Frank
6efb0fb231
Replace the llama.cpp Tiel-Coder-35B-A3B (UD-Q6_K_XL) deployment with FreeToken v0.1.2 — an edge-native MoE serving engine that keeps expert weights in host RAM with an LRU GPU cache and paged KV with radix prefix sharing. Model: Qwen/Qwen3.6-35B-A3B-FP8 (modelopt block-fp8, 128×128 weight_block_size). Architecture: qwen3_5_moe — 40 layers (10 attention + 30 GDN), 256 experts/8 used. Production config: - 2048 MoE cache slots (20% of 10240 expert instances, ~6.0 GiB VRAM) - 453005 KV tokens (~8.6 GiB, ~20 KiB/token for 10 attn layers) - 4 max-running-requests (mamba slot pool: 24 slots) - bs4 decode: ~87 tok/s aggregate, ~30 tok/s bs1 - Prefill: ~1,130 tok/s at 4k prompt Quirk: FreeToken v0.1.2 supports modelopt block-fp8, NVFP4, and per-tensor FP8, but NOT compressed-tensors per-channel FP8 (Huihui/Ornith both failed). Live KV resize via ft ctl cache grows KV by shrinking MoE; reverse needs restart. Old llama.cpp scripts archived to .AGENTTRASH/. Full benchmark sweep (bs1/bs3/bs4/bs6, 8k–700k KV) documented in docs/LLM/freetoken-benchmark-sweep.md.
17 lines
444 B
C++
17 lines
444 B
C++
[Unit]
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Description=llama.cpp Tiel-Coder-35B-A3B Q6_K_XL qwen35moe hybrid MoE (dankserver, frank-default-small)
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After=network-online.target
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Wants=network-online.target
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[Service]
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Type=simple
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User=dank
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Group=dank
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Environment="FRANK_SMALL_ENV_FILE=/home/dank/serverAppConfigs/scripts/frank-default-small.env"
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ExecStart=/home/dank/serverAppConfigs/scripts/serve-frank-small.sh
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Restart=on-failure
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RestartSec=10
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[Install]
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WantedBy=multi-user.target
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