Written by Jakub Rusinowski · Last updated September 19, 2026
Strix POINT, not Strix Halo: 12 Zen 5 cores with a 16-CU Radeon 890M on a 128-bit memory bus. 120 GB/s reflects the LPDDR5X-7500 fitted in machines like the Beelink SER9; AMD's own spec page quotes 89.6 GB/s for DDR5-5600 configurations, so the figure depends on the memory a builder solders down. Either way it is roughly half the Ryzen AI Max+ 395 this catalogue used to model it as.
| VRAM | 32 GB |
| Memory Bandwidth | 120 GB/s |
| TDP | 54 W |
| Architecture | Zen 5 + RDNA 3.5 "Strix Point" |
| Release Year | 2024 |
| MSRP at Launch | $0 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 9.0–19 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | Does not fit — needs ~44 GB of 32 GB usable |
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All models below run comfortably in 32 GB VRAM with Q4_K_M quantization.
| Command R Family | Command R (35B) · 22 GB VRAM · Q4_K_M · ollama run command-r |
| Qwen 3.5 | Qwen 3.5 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.5:35b-a3b |
| Qwen 3.6 | Qwen 3.6 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.6:35b-a3b |
| Nex-N2 | Nex-N2 mini · 22 GB VRAM · Q4_K_M · nex-n2 |
| Yi 1.5 Family | Yi 1.5 34B Chat · 22 GB VRAM · Q4_K_M · ollama run yi:34b |
| Qwen 3 | Qwen 3 32B · 21 GB VRAM · Q4_K_M · ollama run qwen3:32b |
| Aya Expanse | Aya Expanse 32B · 20 GB VRAM · Q4_K_M · ollama run aya-expanse:32b |
| DeepSeek R1 | DeepSeek R1 Distill Qwen 32B · 20 GB VRAM · Q4_K_M · ollama run deepseek-r1:32b |
56 more families also fit 32 GB — browse the full model library.
Yes — the AMD Ryzen AI 9 HX 370 has 32 GB VRAM and runs Strix POINT, not Strix Halo: 12 Zen 5 cores with a 16-CU Radeon 890M on a 128-bit memory bus. 120 GB/s reflects the LPDD
The AMD Ryzen AI 9 HX 370 is estimated to run Llama 3.1 8B at 9.0–19 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 32 GB usable. These are modelled estimates, not measurements — see /en/methodology.
With 32 GB you can run: Command R Family, Qwen 3.5, Qwen 3.6, Nex-N2, Yi 1.5 Family. Use Ollama for the easiest setup: ollama run llama3.1:8b.
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