per second. There is room for its full 64K window.
Fully in unified memory
8K context
Q4_K_M · 18.7 GB
Apache 2.0
Released 14 Aug 2026
New this week
Not in the Ollama library
Japan’s national-institute reasoning model, Japanese and English. A plain dense Llama-style 33B: Q4 is a tight 24 GB fit.
The VRAM budget
weights 18.7 GB
Weights 18.7 GB
KV cache @ 8K 2.00 GB
Runtime overhead 0.6 GB
Free 26.7 GB of 48.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 32.9 GB | 35.5 GB | 58K | 4.4 | −0.1% ppl | Long context |
| Q6_K | 25.4 GB | 28.0 GB | 64K | 5.6 | −0.4% ppl | Long context |
| Q5_K_M | 21.9 GB | 24.5 GB | 64K | 6.5 | −0.8% ppl | Long context |
| Q4_K_M | 18.7 GB | 21.3 GB | 64K | 7.7 | −1.9% ppl | Recommended |
| Q3_K_M | 15.1 GB | 17.7 GB | 64K | 9.5 | −5.4% ppl | Long context |
| Q2_K | 12.9 GB | 15.5 GB | 64K | 11 | −15% ppl | Long context |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.
How to run it
$ llama-server \
-hf llm-jp/llm-jp-4-33b-thinking:Q4_K_M \
-c 8192 -ngl 99
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 18.7 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02macOS caps what the GPU may wire down at about 48.0 GB of 64 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 64K context on this card.