• Apple M3 Ultra vs RTX 5090 – The Final Battle – equivalent on small models, but M3 Ultra is better on big models like DeepSeek R1 Distill Llama 70B
• Comparison of M3 Ultra & RTX 5090 32GB & RTX Pro 6000 96GB has an outcome that it is better to build RTX Pro 6000 system rather than Apple M3 Ultra for $10000 (price of M3 Ultra wit 512GB of RAM)
• Wire eight DGX Sparks into a 1TB “VRAM” cluster – one would need a 4400Gbps switch and QSFP56 cables (~€65).
• Running LLM on AMD "Strix Halo" AI Ryzen MAX+ 395 (HP Z2 G1a Mini Workstation) – Linux tuning and software configuration
  • Building a Framework Desktop cluster with AMD's Ryzen AI Max+ 395
• M4 Mac Mini cluster connected via Thunderbolt.
• Comparison of Jetson Thor, DGX Spark, and Apple Silicon M4 Pro all having unified 128GB of RAM with 273GB/s memory bandwidth.
• Stress-testing the NVIDIA DGX Spark, Dell Pro Max GB10, ASUS Ascent GX10, and MSI Edge Expert all running the same Grace Blackwell (GB10) chip and 128GB of unified RAM.

Name	Price	CPU	GPU	Memory	Storage	Display ports	Extension slots	Connectivity	Wireless	Dimensions	Max TFLOPS	Tokens/sec (120B Q4)	Tokens / €	Tokens / W	Clustering
Framework Desktop	€3591	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	1TB, 2× NVMe PCIe 4.0 ×4 M.2 2280, max 8TB each	1× HDMI v2.1, 2× DisplayPort v1.4 (8K@60Hz)	1× PCIe 4.0 ×4 slot (max 50GbE)	RJ45 5Gbit (Realtek RTL8126), 2× USB4-C, 2× USB-A 3.2 Gen 2	WiFi 7 (AMD RZ717)	123.7×123.1×54.6mm	~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14	Either Ethernet (bad), or adding 50GbE QSFP28 PCIe adapter + cables
Bosgame M5 AI	€2080 (pre-order $1699)	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	2TB, 2× NVMe PCIe 4.0 ×4 M.2 2280, max 8TB each	1× HDMI v2.1, 1× DisplayPort v1.4 (8K@60Hz)		RJ45 2.5Gbit, 2× USB4-C, 3× USB-A 3.2 Gen 2, 2× USB-A 2.0, SD card	WiFi 7, Bluetooth 5.2 (in M.2 2230 key-E PCIe 3.0 slot)		~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14
GEEKOM A9 Mega AI Mini PC	$3200 / €3500 (kickstarter $1899)	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	2TB, 2× NVMe PCIe 4.0 ×4 M.2 2280, max 8TB each	2× HDMI v2.1 (8K@60Hz)		2× RJ45 2.5Gbit, 2× USB4-C (support Display Port 2.1), 2× USB-C, 3× USB-A 3.2 Gen 2, SD card	WiFi 7, Bluetooth 5.4 (in M.2 2230 key-E PCIe 3.0 slot)	171×171×71mm	~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14
GMKtec EVO-X2	€3000	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	2TB, 2× NVMe PCIe 4.0 ×4 M.2 2280, max 8TB each	1× HDMI v2.1, 1× DisplayPort v1.4 (8K@60Hz)		RJ45 2.5Gbit, 2× USB4-C, 3× USB-A 3.2 Gen 2, 2× USB-A 2.0, SD card	WiFi 7, Bluetooth 5.4 (in M.2 2230 key-E PCIe 3.0 slot)	193×185.8×77mm	~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14
Beelink GTR9 Pro	€3000	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	2TB, 2× NVMe PCIe 4.0 ×4 M.2 2280, max 8TB each	1× HDMI v2.1 (8K@60Hz)		2× RJ45 40Gbit, 3× USB4-C, 2× USB-A 3.2 Gen 2, 2× USB-A 2.0, SD card	WiFi 7 (MT7925), Bluetooth 5.4 (in M.2 2230 key-E PCIe 3.0 slot)	180×180×90.8mm	~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14	Maybe 2×40Gbit = 10MB/s
FEVM FA-EX9	€2950	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	2TB, NVMe PCIe 4.0 ×4 + x1 M.2 2280, max 8TB each	1× HDMI v2.1, 1× DisplayPort v1.4 (8K@60Hz)	Oculink 64Gb/s	RJ45 2.5Gbit, 2× USB4-C, 3× USB-A 3.2 Gen 2, 2× USB-A 2.0, SD card	WiFi 7 (MT7925), Bluetooth 5.3 (in M.2 2230 key-E PCIe 3.0 slot)	192×190×55mm	~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14	Oculink
MS-S1 MAX	€3120	AMD Ryzen™ AI Max+ 395, 3.0GHz, 16 cores	AMD Radeon™ 8060S, 2.9GHz, 20 compute units	128GB LPDDR5x, 8000 MT/s	2TB, NVMe PCIe 4.0 ×4 + x1 M.2 2280, max 8TB each	1× HDMI v2.1 (8K@60Hz)	1× PCIe 4.0 ×4 slot	2× RJ45 10Gbit (Realtek RTL8127), 2× USB4-C (40Gb/s, Display Port 2.0, PD out 15W), 2× USB4-C v2 (80Gb/s, Display Port 2.0, PD out 15W), 3× USB-A 3.2 Gen 2, 2× USB-A 2.0, SD card	WiFi 7 (MT7925), Bluetooth 5.4 (in M.2 2230 key-E PCIe 3.0 slot)	192×190×55mm	~50–60 FP16	12–15	0.0055–0.0075	0.10–0.14	See above using MCX416A-CCAT Mellanox ConnectX-4 2× 50Gbit/s QSFP28 ($146)

• More Ryzen AI Max+ 395 mini PCs with 128GB are now available… Check other models
• MSI AI Edge mini-PC based on AMD Ryzen AI Max+ 395 and 128 GB LPDDR5X RAM launching soon

NVidia chipset:

• Rubin has 50 petaflops of FP4, up from 20 petaflops in Blackwell

• Nvidia Tesla V100 в работе и играх – сравнение Tesla V100 с 3090, 4090, 5090 по TFLOPS, Что может серверная Tesla V100 в играх? – установка самых последних драйверов на NVidia Tesla V100 PG503.
• Cheap GMKtec Mini PC with 96GB RAM runs a 70B LLM in Intel Core Ultra CPU using Intel® LLM Library (discontinued), but checkout Geekom A9 Max with AMD Ryzen AI Max runs 120B LLM

• LM Studio – run AI models, locally and privately
• VRAM Calculator for Local Open Source LLMs or try LLM Inference Hardware Calculator
• "speculative decoding" is a technique to dramatically boost Large Language Model speed using a smaller model drafts ahead, while a larger model verifies

• Add new openclaw user: useradd -m -u 987 -s /bin/bash openclaw && mkdir -m 700 /home/openclaw && chown openclaw:openclaw /home/openclaw
• Make sure that gateway.auth.token in /home/openclaw/.openclaw/openclaw.json matches the one in docker-compose.yml (openclaw-gateway.environment.OPENCLAW_GATEWAY_TOKEN).
• Start/stop the container: docker compose up -d openclaw-gateway / docker compose down
• To approve device (OpenClaw UI) request:

  # docker compose exec openclaw-gateway bash
  
  $ /app/openclaw.mjs devices list
  Direct scope access failed; using local fallback.
  Pending (1)
  ┌──────────────────────────────────────┬───────────────────────────────────────────────────┬──────────┬───────────────┬──────────┬────────┐
  │ Request                              │ Device                                            │ Role     │ IP            │ Age      │ Flags  │
  ├──────────────────────────────────────┼───────────────────────────────────────────────────┼──────────┼───────────────┼──────────┼────────┤
  │ 4ec7a9d0-f5f2-4316-9c07-f9e4fa96620a │ f74fdd392ee3bca006e344a86554ef76b473d7e2bd8502fde │ operator │               │ just now │        │
  └──────────────────────────────────────┴───────────────────────────────────────────────────┴──────────┴───────────────┴──────────┴────────┘
  
  $ /app/openclaw.mjs devices approve 4ec7a9d0-f5f2-4316-9c07-f9e4fa96620a
  Direct scope access failed; using local fallback.
  Approved f74fdd392ee3bca006e344a86554ef76b473d7e2bd8502fde (4ec7a9d0-f5f2-4316-9c07-f9e4fa96620a)

• If you get the following error Control UI requires gateway.controlUi.allowedOrigins (set explicit origins), or set gateway.controlUi.dangerouslyAllowHostHeaderOriginFallback=true to use Host-header origin fallback mode then add the following to .openclaw/openclaw.json:

  "gateway": {
    "controlUi": {
      "allowedOrigins": [
        "http://localhost"
      ]
    }
  }

and force that host in Apache configuration:

RequestHeader set Origin "http://localhost:18789"

• To workaround OpenClaw error “Proxy headers detected from untrusted address. Connection will not be treated as local.” add the following to Apache configuration:

  ProxyAddHeaders Off

• To force OpenClaw to bind to localhost instead of *, make sure that `OPENCLAW_GATEWAY_BIND` is not set ([github>openclaw/openclaw/blob/3e72c0352dde84a0bcb3aabafa99c2d4b12d1c46/docker-compose.yml#L34C10-L37|remove lines that configure –bind and –port]]) and configure those via .openclaw/openclaw.json

  "gateway": {
    "port": 18789,
    "mode": "local",
    "bind": "loopback"
  }

  Then check logs:

  openclaw-gateway-1  | 2026-04-05T17:55:40.225+00:00 [canvas] host mounted at http://127.0.0.1:18789/__openclaw__/canvas/ (root /home/node/.openclaw/canvas)
  openclaw-gateway-1  | 2026-04-05T17:55:40.296+00:00 [gateway] listening on ws://127.0.0.1:18789, ws://[::1]:18789 (PID 7)

• To add extras to original docker image do:

  # cat - > Dockerfile
  FROM alpine/openclaw:2026.4.2
  
  USER root
  
  RUN apt-get update -q \
   && apt-get install -y -q --no-install-recommends \
    python3-bs4 \
    python3-dateutil \
    python3-lxml \
    python3-requests \
    python3-yaml \
    && rm -rf /var/lib/apt/lists/* /var/cache/apt/archives/*
  
  USER openclaw
  
  # docker build -t openclaw-custom:2026.4.2 .

  then use openclaw-custom:2026.4.2 in docker-compose.yaml.

• Add to docker-compose.yml:

  services:
    ollama:
      image: ollama/ollama:latest
      user: "987:1001"
      network_mode: host
      volumes:
        - /home/openclaw/.ollama:/.ollama

• Pull LLM model (~3GB) and run it:

  # docker compose up -d ollama
  # docker compose exec ollama ollama pull llama3.1:8b-instruct-q4_K_M
  # docker compose exec ollama ollama run llama3.1:8b-instruct-q4_K_M

• Configure OpenClaw to use it so that configuration in .openclaw/openclaw.json looks like this:

  "models": {
    "providers": {
      "vllm": {
        "baseUrl": "http://127.0.0.1:11434/v1",
        "apiKey": "dummy-api-key",
        "api": "openai-completions",
        "models": [
          {
            "id": "ollama/llama3.1:8b-instruct-q4_K_M",
            "name": "ollama/llama3.1:8b-instruct-q4_K_M",
            "reasoning": false,
        ...

• Add extra directory mappings:

      volumes:
        - /home/openclaw/.openclaw:/home/node/.openclaw
        - /home/openclaw/.openclaw/.agent-browser:/home/node/.agent-browser
        - /home/openclaw/.openclaw/.cache:/home/node/.cache

• Run:

  ~/.openclaw$ ./bin/agent-browser install

• Test:

  ~/.openclaw$ ./bin/agent-browser open google.com

• To configure add this to .openclaw/openclaw.json:

  ~/.openclaw$ ./bin/agent-browser open google.com

• To re-run onboarding, do docker compose run --rm openclaw-cli onboard or like below.
• To switch the model, run docker compose exec openclaw-gateway /app/openclaw.mjs config
• To list all model, run

  # docker compose exec openclaw-gateway /app/openclaw.mjs models list
  
  Model                                      Input      Ctx      Local Auth  Tags
  openrouter/stepfun/step-3.5-flash:free     text       250k     no    yes   default,configured,alias:OpenRouter
  openai/gpt-5.1-codex                       text+image 391k     no    yes   fallback#1,configured,alias:GPT

• When you get a paring request from a bot:

  OpenClaw: access not configured.
  Your Telegram user id: 30528162
  Pairing code: Z5RQ92MX

  you need to run

  # docker compose exec openclaw-gateway /app/openclaw.mjs pairing approve telegram Z5RQ92MX
  Approved telegram sender 30528162.