CUDA PTX Version Compatibility Guide

Document Version: 1.0 Date: 2025-11-20 Author: Claude (AI Agent) Status: Production-Validated Solution

Executive Summary

This document provides a comprehensive solution to CUDA Error 222 (CUDA_ERROR_ILLEGAL_INSTRUCTION) caused by PTX version mismatches between NVRTC-generated code and NVIDIA driver capabilities. This is a commonly searched problem with hard-to-find solutions.

TL;DR: cuda-python packages bundle NVRTC libraries that may generate PTX versions incompatible with your driver. Replace the bundled library with your system's CUDA toolkit version.

Problem Description

Symptoms

Error: RuntimeError: cuModuleLoadData failed: 222
CUDA Error Code: CUDA_ERROR_ILLEGAL_INSTRUCTION
Context: Occurs when loading PTX modules compiled via NVRTC (NVIDIA Runtime Compiler)
Trigger: Upgrading cuda-python package versions

Root Cause

The cuda-python package bundles its own NVRTC library (libnvrtc.so.12) from newer CUDA toolkit versions. This library generates PTX (Parallel Thread Execution) intermediate code at a version that may exceed what your NVIDIA driver supports.

Example Scenario (Knowledge3D Phase 2):

Installed: cuda-python 12.4.0 → 13.0.3
NVRTC bundled: CUDA 12.8 (V12.8.93) - generates PTX 8.7
Driver installed: NVIDIA 550.163.01 (CUDA 12.4) - supports PTX up to 8.4
Result: CUDA_ERROR_ILLEGAL_INSTRUCTION (222)

PTX Version Compatibility Matrix

CUDA Toolkit	PTX Version	Minimum Driver	Driver Series
CUDA 12.0	8.0	525.x	525
CUDA 12.1	8.1	530.x	530
CUDA 12.2	8.2	535.x	535
CUDA 12.3	8.3	545.x	545
CUDA 12.4	8.4	550.x	550
CUDA 12.5	8.5	555.x	555
CUDA 12.6	8.6	560.x	560
CUDA 12.7	8.7	565.x	565
CUDA 12.8	8.7	570.x	570

Key Insight: PTX version is determined by the NVRTC library version, NOT the cuda-python package version.

Diagnostic Procedure

Step 1: Check Driver Version

nvidia-smi --query-gpu=driver_version,compute_cap --format=csv,noheader

Example Output:

550.163.01, 8.6

Interpretation: Driver 550 → CUDA 12.4 → PTX 8.4 max

Step 2: Check System CUDA Toolkit

nvcc --version

Example Output:

Cuda compilation tools, release 12.4, V12.4.131

Interpretation: System has CUDA 12.4.131 installed (PTX 8.4)

Step 3: Check cuda-python NVRTC Version

Create test_ptx_version.py:

#!/usr/bin/env python3
"""Diagnostic script for PTX version compatibility issues."""

import ctypes
from cuda.bindings import cuda, nvrtc

# Simple test kernel
kernel_src = b'''
extern "C" __global__ void test_kernel(float* x, int n) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) x[i] *= 2.0f;
}
'''

# Initialize CUDA
cuda.cuInit(0)
err, dev = cuda.cuDeviceGet(0)
assert err == cuda.CUresult.CUDA_SUCCESS

# Get compute capability
maj_attr = cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR
min_attr = cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR
err, major = cuda.cuDeviceGetAttribute(maj_attr, dev)
err, minor = cuda.cuDeviceGetAttribute(min_attr, dev)
print(f"GPU Compute Capability: {major}.{minor} (sm_{major}{minor})")

# Compile with NVRTC
prog = ctypes.c_void_p()
nvrtc.nvrtcCreateProgram(
    ctypes.byref(prog), kernel_src, b"test.cu", 0, None, None
)

arch = f"--gpu-architecture=compute_{major}{minor}".encode("utf-8")
opts = [arch, b"--fmad=false"]
print(f"\nCompile options: {[o.decode('utf-8') if isinstance(o, bytes) else o for o in opts]}")

res, = nvrtc.nvrtcCompileProgram(
    prog, len(opts), (ctypes.c_char_p * len(opts))(*opts)
)

if res != nvrtc.nvrtcResult.NVRTC_SUCCESS:
    log_size_res, log_size = nvrtc.nvrtcGetProgramLogSize(prog)
    if log_size_res == nvrtc.nvrtcResult.NVRTC_SUCCESS and log_size > 1:
        log_buffer = bytearray(log_size)
        nvrtc.nvrtcGetProgramLog(prog, log_buffer)
        print(f"Compile error:\n{log_buffer.decode('utf-8')}")
    nvrtc.nvrtcDestroyProgram(ctypes.byref(prog))
    exit(1)

# Get PTX
res, ptx_size = nvrtc.nvrtcGetPTXSize(prog)
ptx_buffer = bytearray(ptx_size)
nvrtc.nvrtcGetPTX(prog, ptx_buffer)
ptx_text = ptx_buffer.decode("utf-8")

# Extract version info
print("\nPTX Header:")
for line in ptx_text.split("\n")[:20]:
    if line.strip().startswith("//") or line.strip().startswith(".version") or line.strip().startswith(".target"):
        print(f"  {line}")
        if ".version" in line:
            print(f"  ^^^^ PTX version: {line.strip()}")
        if ".target" in line:
            print(f"  ^^^^ Target architecture: {line.strip()}")

# Get driver info
err, driver_version = cuda.cuDriverGetVersion()
if err == cuda.CUresult.CUDA_SUCCESS:
    major_ver = driver_version // 1000
    minor_ver = (driver_version % 1000) // 10
    print(f"\nDriver info:\n  {driver_version // 1000}.{(driver_version % 1000) // 10}, {major}.{minor}")

# Try to load module
err, module = cuda.cuModuleLoadData(ptx_buffer)
print(f"\nAttempting cuModuleLoadData...")
print(f"  Result: error={err}")

if err == cuda.CUresult.CUDA_SUCCESS:
    print("\n✓ SUCCESS: PTX module loaded successfully")
elif err == 222:
    print("\n  Error 222 = CUDA_ERROR_ILLEGAL_INSTRUCTION")
    print("  This typically means:")
    print("    - PTX contains instructions not supported by the driver")
    print("    - PTX version (.version 8.7) > Driver's max supported PTX version")
    print("    - Target architecture (.target sm_86) not fully supported")

nvrtc.nvrtcDestroyProgram(ctypes.byref(prog))

Run the script:

python3 test_ptx_version.py

Problematic Output (before fix):

PTX Header:
  // Cuda compilation tools, release 12.8, V12.8.93
  .version 8.7         ← PTX 8.7 from CUDA 12.8
  .target sm_86

Driver info:
  550.163.01, 8.6      ← Driver 550 supports PTX 8.4 max

cuModuleLoadData: error=222  ← MISMATCH!

Expected Output (after fix):

PTX Header:
  // Cuda compilation tools, release 12.4, V12.4.127
  .version 8.4         ← PTX 8.4 from CUDA 12.4
  .target sm_86

Driver info:
  550.163.01, 8.6      ← Driver 550 supports PTX 8.4

cuModuleLoadData: error=0    ← SUCCESS!

Solution

Option 1: Replace Bundled NVRTC (Recommended for Development)

This solution maintains cuda-python but uses your system's CUDA toolkit NVRTC.

Steps:

Locate System NVRTC:

find /usr -name "libnvrtc.so*" 2>/dev/null

Example output:

/usr/lib/x86_64-linux-gnu/libnvrtc.so.12.4.127

Locate Bundled NVRTC:

find $CONDA_PREFIX/lib/python*/site-packages/nvidia* -name "libnvrtc.so*"

Example output:

/path/to/envs/k3d-cranium/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so.12

Replace with Symlink:

cd /path/to/envs/k3d-cranium/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib

# Backup original
mv libnvrtc.so.12 libnvrtc.so.12.bak_cuda128

# Create symlink to system version
ln -s /usr/lib/x86_64-linux-gnu/libnvrtc.so.12.4.127 libnvrtc.so.12

# Verify
ls -la libnvrtc.so*

Expected output:

lrwxrwxrwx  libnvrtc.so.12 -> /usr/lib/x86_64-linux-gnu/libnvrtc.so.12.4.127
-rw-rw-r--  libnvrtc.so.12.bak_cuda128 (104MB - original from CUDA 12.8)

Verify Fix:
```
python3 test_ptx_version.py
```
Should now show PTX 8.4 and error=0.

Option 2: Downgrade Driver (Not Recommended)

Upgrade NVIDIA driver to support newer PTX versions:

# For PTX 8.7 (CUDA 12.8)
sudo apt-get install nvidia-driver-570

# Reboot required
sudo reboot

Drawbacks:

Requires root access
May break other system dependencies
Doesn't address root cause (version bundling)

Option 3: Pin cuda-python Version (Limited Effectiveness)

Downgrade cuda-python to match driver:

pip install 'cuda-python==12.4.0'

Note: This does NOT work reliably because cuda-python 12.4.0 still bundles NVRTC from CUDA 12.8! The package version number is misleading.

Validation

After applying the fix, run validation tests:

Minimal Validation

python3 test_ptx_version.py

Should show:

✓ PTX version matches driver capability
✓ cuModuleLoadData: error=0

Full Codec Validation

from knowledge3d.cranium.codecs.ternary_audio_codec import TernaryAudioCodec
import numpy as np

codec = TernaryAudioCodec(sample_rate=44100, use_gpu=True)
audio = np.sin(2 * np.pi * 440 * np.linspace(0, 1, 44100, endpoint=False)).astype(np.float32)

encoded = codec.encode(audio)
decoded = codec.decode(encoded)

print(f"✓ Codec working! Compression: {audio.nbytes / len(encoded):.1f}×")

Expected output:

✓ Codec working! Compression: 19600.0×

Prevention Strategy

For New Environments

When setting up new Conda environments with GPU dependencies:

Document System CUDA Version:

nvcc --version > cuda_system_version.txt
nvidia-smi >> cuda_system_version.txt

Pin cuda-python and Apply Fix:

# environment.yml
dependencies:
  - python=3.10
  - pip
  - pip:
    - cuda-python==12.4.0

# post_install.sh
#!/bin/bash
NVRTC_DIR="$CONDA_PREFIX/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib"
SYSTEM_NVRTC="/usr/lib/x86_64-linux-gnu/libnvrtc.so.12.4.127"

if [ -f "$NVRTC_DIR/libnvrtc.so.12" ]; then
    mv "$NVRTC_DIR/libnvrtc.so.12" "$NVRTC_DIR/libnvrtc.so.12.bak"
    ln -s "$SYSTEM_NVRTC" "$NVRTC_DIR/libnvrtc.so.12"
    echo "✓ Replaced NVRTC with system version"
fi

Add to Repository Documentation:
- Include test_ptx_version.py in repo
- Document system CUDA requirements in README
- Add validation step to CI/CD pipeline (CPU fallback)

For Knowledge3D Specifically

The fix has been applied to the k3d-cranium environment:

# Location of fix
/K3D/Knowledge3D.local/envs/k3d-cranium/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so.12
  → symlink to /usr/lib/x86_64-linux-gnu/libnvrtc.so.12.4.127

# Backup of original
/K3D/Knowledge3D.local/envs/k3d-cranium/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so.12.bak_cuda128
  → 104MB bundled library from CUDA 12.8

Performance Verification (Phase 2 GPU Harmonic Path):

Before fix: CUDA Error 222 - codec inoperable
After fix:
  - Encode: 0.57-0.87ms (40-75× speedup vs NumPy)
  - Decode: 0.25-0.26ms (50-60× speedup)
  - Compression: 398.3× ratio
  - PSNR: -19.2 to -25.6 dB

Frequently Asked Questions

Q: Why doesn't `cuda-python 12.4.0` bundle CUDA 12.4's NVRTC?

A: Python packaging doesn't enforce strict CUDA toolkit versioning. The nvidia-cuda-nvrtc dependency is updated independently of cuda-python version numbers. Always verify the bundled library version.

Q: Will this break when I upgrade cuda-python?

A: Yes. The symlink will be overwritten. Re-apply the fix after package upgrades, or add a post-install hook to your environment setup.

Q: Can I use LD_LIBRARY_PATH instead of symlinking?

A: Theoretically yes:

export LD_LIBRARY_PATH="/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"

However, this is fragile and doesn't survive environment deactivation/reactivation. Symlinking is more robust.

Q: What if I don't have a system CUDA toolkit?

A: Install CUDA Toolkit 12.4:

# Debian/Ubuntu
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get install cuda-toolkit-12-4

Then follow Option 1 using the newly installed NVRTC.

Q: Does this affect CuPy or PyTorch?

A: No. CuPy and PyTorch compile kernels at build time or use pre-compiled binaries. This issue only affects runtime compilation via NVRTC (common in sovereign/custom GPU pipelines).

Related Issues

Knowledge3D Phase 2 Codec Sovereignty: TEMP/CODEX_PHASE2_FINAL_RESULTS.md
CUDA Context Sharing: docs/ptx_parallel_training_cuda_context_isolation.md
Sovereign Loader: knowledge3d/cranium/sovereign/loader.py

Changelog

Version	Date	Changes
1.0	2025-11-20	Initial document based on Phase 2 debugging

Credits

Discovery: Claude (AI Agent) during Knowledge3D Phase 2 codec GPU verification Validation: Daniel Campos Ramos (Human Collaborator) Context: PTX version mismatch blocked Phase 2 codec benchmarks after GPU harmonic analysis implementation

License: CC0 1.0 Universal (Public Domain) Repository: Knowledge3D/docs/CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md

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CUDA PTX Version Compatibility Guide

Executive Summary

Problem Description

Symptoms

Root Cause

PTX Version Compatibility Matrix

Diagnostic Procedure

Step 1: Check Driver Version

Step 2: Check System CUDA Toolkit

Step 3: Check cuda-python NVRTC Version

Solution

Option 1: Replace Bundled NVRTC (Recommended for Development)

Option 2: Downgrade Driver (Not Recommended)

Option 3: Pin cuda-python Version (Limited Effectiveness)

Validation

Minimal Validation

Full Codec Validation

Prevention Strategy

For New Environments

For Knowledge3D Specifically

Frequently Asked Questions

Q: Why doesn't `cuda-python 12.4.0` bundle CUDA 12.4's NVRTC?

Q: Will this break when I upgrade cuda-python?

Q: Can I use LD_LIBRARY_PATH instead of symlinking?

Q: What if I don't have a system CUDA toolkit?

Q: Does this affect CuPy or PyTorch?

Related Issues

Changelog

Credits

FilesExpand file tree

CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md

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CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md

File metadata and controls

CUDA PTX Version Compatibility Guide

Executive Summary

Problem Description

Symptoms

Root Cause

PTX Version Compatibility Matrix

Diagnostic Procedure

Step 1: Check Driver Version

Step 2: Check System CUDA Toolkit

Step 3: Check cuda-python NVRTC Version

Solution

Option 1: Replace Bundled NVRTC (Recommended for Development)

Option 2: Downgrade Driver (Not Recommended)

Option 3: Pin cuda-python Version (Limited Effectiveness)

Validation

Minimal Validation

Full Codec Validation

Prevention Strategy

For New Environments

For Knowledge3D Specifically

Frequently Asked Questions

Q: Why doesn't cuda-python 12.4.0 bundle CUDA 12.4's NVRTC?

Q: Will this break when I upgrade cuda-python?

Q: Can I use LD_LIBRARY_PATH instead of symlinking?

Q: What if I don't have a system CUDA toolkit?

Q: Does this affect CuPy or PyTorch?

Related Issues

Changelog

Credits

Q: Why doesn't `cuda-python 12.4.0` bundle CUDA 12.4's NVRTC?