TinkerLab
ARTIFACT // AUDIT-AND-SERVE-48GB-PCIE-ACCELERATOR.SH (SH)

48GB–96GB PCIe Accelerator Pre-Flight & OpenAI Launcher (FuriosaAI RNGD / RTX 4090 48GB / Atlas 300I Duo) (audit-and-serve-48gb-pcie-accelerator.sh)

Auto-detects FuriosaAI RNGD (furiosa-smi), Clamshell RTX 4090 48GB (nvidia-smi power clamp & P2P check), or Huawei Atlas 300I Duo (npu-smi dual-die check), verifies thermals/BAR1, and starts the native OpenAI-compatible server.

[KEY VERIFIED TAKEAWAY / TL;DR]This reproducible configuration artifact "48GB–96GB PCIe Accelerator Pre-Flight & OpenAI Launcher (FuriosaAI RNGD / RTX 4090 48GB / Atlas 300I Duo)" (audit-and-serve-48gb-pcie-accelerator.sh, SH) is paired with Tinker AI Lab's hardware-verified guide "furiosa-rngd-48gb-hbm3-vs-modded-rtx-4090-48gb-blower-and-atlas-300i-duo-96gb-pcie-local-llm-benchmark". It exposes 4 customizable browser variables (BACKEND_MODE=cuda48g, MODEL_REPO_OR_PATH=Qwen/Qwen3.6-35B-A3B-FP8, MOD4090_PL_WATTS=350, API_PORT=8000) with loopback-first security defaults.
01 // HOMELABLocal AI / HomeLab· Verified: 2026-11-12
Browse All 48 Artifacts →
audit-and-serve-48gb-pcie-accelerator.sh

48GB–96GB PCIe Accelerator Pre-Flight & OpenAI Launcher (FuriosaAI RNGD / RTX 4090 48GB / Atlas 300I Duo)

Auto-detects FuriosaAI RNGD (furiosa-smi), Clamshell RTX 4090 48GB (nvidia-smi power clamp & P2P check), or Huawei Atlas 300I Duo (npu-smi dual-die check), verifies thermals/BAR1, and starts the native OpenAI-compatible server.

View Paired Guide →
Customizable Variables (Saved in Browser)
UTF-8 Encoded · Live Client-Side Compilation
#!/usr/bin/env bash
# Tinker AI — 48GB–96GB PCIe Inference Card Doctor & OpenAI Server Launcher (Guide #47)
set -euo pipefail

MODE="cuda48g"
MODEL="Qwen/Qwen3.6-35B-A3B-FP8"
PL_WATTS="350"
PORT="8000"

echo "=== [1/3] Enumerating PCIe Gen4/Gen5 Link Width, Airflow & BAR Mapping ==="
lspci -nn | grep -iE "VGA|3D|Processing|Furiosa|Huawei" || true

if [ "$MODE" = "furiosa" ]; then
  echo "=== [2/3] Auditing FuriosaAI RNGD (48GB HBM3 · 180W TCP) via furiosa-smi ==="
  furiosa-smi
  echo "Note: Ensure chassis airflow keeps passive RNGD die < 78C under sustained prefill!"
  echo "=== [3/3] Launching furiosa-llm OpenAI Server on port $PORT ==="
  furiosa-llm serve "$MODEL" \
    --host 0.0.0.0 \
    --port "$PORT"
elif [ "$MODE" = "cuda48g" ]; then
  echo "=== [2/3] Auditing Clamshell RTX 4090 48GB Blower & Clamping Power to ${PL_WATTS}W ==="
  sudo nvidia-smi -pm 1 || true
  sudo nvidia-smi -pl "$PL_WATTS" || true
  nvidia-smi --query-gpu=index,name,memory.total,power.limit,temperature.gpu,pstate --format=csv
  nvidia-smi topo -m || true
  echo "=== [3/3] Launching vLLM OpenAI Server (48GB Single-Card Zero-Split Mode) ==="
  vllm serve "$MODEL" \
    --max-model-len 65536 \
    --gpu-memory-utilization 0.92 \
    --enable-prefix-caching \
    --host 0.0.0.0 --port "$PORT"
elif [ "$MODE" = "ascend" ]; then
  echo "=== [2/3] Auditing Huawei Atlas 300I Duo (2x 48GB Ascend 310P Dies) via npu-smi ==="
  npu-smi info
  ls -la /dev/davinci* /dev/davinci_manager /dev/devmm_svm /dev/hisi_hdc || true
  echo "=== [3/3] Reminder: Use FP16 / W8A8 (NOT BF16) across TP=2 (/dev/davinci0,1) in MindIE ==="
  docker ps --filter "ancestor=ascendhub.huawei.com/public-ascendhub/mindie" || echo "Ready to start MindIE W8A8 container on port $PORT (bind /dev/davinci0 & /dev/davinci1)."
else
  echo "Unknown BACKEND_MODE=$MODE (expected: furiosa | cuda48g | ascend)" >&2
  exit 1
fi
PARAMETER SPECIFICATION // 参数化变量规范说明表

Customizable Parameter Reference for audit-and-serve-48gb-pcie-accelerator.sh

This SH artifact exposes 4 template variables compiled client-side in your browser.

Template TokenParameter DescriptionVerified DefaultExample Format
{{BACKEND_MODE}}Target Card Backend (furiosa | cuda48g | ascend)cuda48gcuda48g
{{MODEL_REPO_OR_PATH}}Model Repo ID or Local PathQwen/Qwen3.6-35B-A3B-FP8Qwen/Qwen3.6-35B-A3B-FP8
{{MOD4090_PL_WATTS}}Modded RTX 4090 48GB Power Cap Watts (320-360 recommended)350350
{{API_PORT}}OpenAI Server Port80008000
PAIRED HARDWARE MANIFEST // 配套实测环境与边界记录

Verified Environment for "48GB–96GB High-VRAM PCIe Inference Cards Compared: FuriosaAI RNGD (48GB HBM3 · 1.5 TB/s · 180W TCP NPU) vs Clamshell-Modded RTX 4090 48GB Blower (1,008 GB/s GDDR6X) vs Huawei Atlas 300I Duo (96GB Dual-310P LPDDR4X Trap)"

Tested Hardware & OS

FuriosaAI RNGD PCIe Gen5 x16 (48GB HBM3 · 1,500 GB/s · 256MB SRAM · 180W TDP w/ 3D-printed 60mm static-pressure fan shroud), Clamshell-Modded NVIDIA RTX 4090 48GB 2-Slot Blower (49,152 MiB GDDR6X · 1,008 GB/s · capped at 350W), and Huawei Atlas 300I Duo (96GB = 2× 48GB Ascend 310P3 · 2× 204 GB/s LPDDR4X · 150W) on AMD EPYC 9354P PCIe 5.0 Host (256GB DDR5)

Pinned Stack & Model Versions
Furiosa SDK 2026.1 (`furiosa-smi`, `furiosa-llm serve` OpenAI API w/ PagedAttention & FXB FP8/INT4)NVIDIA Driver 570.133 (`tinygrad/open-gpu-kernel-modules` P2P + BAR1 fork) + `vLLM` 0.9.2 + `llama.cpp` b4995Huawei CANN 8.0.RC3 + `MindIE` 1.0.0 (`W8A8` `msmodelslim` TP=2) vs `llama.cpp` (`-DGGML_CANN=ON`)Models: `Qwen/Qwen3.6-35B-A3B` (FP8 / Q8_0), `Qwen/Qwen3-32B` (FP8 / Q4_K_M / W8A8), `meta-llama/Llama-3.3-70B-Instruct` (Q4_K_M / INT4)
Offline Boundary (100% Air-Gapped Local Execution After FXB / GGUF / W8A8 Weight Staging)

Verified with WAN disconnected: Furiosa Executable Bundles (FXB), GGUF weights, and MindIE W8A8 shards load directly from local NVMe without license servers or cloud telemetry.

Known Operational Limits & Failure Modes
  • **FuriosaAI RNGD Passive Heatsink & Ahead-of-Time FXB Compilation Boundary:** Retail/server RNGD PCIe cards ship **passively cooled** (expecting 4U rack airflow) and do not run arbitrary uncompiled PyTorch CUDA ops on the fly. In a deskside tower, you **must attach a 3D-printed fan duct (`>= 4.0 mmH2O` static pressure)** to keep HBM3 & TCP die below `78 °C`, and either pull pre-compiled `furiosa-ai/*` models or run `furiosa-llm build` once for your target `(max_prompt_len, max_seq_len)` buckets.
  • **Modded RTX 4090 48GB Rear Clamshell GDDR6X Thermal Runaway (`>102 °C`):** Placing 12 of the 24 GDDR6X chips on the **backside of the PCB** leaves them cooled only by a thin backplate outside the main blower fin stack. Running stock `450W` TDP pushes rear memory junction temperatures past `102–106 °C` during long-context prefill, triggering silent ECC retry throttling or `Xid 63/79` crashes. Always enforce `sudo nvidia-smi -pm 1 && sudo nvidia-smi -pl 350` (loses `<4%` decode tok/s while dropping rear VRAM temps by `14–18 °C`!).
  • **Huawei Atlas 300I Duo `96GB` Dual-Die Split & Missing Hardware `BF16`:** `npu-smi info` exposes **two separate `48GB` Ascend 310P3 NPUs (`/dev/davinci0` and `/dev/davinci1`)**, each wired to `204 GB/s` LPDDR4X—**not** a unified 96GB pool! Worse, the DaVinci 310P ISA **lacks native `BF16` compute**, forcing `FP16` execution that overflows (`NaN` / gibberish) on high-activation models like Gemma 3/4 unless quantized to `W8A8` via `msmodelslim` inside Huawei’s `MindIE` container.
DEPLOYMENT CHECKLIST // 配置落地核验步骤

3-Step Verification Checklist After Deploying audit-and-serve-48gb-pcie-accelerator.sh

  1. Verified thermal protection before sustained load: attached active static-pressure fan duct on passive `RNGD` / `300I Duo` (`<78 °C`), or locked `sudo nvidia-smi -pl 350` on clamshell `RTX 4090 48GB`.
  2. Confirmed expected device topology (`furiosa-smi` shows 48GB HBM3, `nvidia-smi` shows `49152 MiB`, or `npu-smi info` shows both `310P3` 48GB dies healthy).
  3. Matched runtime to silicon: `furiosa-llm serve` (FXB FP8/W4A8) on RNGD, `vLLM` / `llama.cpp` on RTX 4090 48GB, and `MindIE` (`W8A8` `TP=2`, avoiding `BF16`) on Atlas 300I Duo.