feat: replace llama.cpp Tiel-Coder with FreeToken v0.1.2 serving Qwen3.6-35B-A3B-FP8
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.
This commit is contained in:
Frank
@@ -1,71 +1,51 @@
|
||||
# Tunables for frank-default-small (Tiel-Coder-35B-A3B UD-Q6_K_XL,
|
||||
# qwen35moe hybrid MoE, Dankserver, RTX 3090).
|
||||
# Tunables for frank-default-small (Qwen3.6-35B-A3B-FP8,
|
||||
# qwen3_5_moe hybrid MoE, Dankserver, RTX 3090).
|
||||
#
|
||||
# Runtime: upstream llama.cpp (latest master, CUDA build with
|
||||
# -DGGML_CUDA_FA_ALL_QUANTS=ON -DCMAKE_CUDA_ARCHITECTURES=86) on RTX 3090
|
||||
# 24 GB; Ryzen 7 7700X; 62 GB RAM. Replaced the ik_llama.cpp Ornith
|
||||
# CRACK Q8_0 deployment on 2026-09-01.
|
||||
# Runtime: FreeToken v0.1.2 (edge-native MoE serving engine).
|
||||
# Installed via uv into /home/dank/freetoken/.venv (CPython 3.12).
|
||||
# FreeToken keeps expert weights in host RAM with an LRU GPU cache;
|
||||
# KV is paged with radix prefix sharing. Only the 10 full-attention
|
||||
# layers page KV (~20 KiB/token); the 30 GDN layers use constant-size
|
||||
# per-sequence state (mamba slot pool).
|
||||
#
|
||||
# Model: peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF, UD-Q6_K_XL quant
|
||||
# (30 GB). Architecture: qwen35moe — 40 layers, full_attention_interval=4
|
||||
# (10 attention + 30 SSM/Mamba), 256 experts / 8 used, 2 KV heads × 256
|
||||
# dim, embedding 2048, rope dimension_count 64, rope freq_base 10 000 000.
|
||||
# Text-only (no mmproj). Built-in chat template in the GGUF.
|
||||
# Model: Qwen/Qwen3.6-35B-A3B-FP8 (HuggingFace safetensors, modelopt
|
||||
# block-fp8 quant, 128x128 weight_block_size, 35 GB on disk).
|
||||
# Architecture: qwen3_5_moe — 40 layers (10 attention + 30 GDN),
|
||||
# 256 experts / 8 used, 2 KV heads × 256 dim, full_attention_interval=4.
|
||||
#
|
||||
# Expert offload: --cpu-moe keeps all 256 expert weights in CPU RAM.
|
||||
# Non-expert weights (~1.6 GB) fit entirely on the 24 GB card.
|
||||
# Replaced the llama.cpp Tiel-Coder-35B-A3B (UD-Q6_K_XL) deployment on
|
||||
# 2026-09-02. The old serve script and env are in .AGENTTRASH/.
|
||||
#
|
||||
# KV cache: Q8_0 for both K and V. With CTX_SIZE=200700 (66 900 per slot
|
||||
# × 3 parallel) and 10 attention layers, the KV cache is ~2 GB — the
|
||||
# requested 2 GB VRAM buffer.
|
||||
# Pool sizing: GPU budget (~17.6 GiB after weights + fixed caches) is
|
||||
# split MoE-first. MOE_CACHE_SIZE pins resident expert slots; NUM_TOKENS
|
||||
# pins the KV pool. At 2048 slots / 453005 tokens the split is ~20%
|
||||
# residency (2048/10240) and ~8.6 GiB KV. Live resize via
|
||||
# `ft ctl cache --kv N --moe M` can grow KV by shrinking MoE without
|
||||
# restart (the reverse requires a restart).
|
||||
#
|
||||
# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE. The
|
||||
# ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
|
||||
# incident). This box is NVIDIA/CUDA, but the rule keeps the env portable.
|
||||
# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE.
|
||||
# The ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
|
||||
# incident). This box is NVIDIA/CUDA, but the rule keeps the env portable.
|
||||
|
||||
# Model file (UD-Q6_K_XL, 30 GB, text-only MoE hybrid).
|
||||
MODEL_DIR=/home/dank/models/tiel-coder-35b
|
||||
MODEL_FILE=Tiel-Coder-35B-A3B-UD-Q6_K_XL.gguf
|
||||
# Model directory (safetensors checkpoint, 35 GB).
|
||||
MODEL_DIR=/home/dank/models/qwen36-35b-a3b-fp8
|
||||
|
||||
# Upstream llama.cpp llama-server (CUDA build, latest master).
|
||||
# Built with -DGGML_CUDA_FA_ALL_QUANTS=ON -DCMAKE_CUDA_ARCHITECTURES=86
|
||||
# using the staged CUDA 13.2 toolkit at /home/dank/cuda13-stage.
|
||||
LLAMA_SERVER=/home/dank/llama.cpp/build/bin/llama-server
|
||||
# FreeToken venv (uv-managed CPython 3.12, ft CLI, flashinfer, torch+cu130).
|
||||
FT_VENV=/home/dank/freetoken/.venv
|
||||
CUDA_HOME_DIR=/home/dank/freetoken/.venv/lib/python3.12/site-packages/nvidia/cu13
|
||||
|
||||
ALIAS=frank-default-small
|
||||
BIND_ADDR=0.0.0.0
|
||||
LISTEN_PORT=7770
|
||||
|
||||
# Total context budget across all parallel slots. 200700 / 3 = 66 900
|
||||
# per slot. The model's context_length is 262 144, so 66 900 is well
|
||||
# within range.
|
||||
CTX_SIZE=200700
|
||||
PARALLEL=3
|
||||
# Expert residency: 2048 of 10240 total expert instances (20%).
|
||||
# Each slot ~3 MiB → ~6.0 GiB VRAM for the MoE cache.
|
||||
MOE_CACHE_SIZE=2048
|
||||
|
||||
# Expert offload: all MoE expert weights pinned to CPU RAM.
|
||||
CPU_MOE=1
|
||||
# KV pool: 453005 tokens (~8.6 GiB at ~20 KiB/token).
|
||||
# Covers 4 concurrent sessions at ~113k unique context each
|
||||
# (more with radix prefix sharing for common prefixes).
|
||||
NUM_TOKENS=453005
|
||||
|
||||
# All non-expert layers on GPU (auto-fit will determine the exact count;
|
||||
# with --cpu-moe the non-expert footprint is ~1.6 GB, leaving ample room).
|
||||
N_GPU_LAYERS=999
|
||||
|
||||
# KV cache: Q8_0 for both K and V. ~2 GB total for 200700 ctx × 3 slots
|
||||
# × 10 attention layers × (2 KV heads × 256 dim × 1 byte) × 2 (K+V).
|
||||
CACHE_TYPE_K=q8_0
|
||||
CACHE_TYPE_V=q8_0
|
||||
|
||||
VERBOSITY=2
|
||||
|
||||
# Prefill batch sizing.
|
||||
BATCH_SIZE=4096
|
||||
UBATCH_SIZE=1024
|
||||
|
||||
# Sampling.
|
||||
TEMPERATURE=1.0
|
||||
TOP_K=20
|
||||
TOP_P=0.95
|
||||
MIN_P=0.0
|
||||
|
||||
# Compute threads lowered so other processes keep a core.
|
||||
THREADS=7
|
||||
THREADS_HTTP=8
|
||||
# Max concurrent decoding requests (also caps mamba slot allocation).
|
||||
MAX_RUNNING_REQUESTS=4
|
||||
@@ -1,5 +1,5 @@
|
||||
[Unit]
|
||||
Description=llama.cpp Tiel-Coder-35B-A3B Q6_K_XL qwen35moe hybrid MoE (dankserver, frank-default-small)
|
||||
Description=FreeToken Qwen3.6-35B-A3B-FP8 qwen3_5_moe hybrid MoE (dankserver, frank-default-small)
|
||||
After=network-online.target
|
||||
Wants=network-online.target
|
||||
|
||||
@@ -13,4 +13,4 @@ Restart=on-failure
|
||||
RestartSec=10
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
WantedBy=multi-user.target
|
||||
@@ -1,23 +1,25 @@
|
||||
#!/usr/bin/env bash
|
||||
# Serve peculiar-ragdoll/Tiel-Coder-35B-A3B (UD-Q6_K_XL) as
|
||||
# frank-default-small via stock llama.cpp on Dankserver (RTX 3090).
|
||||
# Serve Qwen/Qwen3.6-35B-A3B-FP8 as frank-default-small via FreeToken
|
||||
# on Dankserver (RTX 3090, 60 GB RAM).
|
||||
#
|
||||
# Runtime: upstream llama.cpp (latest master, CUDA build with
|
||||
# -DGGML_CUDA_FA_ALL_QUANTS=ON -DCMAKE_CUDA_ARCHITECTURES=86).
|
||||
# The model is a 35B-total / 8-active MoE hybrid (qwen35moe arch) with
|
||||
# SSM/Mamba layers (full_attention_interval=4: 10 attention + 30 SSM
|
||||
# layers out of 40). 256 experts, 8 used per token.
|
||||
# Runtime: FreeToken v0.1.2 (edge-native MoE serving engine).
|
||||
# - Experts live in host RAM; an LRU cache of expert slots is resident
|
||||
# on the GPU. Misses are streamed over PCIe.
|
||||
# - Paged KV cache (radix prefix sharing). Only the 10 full-attention
|
||||
# layers page KV (~20 KiB/token); the 30 GDN/linear-attention layers
|
||||
# hold constant-size per-sequence state (mamba slots).
|
||||
# - OpenAI-compatible API (/v1/chat/completions, /v1/models).
|
||||
#
|
||||
# Expert offload: --cpu-moe keeps all 256 expert weights in CPU RAM.
|
||||
# Non-expert weights (attention, SSM, shared expert, embeddings, output)
|
||||
# are ~1.6 GB and fit entirely on the 24 GB RTX 3090.
|
||||
# Model: Qwen/Qwen3.6-35B-A3B-FP8 (safetensors, modelopt block-fp8,
|
||||
# 128x128 weight_block_size). Architecture: qwen3_5_moe — 40 layers,
|
||||
# full_attention_interval=4 (10 attention + 30 GDN), 256 experts / 8 used,
|
||||
# 2 KV heads × 256 dim. 35 GB on disk.
|
||||
#
|
||||
# KV cache: Q8_0 for both K and V. With CTX_SIZE=200700 (66 900 per
|
||||
# slot × 3 parallel) and 10 attention layers (2 KV heads × 256 dim),
|
||||
# the KV cache is ~2 GB — the requested 2 GB VRAM buffer.
|
||||
#
|
||||
# Model is loaded with mmap (default). The 30 GB Q6_K_XL file is
|
||||
# memory-mapped; only accessed pages enter RAM.
|
||||
# Pool sizing: the GPU budget is split MoE-first between expert residency
|
||||
# and KV pages. --moe-cache-size pins the number of resident expert
|
||||
# slots; --num-tokens pins the KV pool capacity. Live resize is possible
|
||||
# via `ft ctl cache --kv N --moe M` (grows KV by shrinking MoE; shrinking
|
||||
# KV requires a restart due to the swap-during-rebuild memory cost).
|
||||
#
|
||||
# Tunables come from frank-default-small.env unless FRANK_SMALL_ENV_FILE
|
||||
# overrides that path.
|
||||
@@ -33,55 +35,39 @@ if [[ -f "$ENV_FILE" ]]; then
|
||||
fi
|
||||
|
||||
# --- defaults (used when the env file does not set them) -----------------
|
||||
: "${MODEL_DIR:=/home/dank/models/tiel-coder-35b}"
|
||||
: "${MODEL_FILE:=Tiel-Coder-35B-A3B-UD-Q6_K_XL.gguf}"
|
||||
: "${LLAMA_SERVER:=/home/dank/llama.cpp/build/bin/llama-server}"
|
||||
: "${MODEL_DIR:=/home/dank/models/qwen36-35b-a3b-fp8}"
|
||||
: "${ALIAS:=frank-default-small}"
|
||||
: "${BIND_ADDR:=0.0.0.0}"
|
||||
: "${LISTEN_PORT:=7770}"
|
||||
: "${CTX_SIZE:=200700}"
|
||||
: "${PARALLEL:=3}"
|
||||
: "${CACHE_TYPE_K:=q8_0}"
|
||||
: "${CACHE_TYPE_V:=q8_0}"
|
||||
: "${N_GPU_LAYERS:=999}"
|
||||
: "${BATCH_SIZE:=4096}"
|
||||
: "${UBATCH_SIZE:=1024}"
|
||||
: "${VERBOSITY:=2}"
|
||||
: "${CPU_MOE:=1}"
|
||||
: "${TEMPERATURE:=1.0}"
|
||||
: "${TOP_K:=20}"
|
||||
: "${TOP_P:=0.95}"
|
||||
: "${MIN_P:=0.0}"
|
||||
: "${THREADS:=7}"
|
||||
: "${THREADS_HTTP:=8}"
|
||||
: "${FT_VENV:=/home/dank/freetoken/.venv}"
|
||||
: "${CUDA_HOME_DIR:=${FT_VENV}/lib/python3.12/site-packages/nvidia/cu13}"
|
||||
|
||||
MODEL="$MODEL_DIR/$MODEL_FILE"
|
||||
# Pool sizing (expert residency vs KV cache tradeoff).
|
||||
: "${MOE_CACHE_SIZE:=2048}"
|
||||
: "${NUM_TOKENS:=453005}"
|
||||
: "${MAX_RUNNING_REQUESTS:=4}"
|
||||
|
||||
if [[ ! -f "$MODEL" ]]; then
|
||||
echo "missing required file: $MODEL" >&2
|
||||
# Paths
|
||||
FT_BIN="${FT_VENV}/bin/ft"
|
||||
MODEL="${MODEL_DIR}"
|
||||
|
||||
if [[ ! -x "$FT_BIN" ]]; then
|
||||
echo "missing FreeToken CLI: $FT_BIN" >&2
|
||||
exit 1
|
||||
fi
|
||||
if [[ ! -x "$LLAMA_SERVER" ]]; then
|
||||
echo "missing llama-server binary: $LLAMA_SERVER" >&2
|
||||
if [[ ! -d "$MODEL" ]]; then
|
||||
echo "missing model directory: $MODEL" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exec "$LLAMA_SERVER" \
|
||||
export PATH="${FT_VENV}/bin:${PATH}"
|
||||
export CUDA_HOME="${CUDA_HOME_DIR}"
|
||||
|
||||
exec "$FT_BIN" serve \
|
||||
--model "$MODEL" \
|
||||
$([ "$CPU_MOE" = "1" ] && printf -- "--cpu-moe") \
|
||||
--cache-type-k "$CACHE_TYPE_K" \
|
||||
--cache-type-v "$CACHE_TYPE_V" \
|
||||
--ctx-size "$CTX_SIZE" \
|
||||
--parallel "$PARALLEL" \
|
||||
--gpu-layers "$N_GPU_LAYERS" \
|
||||
--batch-size "$BATCH_SIZE" \
|
||||
--ubatch-size "$UBATCH_SIZE" \
|
||||
--temp "$TEMPERATURE" \
|
||||
--top-k "$TOP_K" \
|
||||
--top-p "$TOP_P" \
|
||||
--min-p "$MIN_P" \
|
||||
--threads "$THREADS" \
|
||||
--threads-http "$THREADS_HTTP" \
|
||||
--alias "$ALIAS" \
|
||||
--served-model-name "$ALIAS" \
|
||||
--host "$BIND_ADDR" \
|
||||
--port "$LISTEN_PORT"
|
||||
--port "$LISTEN_PORT" \
|
||||
--moe-cache-size "$MOE_CACHE_SIZE" \
|
||||
--num-tokens "$NUM_TOKENS" \
|
||||
--max-running-requests "$MAX_RUNNING_REQUESTS"
|
||||
Reference in New Issue
Block a user