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turing-multi-gpu-llm-server/scripts/start-server.sh
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#!/bin/bash
# llama-server launcher for Qwen3.8-27B on Turing GPUs (RTX 2060 12GB + CMP 50HX 10GB)
# Built with NVIDIA NCCL for hardware-accelerated multi-GPU tensor parallelism.
# Accelerated with HauhauCS FastMTP 32K draft sidecar and official thinking parameters.
export LD_LIBRARY_PATH=/opt/llama.cpp-xrip/build-nccl/bin:/opt/minicpm-venv/lib/python3.13/site-packages/nvidia/nccl/lib:/lib/x86_64-linux-gnu
# Low-Latency NCCL & CUDA Driver Pipeline Optimizations
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NCCL_BUFFSIZE=2097152
export NCCL_NET_GDR_LEVEL=0
export NCCL_P2P_DISABLE=0
export NCCL_ALGO=RING
export NCCL_PROTO=SIMPLE
# Dynamically target the 12GB RTX 2060 for mmproj and FastMTP draft model
RTX_DEV=$(/opt/llama.cpp-xrip/build-nccl/bin/llama-cli --list-devices | grep -i "RTX 2060" | awk '{print $1}' | tr -d ':')
RTX_DEV=${RTX_DEV:-CUDA0}
exec /opt/llama.cpp-xrip/build-nccl/bin/llama-server \
-m /opt/models/gguf/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf \
--spec-draft-model /opt/models/gguf/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-FastMTP-32K.gguf \
--spec-draft-device "$RTX_DEV" \
--spec-draft-ngl all \
--spec-type draft-mtp \
--spec-draft-n-max 3 \
--spec-draft-p-min 0 \
--mmproj /opt/models/gguf/mmproj-Qwen3.8-27B-Uncensored-f16.gguf \
--mmproj-device "$RTX_DEV" \
--image-min-tokens 1024 \
--temp 1.0 \
--top-k 20 \
--top-p 0.95 \
--min-p 0 \
--presence-penalty 0 \
--repeat-penalty 1.0 \
--reasoning on \
--reasoning-effort xhigh \
--reasoning-preserve \
--reasoning-format deepseek \
--numa isolate \
-ngl 99 \
-c 131072 \
--parallel 1 \
--cache-prompt \
--cache-reuse 64 \
--cache-idle-slots \
--slot-prompt-similarity 0.10 \
--cache-ram 32768 \
--slot-save-path /var/cache/llama-slots \
--split-mode tensor \
--flash-attn on \
--batch-size 2048 \
--ubatch-size 1024 \
--jinja \
--threads 12 \
--cache-type-k q5_0 \
--cache-type-v q5_0 \
--host 0.0.0.0 \
--port 8080