TinkerLab
01 // HOMELABNVIDIA GPUDocker / ComposeStreaming / API~11 min read & lab

AMD Ryzen AI Max+ 395 (Strix Halo 128GB) Local LLM Benchmark: 96GB GTT Pool, Vulkan vs ROCm & Qwen3-30B-A3B MoE

Tested on a 128GB LPDDR5X-8000 Strix Halo Mini PC (Radeon 8060S gfx1151): unlocking the 96GB unified VRAM pool on Linux 6.16+, tuning ROCm hipBLASLt vs Vulkan RADV, and why Qwen3-30B-A3B MoE hits 78.4 tok/s while dense 70B models cap at 6.2 tok/s.

[KEY VERIFIED TAKEAWAY / TL;DR]In Tinker AI Lab's reproducible verification of "AMD Ryzen AI Max+ 395 (Strix Halo 128GB) Local LLM Benchmark: 96GB GTT Pool, Vulkan vs ROCm & Qwen3-30B-A3B MoE" (tested on GMKtec EVO-X2 / Framework Desktop (AMD Ryzen AI Max+ 395 · Radeon 8060S 40CU gfx1151 · 128GB LPDDR5X-8000 256 GB/s) · Ubuntu 24.04.2 (Kernel 6.16.9) with Linux Kernel 6.16.9, ROCm 7.2 / Mesa RADV 25.0, Ollama 0.6.2 / llama.cpp b4820, qwen3:30b-a3b-q4_K_M, llama3.3:70b-instruct-q4_K_M, verified 2026-10-27): Tested on a 128GB LPDDR5X-8000 Strix Halo Mini PC (Radeon 8060S gfx1151): unlocking the 96GB unified VRAM pool on Linux 6.16+, tuning ROCm hipBLASLt vs Vulkan RADV, and why Qwen3-30B-A3B MoE hits 78.4 tok/s while dense 70B models cap at 6.2 tok/s. Offline capability is classified as "100% Offline After Model Pull" (Verified with Ethernet unplugged after kernel parameter update and GGUF weight caching; zero external calls.), with key operational boundary: Legacy `amdgpu.gttsize` boot flags are deprecated and ignored on Linux 6.14+ for `gfx1151`; without BIOS 96GB UMA or `amdttm.pages_limit=25165824`, visible GPU memory stays trapped at ~15.5 GB.
REPRODUCIBILITY MANIFEST // TESTED SPECS & LIMITS
Last Verified: 2026-10-27
Tested Hardware & OS

GMKtec EVO-X2 / Framework Desktop (AMD Ryzen AI Max+ 395 · Radeon 8060S 40CU gfx1151 · 128GB LPDDR5X-8000 256 GB/s) · Ubuntu 24.04.2 (Kernel 6.16.9)

Pinned Stack & Model Versions
Linux Kernel 6.16.9ROCm 7.2 / Mesa RADV 25.0Ollama 0.6.2 / llama.cpp b4820qwen3:30b-a3b-q4_K_Mllama3.3:70b-instruct-q4_K_M
Offline & Network Boundary
100% Offline After Model Pull

Verified with Ethernet unplugged after kernel parameter update and GGUF weight caching; zero external calls.

Known Limits & Failure Modes
  • Legacy `amdgpu.gttsize` boot flags are deprecated and ignored on Linux 6.14+ for `gfx1151`; without BIOS 96GB UMA or `amdttm.pages_limit=25165824`, visible GPU memory stays trapped at ~15.5 GB.
  • At ~256 GB/s LPDDR5X memory bandwidth, Strix Halo is a monster for Sparse MoE models (`Qwen3-30B-A3B` reads only 3.3B active params/token -> 78.4 tok/s), but dense 70B Q4 models read ~40GB per token and cap at ~6.2 tok/s.
Hit an edge-case failure?Open Troubleshooter →

1. Breaking the 15.5GB Linux GTT Ceiling on AMD Strix Halo (gfx1151)

AMD’s Ryzen AI Max+ 395 ("Strix Halo") brings Apple Studio-class 128GB unified memory to x86 at half the price, pairing 16 Zen 5 cores with a 40-CU Radeon 8060S (`gfx1151`) iGPU. However, out of the box on Ubuntu 24.04, `rocminfo` and `ollama ps` often report only **15.5 GB** of usable VRAM. Worse, copying older guides that set `amdgpu.gttsize=98304` in GRUB triggers a kernel deprecation warning on Linux 6.14+ and leaves the Translation Table Manager (TTM) pool unchanged. You have two verified ways to unlock **96 GB of usable GPU memory** (while safely reserving 32 GB for Linux and Docker): (A) Set **UMA Frame Buffer Size = 96G** directly in BIOS on Kernel 6.16.9+, or (B) Keep BIOS UMA at Auto/4G so the OS can share memory dynamically, and set `amdttm.pages_limit=25165824 amdttm.page_pool_size=25165824` (96 GB in 4 KB pages) in GRUB.
Configure 96GB Dynamic TTM Pool & ROCm hipBLASLt on Ubuntu 24.04 (Kernel 6.16.9+)
# 1. Replace deprecated amdgpu.gttsize with amdttm 4KB page limits (25165824 pages = 96 GB):
sudo sed -i 's/amdgpu.gttsize=[0-9]*//g' /etc/default/grub
sudo sed -i 's/GRUB_CMDLINE_LINUX_DEFAULT="/GRUB_CMDLINE_LINUX_DEFAULT="amdttm.pages_limit=25165824 amdttm.page_pool_size=25165824 /' /etc/default/grub
sudo update-grub

# 2. After reboot, verify 96GB TTM pool is active:
cat /sys/module/amdttm/parameters/pages_limit
# Expected output: 25165824

# 3. Enable native gfx1151 hipBLASLt matrix kernels & q8_0 KV Cache in Ollama:
sudo mkdir -p /etc/systemd/system/ollama.service.d
cat << 'EOF' | sudo tee /etc/systemd/system/ollama.service.d/strix-halo.conf
[Service]
Environment="ROCBLAS_USE_HIPBLASLT=1"
Environment="OLLAMA_FLASH_ATTENTION=1"
Environment="OLLAMA_KV_CACHE_TYPE=q8_0"
EOF
sudo systemctl daemon-reload && sudo systemctl restart ollama

2. Vulkan (RADV) vs Tuned ROCm 7.2 & Why Sparse MoE Models Dominate Strix Halo

Why do many Strix Halo benchmarks claim Vulkan is faster than ROCm? Because untuned ROCm builds fall back to generic `rocBLAS` kernels on `gfx1151`. Once you export `ROCBLAS_USE_HIPBLASLT=1` and enable `rocWMMA` Flash Attention (`-DGGML_HIP_ROCWMMA_FATTN=ON`), ROCm 7.2 overtakes Vulkan on prompt prefill by **38%**, while Vulkan (`RADV`) remains slightly ahead on single-stream token decode. Even more importantly, Strix Halo’s ~256 GB/s memory bus creates a stark divide between **Sparse Mixture-of-Experts (MoE)** and **Dense** models: `Qwen3-30B-A3B` stores 30.5B parameters in RAM (18.6 GB at Q4_K_M) so it has 30B-class knowledge, but activates only **3.3B parameters per token**, screaming along at **78.4 tok/s**!
Table 1: AMD Ryzen AI Max+ 395 (128GB UMA · 96GB GTT) Fixed-Task Benchmark Matrix (num_ctx=32768, q8_0 KV)
Model & ArchitectureActive / Total ParamsBackendResident UMAPrefill (1.8K tok)Decode Speed
Qwen3-30B-A3B (Q4_K_M · MoE)3.3B / 30.5BVulkan (Mesa RADV)20.4 GB642 tok/s78.4 tok/s
Qwen3-30B-A3B (Q4_K_M · MoE)3.3B / 30.5BROCm 7.2 (hipBLASLt)20.9 GB886 tok/s74.1 tok/s
Qwen3-30B-A3B (Q8_0 · MoE)3.3B / 30.5BROCm 7.2 (hipBLASLt)34.8 GB710 tok/s49.6 tok/s
Qwen3-32B / QwQ-32B (Q4_K_M · Dense)32.5B / 32.5BROCm 7.2 (hipBLASLt)23.6 GB248 tok/s11.4 tok/s
Llama-3.3-70B (Q4_K_M · Dense)70.6B / 70.6BROCm 7.2 (hipBLASLt)46.8 GB112 tok/s6.2 tok/s

3. Multi-Model Co-Residency: Running Coding Agent + Vision OCR + Embeddings in 96GB

On a 24GB discrete GPU (RTX 3090/4090), loading a 32B coding model leaves zero room for a Vision OCR model (`qwen2.5vl:7b`) or an embedding model (`bge-m3`), forcing Ollama to unload and reload 20GB weights from disk on every workflow step. On Strix Halo’s 96GB pool, setting `OLLAMA_MAX_LOADED_MODELS=4` and `OLLAMA_KEEP_ALIVE=24h` allows `qwen3:30b-a3b-q8_0` (34.8 GB) + `qwen2.5-coder:32b-q4_K_M` (22.4 GB) + `qwen2.5vl:7b` (6.8 GB) + `bge-m3` (1.2 GB) = **65.2 GB** to stay pinned in unified memory simultaneously with zero disk thrashing.
[WARNING]Do not confuse the 50 TOPS XDNA 2 NPU with the Radeon 8060S iGPU: Ollama, llama.cpp, and vLLM run on the 40-CU GPU (256 GB/s), whereas the NPU is designed for low-power background audio/vision models under Ryzen AI Software.
FIELD Q&A // 常见实测疑问与边界解答

Frequently Asked Questions & Edge Cases

Q: How does a 128GB AMD Strix Halo Mini PC compare to a 128GB Apple Mac Studio (M4 Max) for local LLMs?

Mac Studio M4 Max has ~546 GB/s memory bandwidth (2.1x higher than Strix Halo’s ~256 GB/s), so dense 70B models run ~2.1x faster on Mac Studio (~13 tok/s vs ~6.2 tok/s). However, Strix Halo costs roughly half as much, runs native x86 Linux/Docker with GPU passthrough (unlike macOS Docker Desktop which lacks Metal passthrough), and hits 78+ tok/s on Sparse MoE models like `Qwen3-30B-A3B`.

Q: Should I set BIOS UMA Frame Buffer to 96GB or use `amdttm.pages_limit` on Linux?

If the machine is a dedicated LLM server on Kernel 6.16.9+, setting BIOS UMA Frame Buffer to 96GB is the simplest zero-flag option. If you also use the machine for heavy CPU compilation or VMs when LLMs are idle, keep BIOS UMA at Auto/4GB and set `amdttm.pages_limit=25165824` so the 96GB pool is shared dynamically between CPU and iGPU.

REPRODUCIBLE ARTIFACT // 配套可参数化附件

AMD Ryzen AI Max+ 395 (Strix Halo 128GB) GTT/TTM Memory & MoE Benchmark Script

Verifies Linux kernel amdttm.pages_limit / BIOS 96GB UMA allocation on gfx1151, sets ROCBLAS_USE_HIPBLASLT=1, and benchmarks Qwen3-30B-A3B MoE vs dense models.

tune-strix-halo-llm.sh ↗
Edit variables above to update the configuration live
#!/usr/bin/env bash
# Tinker AI — AMD Ryzen AI Max+ 395 (Strix Halo 128GB · gfx1151) UMA & LLM Verifier (Guide #21)
set -euo pipefail

MODEL="qwen3:30b-a3b-q4_K_M"
TARGET_PAGES="25165824"
CTX="32768"

echo "=== [1/4] Kernel, BIOS UMA & TTM Page Limit Audit ==="
uname -r
if [ -f /sys/module/amdttm/parameters/pages_limit ]; then
  CUR_PAGES=$(cat /sys/module/amdttm/parameters/pages_limit)
  CUR_GB=$(( CUR_PAGES * 4 / 1024 / 1024 ))
  echo "Current amdttm.pages_limit: $CUR_PAGES (~$CUR_GB GB addressable GTT pool)"
  if [ "$CUR_PAGES" -lt "$TARGET_PAGES" ]; then
    echo "[WARN] GTT pool is below target ($TARGET_PAGES pages). Add 'amdttm.pages_limit=$TARGET_PAGES amdttm.page_pool_size=$TARGET_PAGES' to GRUB_CMDLINE_LINUX_DEFAULT."
  fi
fi

echo "=== [2/4] Verifying Radeon 8060S (gfx1151) Visible VRAM ==="
if command -v rocminfo >/dev/null 2>&1; then
  rocminfo | grep -E "Name:\s+gfx|Size:" | head -n 6 || true
fi

echo "=== [3/4] Applying Strix Halo ROCm hipBLASLt & Flash Attention Optimizations ==="
export ROCBLAS_USE_HIPBLASLT=1
export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE=q8_0
echo "Exported ROCBLAS_USE_HIPBLASLT=1 OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0"

echo "=== [4/4] Benchmarking $MODEL at num_ctx=$CTX (seed=42) ==="
curl -sS http://127.0.0.1:11434/api/generate -d "{
  \"model\": \"$MODEL\",
  \"prompt\": \"/no_think Compare sparse Mixture-of-Experts (MoE) memory bandwidth utilization against dense 70B models on 256 GB/s unified memory in 3 bullet points.\",
  \"stream\": false,
  \"options\": { \"num_ctx\": $CTX, \"temperature\": 0, \"seed\": 42 }
}" | python3 -c "
import sys, json
d = json.load(sys.stdin)
ec = d.get('eval_count', 0)
ed = d.get('eval_duration', 1) / 1e9
pc = d.get('prompt_eval_count', 0)
pd = d.get('prompt_eval_duration', 1) / 1e9
print(f'Prefill Speed : {pc} tokens ({pc/pd:.1f} tok/s)')
print(f'Decode Speed  : {ec} tokens ({ec/ed:.2f} tok/s)')
"
ollama ps

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