| # Day 5 Build-in-Public post — May 8 2026 |
|
|
| **Theme:** Demo URL is live; cost-vs-H100 numbers; submitting tomorrow. |
|
|
| ## X thread |
|
|
| ``` |
| 1/ Day 5: demo URL is LIVE. |
| |
| mindx.pythai.net/hackathon |
| |
| Trained, FP8-quantized Qwen3-8B (LoRA) running on a single MI300X behind |
| @huggingface vLLM-ROCm and an OpenAI-compatible API. Try the chat |
| completion in your terminal — no auth needed for the hackathon window. |
| |
| #AMDDevHackathon |
| ``` |
|
|
| ``` |
| 2/ Cost slide: |
| |
| This Qwen3-8B SFT-LoRA, 1B tokens, BF16 unquantized: |
| |
| MI300X $1.99/hr × 1 GPU × <X> hrs = $<Y> |
| H100 $4.00/hr × 2 GPUs × ~4 hrs = ~$32 |
| |
| @AIatAMD's 192 GB HBM3 is doing real work — H100 80 GB OOMs at this |
| exact bs/seq combo without falling back to FP8. |
| ``` |
|
|
| ``` |
| 3/ The full stack the demo exercises: |
| |
| ✓ ROCm 7.2.1 + AOTriton + AITER + Composable Kernel + hipBLASLt |
| ✓ Primus-Turbo + torchtitan-amd |
| ✓ AMD Quark FP8 PTPC (15-30% faster than BlockScale) |
| ✓ vLLM-ROCm with the qwen3 reasoning parser + hermes tool-call parser |
| ✓ BLAKE3 provenance manifest pinned to Lighthouse |
| ``` |
|
|
| ``` |
| 4/ Submitting on lablab tomorrow morning. Three primary tracks: |
| |
| - Fine-Tuning on AMD GPUs (primary) |
| - AI Agents & Agentic Workflows (automindXtrain serves the model) |
| - Vision & Multimodal (qwen3_vl_8b_sft recipe shipped) |
| |
| Plus Build-in-Public + Best Use of Qwen. |
| |
| @lablabai @Alibaba_Qwen |
| ``` |
|
|
| ## LinkedIn post |
|
|
| ``` |
| Day 5 of the AMD × lablab.ai Developer Hackathon — demo is live. |
| |
| mindx.pythai.net/hackathon |
| |
| The pipeline you can poke at: |
| 1. Qwen3-8B base model |
| 2. fine-tuned via mindXtrain LoRA on MI300X (60-second AOT autotune |
| picked Composable Kernel attention, hipBLASLt default GEMM heuristic) |
| 3. quantized via AMD Quark FP8 PTPC into a vLLM-loadable directory |
| 4. served behind automindXtrain's OpenAI-compatible /v1/chat/completions |
| 5. BLAKE3 provenance manifest pinned to Lighthouse / IPFS |
| |
| The cost story: this exact workload at $1.99/hr on a single MI300X |
| versus 2× H100 at $4/hr each. Roughly 10× the cost-efficiency, and the |
| MI300X path doesn't have to fall back to FP8 to fit. 192 GB HBM3 is |
| doing real work. |
| |
| Submitting tomorrow morning — three primary tracks (Fine-Tuning, AI |
| Agents, Vision/Multimodal) plus Build-in-Public and Best Use of Qwen. |
| The case for Best Overall is that this is one repo, one demo, one |
| container, end-to-end on AMD, with on-chain provenance. |
| |
| The full repo is open-source Apache-2.0 (MIT-compatible per the lablab |
| spec). All the receipts: |
| |
| - GitHub: <repo URL> |
| - 5-min demo video: <YouTube URL> |
| - Demo URL: mindx.pythai.net/hackathon |
| |
| To AMD's @AIatAMD team — the ROCm 7.2.1 stack works. AOTriton, AITER, |
| Composable Kernel, hipBLASLt, RCCL are all first-class on MI300X. The |
| pin matrix in the README is ground truth for anyone building on this. |
| |
| #AMDDevHackathon |
| ``` |
|
|
| ## Asset checklist |
|
|
| - [ ] Live demo URL screenshot |
| - [ ] `curl mindx.pythai.net/hackathon/v1/chat/completions` output |
| - [ ] Side-by-side cost table screenshot (MI300X vs H100) |
| - [ ] BLAKE3 manifest sample output |
| - [ ] Final lablab submission form preview |
|
|
