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

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.

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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.

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aeon-7

vllm-ultimate-dgx-spark

AEON vLLM Ultimate — vLLM 0.24.0 built from source for DGX Spark / Blackwell (sm_121a/GB10). One image serves the whole AEON fleet (Gemma-4-26B-A4B, Qwen3.6-27B, Qwen3.6-35B-A3B) with DFlash speculative decoding, NVFP4 weights + Triton NVFP4/FP8 KV cache, high-concurrency DFlash fix, UMA cudagraph clamp, FlashInfer 0.6.12.

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syv-ai

hyperqwen

Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.

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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.

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