A light llama-like llm inference framework based on the triton kernel.
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Updated
Aug 26, 2026 - Python
A light llama-like llm inference framework based on the triton kernel.
Reproducible recipe: serve abliterated Gemma-4-12B (gemma4_unified) at 50-118 tok/s on no-NVLink Blackwell (SM120) via vLLM nightly + ModelOpt FP8/NVFP4 + MTP spec-decode.
Reproducible kit to deploy DeepSeek-V4-Flash-DSpark on a 2× NVIDIA DGX Spark (GB10) cluster: vLLM TP=2 over QSFP 200GbE, NVFP4 KV, DSpark speculative decoding, 1M context, systemd self-heal. Apache-2.0.
Patches + recipe to deploy festr2/MiMo-V2.5-Pro-NVFP4-MXFP8-attn-TP8 on 8-node DGX Spark sm_121 (Ray + vLLM, TP=8). Fixes the fused-qkv loader bug that mis-slotted Q values as K/V on 7 of 8 ranks.
Field-tested guide: multi-GPU vLLM tensor-parallel (TP=2/TP=4) on Intel Arc Pro B70 (Battlemage BMG-G31, Xe2) on Linux. Driver setup (xe force_probe=e223), bare-metal vLLM + oneAPI 2025.3, the compute-runtime multi-root USM + triton-xpu init_devices fixes, FP8/int4-AutoRound quant, root-cause error reports. AI-agent readable (AGENTS.md).
A ~800-line PyTorch implementation of Megatron-LM's TP + PP + DP + AMP. 1.6-2x faster than Megatron-Core on 125M models.
Deploy DeepSeek-V4-Flash-0731 on dual NVIDIA RTX PRO 6000 Blackwell GPUs with vLLM PR #41834 (jasl fork) and DSpark speculative decoding, achieving ~200-227 tok/s in no-overseas-network environments.
Serve an abliterated Gemma-4-12B at high speeds on Blackwell GPUs without NVLink using vLLM, FP8 quantization, and MTP speculative decoding.
Reproducible llama.cpp kernel and runtime optimization lab for dual NVIDIA Tesla V100 GPUs (SM70)
Two-node DGX Spark operator tooling and measurement harness for DeepSeek-V4-Flash
Serve DeepSeek-V4-Flash-0731 on 2x NVIDIA GB10 / DGX Spark (sm_121) with vLLM TP=2 over 200GbE RoCE — runbook, community survey, tuning, and an L1-L3 benchmark suite.
From-scratch C++/SYCL LLM inference engine for Intel Arc (B-series) — 8+ architectures, tensor-parallel, beats llama.cpp on Arc B70
Measured LLM benchmarks for NVIDIA DGX Spark (GB10): DeepSeek-V4-Flash 284B MoE on a TP=2 pair over 200G RoCE — tok/s by profile and concurrency, 1M-token context curve, the MoE backend flag, monitoring traps. Every number links to raw runs.
Deployment kit: Thinking Machines Inkling-Small-NVFP4 (276B MoE) on 2x NVIDIA DGX Spark — TP=2 over QSFP, vLLM 0.26, FA4 SM120 kernel fixes, full docs + bench harness. C1 21.4 / C8 93.9 tok/s measured.
Lightweight terminal launcher and auto-optimizer for llm models using llama.cpp with hardware detection, tensor sharding, benchmarks, presets, context tuning, and OpenAI-compatible serving.
Run GLM-5.3-Flash-NVFP4 (320B/18B active, NoPE sparse MLA, 131K ctx) across two NVIDIA DGX Sparks at TP=2. Adds the FlashInfer (32,2176) sparse-MLA specialization that TP=2 needs. Tested on real hardware.
Will a Hugging Face LLM fit your GPU or DGX? Plan weights, KV cache or recurrent state, context, TP/DP, and measured calibration
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