CMP 170HX unlock guide: 64 GB HBM2e for $250-800

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What is the CMP 170HX?

• GPU: Ampere GA100 (74 SMs)
• Memory: 8 GB HBM2e (artificially capped)
• Bandwidth: ~732 GB/s (limited by capped memory)
• TDP: 250W
• Interface: PCIe 4.0 x16
• Display outputs: None
• Cooling: Passive (requires server chassis or custom fans)

• Memory: 64 GB HBM2e (full capacity)
• Bandwidth: 732-1,100 GB/s (varies by workload)
• All 74 SMs active
• Full CUDA support

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Unlock methods

1. Vix Loader (Windows, recommended): No-flashing unlock via registry keys. Fully reversible.
2. Linux kernel patching: For server deployments. Requires Proxmox VE or bare-metal Linux.

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Method 1: Vix Loader (Windows, no flashing)

• Windows 10/11 (64-bit)
• NVIDIA driver 616.56 or later
• CMP 170HX card
• Administrator privileges

1. Install NVIDIA driver 616.56 or later Download from NVIDIA’s driver portal. Do not use DDU (Display Driver Uninstaller) — the unlock relies on specific driver state.
2. Download Vix Loader Clone or download from Panchovix/nvidia-windows-vbios-vix-loader.
3. Run the Vix Loader script Open PowerShell as Administrator and run:
   .\vix-loader.ps1
   
   The script modifies registry keys to load the modified VBIOS at boot. It does not flash the card.
4. Reboot After reboot, the card reports 64 GB HBM2e. Verify with:
   nvidia-smi
   
   You should see:
   +-----------------------------------------------------------------------------+
   | NVIDIA-SMI 560.xx       Driver Version: 560.xx       CUDA Version: 12.6     |
   |-------------------------------+----------------------+----------------------+
   | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
   | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
   |===============================+======================+======================|
   |   0  CMP 170HX           Off  | 00000000:01:00.0 Off |                    0 |
   | N/A   45C    P0    50W / 250W |      0MiB / 65536MiB |      0%      Default |
   +-------------------------------+----------------------+----------------------+
   
5. Verify the unlock Run a CUDA sample or inference workload to confirm the full 64 GB is accessible:
   python -c "import torch; print(torch.cuda.get_device_properties(0).total_memory / 1e9, 'GB')"
   
   Should output: 64.xxxxx GB

.\vix-loader.ps1 --revert

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Method 2: Linux kernel patching (servers)

• Linux (Ubuntu 22.04 or 24.04 recommended)
• NVIDIA driver 550.xx or later
• Kernel headers and build tools
• CMP 170HX card

1. Install prerequisites
   sudo apt update
   sudo apt install build-essential linux-headers-$(uname -r) git
   
2. Install NVIDIA driver Download and install the latest NVIDIA driver from NVIDIA’s Linux driver portal. Do not use the nvidia-driver-xxx package from Ubuntu repos — you need the runfile installer.
   wget https://us.download.nvidia.com/XFree86/Linux-x86_64/560.xx/NVIDIA-Linux-x86_64-560.xx.run
   sudo sh NVIDIA-Linux-x86_64-560.xx.run
   
3. Clone the unlock repository
   git clone https://github.com/PixelML/club-170hx.git
   cd club-170hx
   
4. Apply the kernel module patch The community maintains patches for specific driver versions. Check the repo for the latest patch that matches your driver:
   ls patches/
   # Output: 550.120.patch 550.144.patch 560.28.patch ...
   
   Apply the patch:
   sudo ./apply-patch.sh 560.28
   
   This patches the NVIDIA kernel module to expose the full 64 GB.
5. Reboot and verify
   sudo reboot
   nvidia-smi
   
   Should show 65536 MiB (64 GB).

• Single-card setups
• 4x CMP 170HX in a single workstation
• 8x CMP 170HX in a mining rig chassis
• Proxmox VE passthrough

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Performance benchmarks

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Single-card performance

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Multi-card performance

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Comparison to other hardware

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Setup requirements

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Cooling

• Add a custom fan shroud (3D-printed or acrylic) to direct airflow over the heatsink
• Use a 92mm or 120mm fan mounted to the card’s heatsink
• Target: 20-30 CFM airflow over the card

• Use a mining rig chassis with integrated fans
• Or a 4U server chassis with high-CFM fans (e.g., Norco RPC-4224)
• Target: 50+ CFM per card, with intake/exhaust balance

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Power

• 1x 8-pin PCIe power connector (or 12VHPWR with adapter)
• 750W+ PSU recommended (headroom for CPU, storage, fans)

• 4x 8-pin PCIe power connectors (or 12VHPWR)
• 1,600W+ PSU recommended (4x 250W = 1,000W + headroom)
• Use a PSU with multiple 12V rails or a server PSU with breakout board

• 8x 8-pin PCIe power connectors
• 2,500W+ PSU (or dual PSUs)
• Mining rig chassis with integrated power distribution

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Chassis

• Standard ATX workstation case works
• Ensure adequate airflow (front intake, rear exhaust)
• Add custom fan shroud for directed cooling

• 4U server chassis (e.g., Rosewill RSV-L4411U, Norco RPC-4224)
• Or a workstation case with 4x full-length PCIe slots (e.g., Fractal Design Define 7 XL)
• Check PCIe slot spacing — some motherboards have slots too close for blower-style coolers

• Mining rig chassis (e.g., Antminer S9, or open-frame mining rigs)
• Or a rack-mount server with PCIe risers
• Requires PCIe bifurcation support on motherboard (x16 → 2x x8 or 4x x4)

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Software stack

• Linux (Ubuntu 22.04 or 24.04) for production serving
• Windows 10/11 for Vix Loader unlock method
• Proxmox VE for virtualized multi-card setups

• vLLM: Production-grade, concurrent batching, best for API serving
• SGLang: High-throughput, RadixAttention for prefix caching
• llama.cpp: CPU+GPU offloading, GGUF quantization, flexible
• Ollama: Simplest setup, single-user, good for experimentation

• Use tensor parallelism (TP=4 or TP=8) to split models across cards
• vLLM and SGLang support tensor parallelism natively
• PCIe bandwidth is the bottleneck — expect 10-20% overhead vs NVLink

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Common issues and troubleshooting

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Issue: Card still shows 8 GB after unlock

1. Verify driver version: nvidia-smi should show 616.56 or later
2. Re-run Vix Loader script with administrator privileges
3. Check Windows Event Viewer for errors
4. Try a different driver version (560.xx series is well-tested)

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Issue: Thermal throttling under load

1. Add a 92mm fan directly to the card’s heatsink
2. Increase chassis intake fan speed
3. Lower power limit: nvidia-smi -pl 200 (reduces TDP from 250W to 200W)
4. Undervolt the card (advanced): Use nvidia-smi --set-gpu-voltage-offset to reduce voltage

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Issue: PCIe bandwidth bottleneck in multi-card setup

1. Use fewer cards (2x instead of 4x) for better per-card throughput
2. Upgrade to a workstation/server motherboard with PCIe 5.0 (2x bandwidth)
3. Accept the 10-20% overhead — it’s still cheaper than NVLink solutions
4. Use pipeline parallelism instead of tensor parallelism (less inter-GPU communication)

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Issue: Out-of-memory (OOM) on large models

1. Use lower quantization (Q4 instead of Q8)
2. Reduce --max-model-len to limit KV cache size
3. Use --gpu-memory-utilization 0.90 to reserve 10% for overhead
4. Offload layers to CPU: --cpu-offload in llama.cpp

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Issue: NVIDIA patches the unlock in a future driver

1. Do not update drivers until the community confirms the unlock still works
2. Pin your driver version: sudo apt-mark hold nvidia-driver-xxx
3. If already updated, check the Panchovix repo or PixelML club-170hx for a new patch
4. As a last resort, revert to stock 8 GB mode (Vix Loader --revert)

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Is CMP 170HX right for you?

• You need 64+ GB VRAM on a budget ($250-800)
• You enjoy tinkering and accept the unsupported path
• You’re comfortable with Linux or Windows command-line setup
• You need HBM2e bandwidth for memory-bound workloads
• You’re building a multi-card inference server

• You need display outputs (use RTX 3090/4090 instead)
• You want a plug-and-play experience (use RTX 5090 or Mac Studio)
• You need warranty support and professional drivers (use RTX PRO 6000)
• You’re uncomfortable with the risk of NVIDIA patching the unlock
• You need low power consumption (250W per card is not efficient for small models)

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Next steps