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.
71 lines
2.5 KiB
C++
71 lines
2.5 KiB
C++
# Tunables for frank-default-small (Tiel-Coder-35B-A3B UD-Q6_K_XL,
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# qwen35moe hybrid MoE, Dankserver, RTX 3090).
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#
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# Runtime: upstream llama.cpp (latest master, CUDA build with
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# -DGGML_CUDA_FA_ALL_QUANTS=ON -DCMAKE_CUDA_ARCHITECTURES=86) on RTX 3090
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# 24 GB; Ryzen 7 7700X; 62 GB RAM. Replaced the ik_llama.cpp Ornith
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# CRACK Q8_0 deployment on 2026-09-01.
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#
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# Model: peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF, UD-Q6_K_XL quant
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# (30 GB). Architecture: qwen35moe — 40 layers, full_attention_interval=4
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# (10 attention + 30 SSM/Mamba), 256 experts / 8 used, 2 KV heads × 256
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# dim, embedding 2048, rope dimension_count 64, rope freq_base 10 000 000.
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# Text-only (no mmproj). Built-in chat template in the GGUF.
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#
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# Expert offload: --cpu-moe keeps all 256 expert weights in CPU RAM.
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# Non-expert weights (~1.6 GB) fit entirely on the 24 GB card.
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#
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# KV cache: Q8_0 for both K and V. With CTX_SIZE=200700 (66 900 per slot
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# × 3 parallel) and 10 attention layers, the KV cache is ~2 GB — the
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# requested 2 GB VRAM buffer.
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#
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# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE. The
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# ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
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# incident). This box is NVIDIA/CUDA, but the rule keeps the env portable.
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# Model file (UD-Q6_K_XL, 30 GB, text-only MoE hybrid).
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MODEL_DIR=/home/dank/models/tiel-coder-35b
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MODEL_FILE=Tiel-Coder-35B-A3B-UD-Q6_K_XL.gguf
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# Upstream llama.cpp llama-server (CUDA build, latest master).
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# Built with -DGGML_CUDA_FA_ALL_QUANTS=ON -DCMAKE_CUDA_ARCHITECTURES=86
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# using the staged CUDA 13.2 toolkit at /home/dank/cuda13-stage.
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LLAMA_SERVER=/home/dank/llama.cpp/build/bin/llama-server
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ALIAS=frank-default-small
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=7770
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# Total context budget across all parallel slots. 200700 / 3 = 66 900
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# per slot. The model's context_length is 262 144, so 66 900 is well
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# within range.
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CTX_SIZE=200700
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PARALLEL=3
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# Expert offload: all MoE expert weights pinned to CPU RAM.
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CPU_MOE=1
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# All non-expert layers on GPU (auto-fit will determine the exact count;
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# with --cpu-moe the non-expert footprint is ~1.6 GB, leaving ample room).
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N_GPU_LAYERS=999
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# KV cache: Q8_0 for both K and V. ~2 GB total for 200700 ctx × 3 slots
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# × 10 attention layers × (2 KV heads × 256 dim × 1 byte) × 2 (K+V).
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CACHE_TYPE_K=q8_0
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CACHE_TYPE_V=q8_0
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VERBOSITY=2
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# Prefill batch sizing.
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BATCH_SIZE=4096
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UBATCH_SIZE=1024
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# Sampling.
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TEMPERATURE=1.0
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TOP_K=20
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TOP_P=0.95
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MIN_P=0.0
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# Compute threads lowered so other processes keep a core.
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THREADS=7
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THREADS_HTTP=8 |