| # Day 1 Build-in-Public post — May 4 2026 |
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| **Theme:** Why MI300X for sovereign cognition, and the framework I'm shipping for the hackathon. |
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| ## X thread (≤280 chars per tweet) |
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| ``` |
| 1/ Day 1 of the AMD × @lablabai Developer Hackathon. Shipping mindXtrain — the |
| first one-command Qwen3 fine-tuner native to MI300X. 192 GB HBM3 means BF16 8B |
| fits with headroom and 32B fits at all. H100 80 GB just OOMs. |
| |
| #AMDDevHackathon @AIatAMD |
| ``` |
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| ``` |
| 2/ The differentiator: a 60-second AOT autotune probe. Before each run, a |
| micro-benchmark picks Composable Kernel vs Triton attention, hipBLASLt |
| heuristics, and RCCL config. Static plan, no JIT autotune in production. |
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| Nothing else in the @huggingface ecosystem ships this. |
| ``` |
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| ``` |
| 3/ Day-1 deliverables green: |
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| ✓ uv workspace, 64 Python files, 12 Qwen3 recipes |
| ✓ 27 tests passing (CPU-only) |
| ✓ Pydantic schema enforces MI300X invariants (xGMI gotcha, MoE gate frozen) |
| ✓ Foundry contracts for write-once provenance anchoring |
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| GitHub: <repo URL when public> |
| ``` |
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| ``` |
| 4/ Hero workload target on Qwen3-8B / 1× MI300X / BF16: |
| - >15 000 tok/s |
| - MFU >40% |
| - time-to-loss-1.5 <90 min |
| - total cost <$3 (vs ~$32 on 2× H100) |
| |
| Cost slide writes itself. @Alibaba_Qwen |
| ``` |
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| ## LinkedIn post (long-form) |
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| ``` |
| Day 1 of the AMD × lablab.ai Developer Hackathon — shipping mindXtrain, a |
| one-command Qwen3 fine-tuner native to AMD MI300X. |
| |
| Why MI300X for this specific work: 192 GB HBM3 means a Qwen3-8B BF16 LoRA |
| job at bs=8 seq=4096 fits with massive headroom on a single GPU. The same |
| workload on H100 80 GB requires either quantization or splitting across |
| two cards. At AMD Developer Cloud's $1.99/hr versus H100 list of $4/hr, |
| the same 1B-token training run lands at $3 versus $32. 4× cheaper. |
| |
| The differentiator isn't the model or the dataset — it's the 60-second |
| AOT autotune probe that runs before each training job. It picks |
| Composable Kernel vs Triton SDPA based on measured timings, picks the |
| hipBLASLt heuristic for the run's shape, and locks in the NCCL channel |
| count. Static plan, written to disk, consumed at training start. No JIT |
| autotune in production — full reproducibility. |
| |
| Day 1 status: |
| ✓ uv workspace with 3 packages (automindXtrain → mindXtrain → custmodel) |
| ✓ 64 Python files, 12 Qwen3 recipes, 27 tests passing on CPU |
| ✓ Pydantic schema enforces MI300X invariants (1- or 8-GPU FSDP, MoE |
| gate frozen, AOT-only autotune policy) |
| ✓ Foundry contracts for write-once provenance anchoring (no proxy, |
| no admin keys) |
| ✓ Full doc hub under docs/ |
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| Heading to the AMD Developer Cloud now to provision the MI300X droplet |
| for Day 2's autotune probes. The hard part — making Composable Kernel |
| and Triton race head-to-head and capturing the wow-moment for the demo |
| video — starts tomorrow. |
| |
| #AMDDevHackathon |
| ``` |
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| ## Asset checklist |
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| - [ ] Screenshot of `uv run mindxtrain init --list` output |
| - [ ] Screenshot of `uv run pytest -q` showing 27 passed |
| - [ ] Screenshot of the `mindxtrain.tuned.yaml` from a dry-run bench |
| - [ ] Repo URL once public |
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