Fast LLM speculative inference server for consumer hardware.

Model compression toolkit engineered for enhanced usability, comprehensiveness, and efficiency.

DFlash & TurboQuant in llama.cpp with up to 3x faster generation and 7.5x more KV cache in same VRAM

Fully uncensored, capability-enhanced abliteration of Qwen3.6-27B. NVFP4 + z-lab DFlash speculative decoding (n=12) on the unified ghcr.io/aeon-7/aeon-vllm-ultimate:latest container, tuned for long-context draft acceptance on DGX Spark. 6 HF variants (BF16/NVFP4/MTP/MTP-XS), docker-compose, and QuickStart.

vLLM patcher for Qwen3.6 on consumer NVIDIA — Qwen3.6-35B-A3B-FP8 (192 tok/s, +68% over stock) + Qwen3.6-27B-int4-AutoRound + 256K context. 126 patches: TurboQuant k8v4 KV, MTP/DFlash spec-decode, FULL cudagraph, hybrid GDN streaming, structured boot summary, one-command installer, 1958 tests. v7.72.2.

DFlash vLLM for DGX Spark — Plug & Play Block-Diffusion Speculative Decoding

vLLM Qwen 3.6-27B (AWQ-INT4) + DFlash speculative decoding on AMD Strix Halo (gfx1151 iGPU, 128 GB UMA, ROCm 7.13). 24.8 t/s single-stream, vision, tool calling, 256K context, OpenAI-compatible, Docker. Matches DGX Spark FP8+DFlash+MTP at a third of the cost. No CUDA.

Local AI workstation — discover, run, chat, benchmark, and generate images from open-weight models. DFlash/DDTree speculative decoding, TurboQuant & TriAttention cache compression strategies, MLX + llama.cpp + vLLM + MTPLX backends.

llama.cpp fork optimized for NVIDIA DGX Spark / GB10 (Blackwell, SM 12.1) — TurboQuant weights + KV, NVFP4, DFlash MTP

Qwen3.6 27B × DFlash — 30-35 tok/s on NVIDIA DGX Spark (GB10) - LLama.Cpp

Experimental DDTree-on-vLLM research track for Qwen3.6 AEON Ultimate on DGX Spark / GB10.

Run Qwen 3.6-27B AWQ-INT4 models with DFlash speculative decoding on AMD Strix Halo hardware using vLLM for high-throughput inference.

GGUF-native DFlash speculative decoding runtime for local models

vLLM v0.21 + Gemma 4 DFlash + real thinking_token_budget enforcement on Blackwell (sm_120 / sm_121a)

Qwen3.6-27B BF16+DFlash 13-config parameter sweep on repne/vllm:v2. Stage A (3x3 buffer/graph) + Stage B (num_speculative_tokens) + Quality (HumanEval/MBPP). 7h, 421 problems, 195 cells.

CLI for building and testing DFlash-style speculative decoding draft models.

Study #4: FP8+MTP{3,5} speed on repne/vllm:v2 + max_tokens=8192 quality re-runs for BF16+DFlash n=8 and FP8+MTP=3. Follow-up to studies #2 and #3.

Same BF16+DFlash config on Repne fork vs upstream vLLM v0.20.1, dual RTX PRO 6000 Blackwell. Upstream's dflash collapses at long context (5-6x slower at 131K).

vLLM v0.21 + Qwen 3.6 DFlash + real thinking_token_budget enforcement on Blackwell (sm_120 / sm_121a)