GLM-5.2-NVFP4-REAP-469B serving on SM120 (4× RTX PRO 6000 Blackwell) — one-command vLLM launch recipe, 250K context, DeepSeek Sparse Attention + MTP speculative decode
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Jun 19, 2026 - Shell
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rtx-pro-6000
Here are 13 public repositories matching this topic...
From-scratch C++/CUDA inference engine for the NVIDIA RTX 5090 (sm_120a) — the best single-GPU backend for agentic AI: tool calling, long-context loops, reasoning and concurrent sub-agents on top of the fastest single-stream decode on the 5090 (beats llama.cpp, at-or-ahead of vLLM on NVFP4). 100% written by Claude Code.
cpp cuda quantization inference-engine mixture-of-experts blackwell llm safetensors llama-cpp vllm local-llm llm-inference qwen gguf fp4 rtx-5090 sm120 nvfp4 rtx-pro-6000 gated-deltanet
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Jul 7, 2026 - Cuda
Optimized vLLM setup for Qwen3.6-27B-FP8 on dual RTX PRO 6000 Blackwell (192 GB GDDR7, no NVLink) ; config, benchmark sweep results, and custom chat template with thinking mode off by default.
benchmark blackwell fp8 vllm local-llm llm-inference speculative-decoding qwen3 multi-token-prediction rtx-pro-6000
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May 10, 2026 - Shell
Systematic 24-hour benchmark study of Qwen3.6-27B inference on dual NVIDIA RTX PRO 6000 Blackwell SM120 (TP=2). 8 experiments comparing repne/vllm fork vs upstream vLLM across FP8/BF16/NVFP4/Q8_0 quants and MTP/DFlash speculative decoding. Peak: 2,083 tok/s at c=32. Quality: KLD vs BF16 = 0.0018 (noise floor).
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Jun 3, 2026 - Python
Production-grade FlashAttention FP8 e4m3 forward kernel for NVIDIA Blackwell consumer GPUs (sm_120a, e.g. RTX PRO 6000). 647–652 TFLOPS at hd=128, sl=8192. Multi-kernel dispatcher, C library with Go and Python bindings
cuda transformer attention gpu-kernels tensor-cores blackwell fp8 flash-attention fp8e4m3 sm120 rtx-pro-6000 sm-120a
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Jun 12, 2026 - Cuda
Hub for ongoing Qwen inference benchmarks on NVIDIA Blackwell. Indexes all studies, hosts the rolling SOTA leaderboard, points to the toolchain.
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May 15, 2026 - Python
Deploy the GLM-5.2-469B model on four RTX PRO 6000 Blackwell GPUs using a turnkey vLLM Docker configuration to enable high-speed sparse attention and inference.
android open-source benchmark ocr smartthings smartapp moe glm zwave gles2 reap vllm chatglm-6b long-horizon rtx-pro-6000
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Jul 7, 2026 - Shell
Fish Audio OpenAudio S2-Pro on vLLM-Omni. low-latency ~100ms TTFA, OpenAI-compatible, runs on NVIDIA Blackwell (RTX 5090 / RTX PRO 6000). Self-hosted streaming TTS & voice cloning.
text-to-speech streaming real-time cuda self-hosted tts speech-synthesis low-latency openaudio voice-cloning fastapi blackwell openai-api vllm fish-speech rtx-5090 rtx-pro-6000 elevenlabs-alternative
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Jun 27, 2026 - Python
QuantLoom·量梭 的野心，从不只是在手机上弹出几条信号。 这座织机真正要为你织出的终极产物，是 RTX Pro 6000 —— 黑曜神机 的自由召唤权。 它是躺在你机箱里的黑色方尖碑，数万核心如暗夜星海 它是本地训推大模型、实时织造全市场量能全景图、回溯十年资金指纹的物质根基 它过去只降落在超算中心、顶级量化基金和神秘矿场 QuantLoom 每织出一匹盈利的锦缎，都是在为这座黑色圣坛添一根金线。当金线积聚成缆，黑曜神机便会从虚空货架撕开一道裂缝，降临在你的阵中。 从此，你拥有了一座个人算力神殿。
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May 20, 2026 - Python
Forge a reproducible code/LLM eval leaderboard on idle TU Delft DAIC RTX Pro 6000 (Blackwell, sm_120) GPUs — sibling of preCal. Strict GPU-generate / CPU-score decouple, requeue-safe SLURM backfill, published to the HF Hub.
benchmarking leaderboard slurm code-generation reproducibility tu-delft huggingface vllm llm-evaluation rtx-pro-6000
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Jun 19, 2026 - Python
Stress-validation of Qwen3.6-27B inference configurations on dual RTX PRO 6000 Blackwell. 5 configs x 4 phases (gates, throughput matrix, HumanEval, MBPP) = 2,105 hard coding problems, zero crashes. Headline: FP8+MTP=3 wins HumanEval (79.3%), BF16+DFlash wins MBPP (89.5%). MTP=5 dominated on correctness despite faster raw tok/s.
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May 7, 2026 - Python
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