# Tunables for frank-default-small (HauhauCS Q6_K_P, ngram-mod spec, dankserver)
# Runtime: llama-server b10108; RTX 3090 24GB; Ryzen 7 7700X; 62GB RAM.
MODEL_DIR=/home/dank/models
ALIAS=frank-default-small
HOST=0.0.0.0
PORT=7770
# 3 parallel slots x 200000 ctx each = 600000 total context pool.
# (Reduced from 262144/stream per operator: allows the larger batch to fit VRAM.)
CTX_SIZE=600000
PARALLEL=3
SPEC_TYPE=ngram-mod
# Ngram-mod speculative decoding: look back this many tokens to guess the
# next tokens. Defaults (48/64/24) are a reasonable starting point; tune if
# you want more or fewer draft lookahead tokens.
NGRAM_MOD_N_MIN=48
NGRAM_MOD_N_MAX=64
NGRAM_MOD_N_MATCH=24
CACHE_TYPE_K=q8_0
CACHE_TYPE_V=f16
# All 40 layers keep their MoE expert weights on CPU (targeted expert offload).
N_CPU_MOE=40
# Every non-expert tensor (attention, norms, embedding, LM head) goes to GPU.
N_GPU_LAYERS=all
# Larger batches for prefill throughput. The bigger physical batch grows GPU
# activation buffers; ctx-checkpoints parks attention checkpoints in RAM to
# hold peak GPU memory down so the larger ubatch fits without OOM.
BATCH_SIZE=3072
UBATCH_SIZE=1536
CTX_CHECKPOINTS=4
# Load the model into RAM rather than lazy-mapping it. The MoE experts live on
# CPU, so eager RAM load avoids page faults during decode (uses ~31GB RAM).
NO_MMAP=1
THREADS=8
THREADS_HTTP=8