HN · GitHub Signals

OPEN SOURCE, IN FOCUS

glm-5.3-flash-exl3-2x-dgx-sparks-tensorfold

miaai-lab/glm-5.3-flash-exl3-2x-dgx-sparks-tensorfold

GLM-5.3-Flash EXL3 on 2x DGX Spark with TensorFold

Explore on GitHub ↗

Saved projects stay in this browser. No account needed.

TAKE A CLOSER LOOK

Start with the source.

The repository is the place to check installation steps, supported platforms, the license, and current maintenance before choosing a tool.

Read the documentation on GitHub ↗

Keep exploring

Find projects in the same areas, or search the archive for a different approach.

ANOTHER GOOD FIND

Explore related projects

View the archive ↗

Connected by topics and descriptions.

jayleaton

glm53-tensorfold-spark

GLM-5.3-Flash (abliterated EXL3) on 2x NVIDIA DGX Spark with the TensorFold engine: 1.8x faster decode than vLLM, 4x256k concurrent threads, byte-exact speculative decoding. Work in progress.

Take a look →
0xbakeer

qwen3.8-27b-4-bit-on-a-single-dgx-spark

Serving 4-bit Qwen3.8-27B on a single DGX Spark (GB10): 75 tok/s single-stream, 246 tok/s aggregate at 8-way concurrency. NVFP4 vs MixedInt4-AutoRound vs the FP8 baseline, measured on one harness — including why the quantization advantage collapses to +0.2% by c16.

Take a look →
0xbakeer

qwen3.8-27b-fp8-on-a-single-dgx-spark

Serving Qwen3.8-27B-FP8 on a single DGX Spark (GB10): 7.88 to 58.5 tok/s single-stream from decode strategy alone, weights untouched. Speculative decoding and prefix caching benchmarked, plus DFlash 2 — the only Qwen3.8-27B build that can serve it under vLLM.

Take a look →