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AMD Quark Quantization - IN PROGRESS

Current Status: ✓ RUNNING

AMD Quark FP8 quantization is now successfully running after fixing the ROCm environment.

What's Happening

[1/5] ✓ Loading tokenizer... DONE
[2/5] ⏳ Loading base model... DOWNLOADING (~50GB)
[3/5] ⏸ Preparing calibration data... PENDING
[4/5] ⏸ Quantizing model... PENDING (1-2 hours)
[5/5] ⏸ Saving quantized model... PENDING

Estimated completion: 1-2 hours from when download finishes

Environment Setup Summary

Problem: Quantization Tools Broke ROCm Environment

All attempted quantization tools (AutoAWQ, llm-compressor, GPTQModel) have hard CUDA dependencies that replaced ROCm PyTorch with CUDA versions.

Solution: Isolated Python 3.10 Venv + AMD Quark

  1. Restored main venv (torch313-rocm):

    • PyTorch 2.10.0.dev20250926+rocm6.3
    • Python 3.13.12
    • AMD 7900 XTX detected ✓

  2. Created quantization venv (quark-quantization):

    • Python 3.10.12 (Quark requires 3.10-3.12)
    • PyTorch 2.5.1+rocm6.2
    • AMD Quark 0.11
    • Transformers 5.1.0

Why AMD Quark?

  • AMD-specific optimization: Designed for AMD GPUs with ROCm
  • FP8 support: Native AMD format (better than AWQ for ROCm)
  • No CUDA dependencies: Won't break ROCm environment
  • Active development: Latest release 0.11 (Jan 2026)

Technical Details

Quantization Config

  • Base model: zenlm/zen-coder-flash (coding-optimized GLM-4.7-Flash)
  • Quantization: FP8 (8-bit floating point, AMD-optimized)
  • Calibration: 128 samples from wikitext-2
  • Output: models-glm-4.7-quark-fp8/

What FP8 Provides

  • Model size: ~7-8GB (down from 50GB FP16)
  • Quality loss: Minimal (<2% with FP8)
  • Speed: 2-3x faster inference on AMD GPUs
  • VRAM usage: Should easily fit in 24GB with KV cache

Monitoring Progress

Check quantization log:

tail -f quantize_quark.log

Or check background task output:

tail -f /tmp/claude-1000/-home-ljubomir-sglang-rocm-glm-4-7-flash/tasks/bb361da.output

Monitor GPU usage:

watch -n 5 rocm-smi

After Quantization Completes

1. Verify Output

ls -lh models-glm-4.7-quark-fp8/
du -sh models-glm-4.7-quark-fp8/

Expected size: ~7-8GB

2. Create Startup Script

cp run_sglang_8081_cyankiwi.sh run_sglang_8081_quark.sh
nano run_sglang_8081_quark.sh

Update to:

--model-path /home/ljubomir/sglang-rocm-glm-4.7-flash/models-glm-4.7-quark-fp8
--quantization fp8

3. Test Server

# Start server (use torch313-rocm venv, NOT quark-quantization)
source ~/python3-venv/torch313-rocm/bin/activate
./run_sglang_8081_quark.sh

4. Run Benchmarks

# In another terminal
python3 bench_comprehensive.py --base http://192.168.1.251:8081 --runs 3

Files Created

  • ~/python3-venv/quark-quantization/ - Isolated Python 3.10 venv for Quark
  • quantize_glm_quark.py - AMD Quark FP8 quantization script
  • quantize_quark.log - Quantization progress log
  • models-glm-4.7-quark-fp8/ - Output quantized model (pending)

If Quantization Fails

Common Issues

  1. OOM during quantization:

    • Model loads to GPU for quantization
    • Should fit in 24GB but might need tweaking
    • Solution: Add device_map="cpu" in script if needed

  2. Calibration data issues:

    • Wikitext might not be ideal for coding model
    • Solution: Switch to code-specific dataset

  3. Model incompatibility:

    • GLM-4 MoE Lite might have unsupported layers
    • Solution: Try base GLM-4.7-Flash instead of ZenCoder

Fallback Options

If Quark fails, you still have:

  1. Use QuantTrio with tiny context (known to work):

    ./run_sglang_8081.sh  # max-total-tokens=2048

  2. Search for pre-quantized models:

    • Look for recent GLM-4.7-Flash quantizations on HuggingFace
    • Filter by single-GPU compatibility

  3. Try different model:

    • Qwen2.5-Coder-7B-Instruct (excellent quantization support)
    • DeepSeek-Coder-V2-Lite-Instruct

Next Steps After Success

  1. ✓ Verify quantized model loads in SGLang
  2. Run comprehensive benchmarks
  3. Compare with llama.cpp baseline (~10 tok/sec)
  4. Test prefix caching effectiveness (run multiple times)
  5. Tune max_total_tokens based on VRAM usage
  6. Document performance results in TESTING.md

Sources