Update documentation with AWQ+DFlash lessons learned and improved validation script
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# AWQ + DFlash on Ampere Hardware
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## Date: 2026-08-08
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## Objective
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Deploy Qwen3.6-35B-A3B with AWQ 4bit quantization and DFlash speculative decoding on NVIDIA Ampere-class GPU (24GB VRAM).
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## Scripts Created
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- `vllm-awq-qwen36-dflash.sh` — Docker-based AWQ + DFlash
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- `vllm-awq-qwen36-dflash-ampere.sh` — Systemd service, Ampere-optimized
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## Key Findings
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### vLLM CLI Changes (Nightly)
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- `--speculative-model` removed; use `--speculative-config` JSON instead
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- Format: `--speculative-config '{"method": "dflash", "model": "z-lab/Qwen3.6-35B-A3B-DFlash", "num_speculative_tokens": 4}'`
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- `num_speculative_tokens` is required when model is provided
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### Memory Issues
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- **OOM with 262144 context**: AWQ→Marlin weight conversion requires extra GPU memory
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- **Solution**: Lower to 32768 context temporarily
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- **CPU offload**: `--cpu-offload-gb 40` + `--cpu-offload-params "experts"` helps
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### Conflicts
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- **Prefix caching + Mamba cache**: Causes `AssertionError` in `unify_kv_cache_spec_page_size`
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- **DFlash speculative decoding**: KV cache assertion bug in nightly vLLM
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- Workaround: Disable speculative decoding or prefix caching
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### Performance Observations (no speculative decoding)
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| Streams | Prompt Speed | Completion Speed |
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|---------|-------------|------------------|
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| 1 | 4.27 tok/s | 21.42 tok/s |
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| 2 | 2.44 tok/s | 12.25 tok/s |
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| 3 | 1.35 tok/s | 6.76 tok/s |
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| 4 | 1.43 tok/s | 7.19 tok/s |
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Note: Speeds decrease with more streams due to KV cache and compute contention.
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## Systemd Service
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- Service: `vllm-awq-qwen36.service`
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- ExecStart: `/home/dank/vllm/vllm-awq-qwen36-dflash-ampere.sh`
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- Port: 34401
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- Status: Disabled (needs speculative decoding fix)
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## Next Steps
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1. Fix DFlash speculative decoding (upstream vLLM bug or workaround)
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2. Test with lower context length for full functionality
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3. Benchmark with and without speculative decoding
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4. Investigate `max_num_scheduled_tokens` warning (set to 1530 due to speculative settings)
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