frank-default-small: prefill-max ncmoe 35 / ubatch 3072
Prefill is the priority for this workload. Switch from decode-max (ncmoe 32 / ubatch 1024) to prefill-max: - n-cpu-moe 35, ubatch 3072: prefill ~1458 t/s (top tier), gen ~45.4 t/s, ~1.2 GiB headroom at the 800k pool. - Gen is independent of ubatch; ub 3072 reaches best prefill with no decode cost. Docs synced (benchmark Chosen State, deployment, specs).
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@@ -8,17 +8,17 @@ PORT=7770
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# (Increased from 3×200k to add a 4th parallel stream.)
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CTX_SIZE=800000
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PARALLEL=4
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# Spec: ngram-mod. n_min=48, n_max=64, n_match=24 (defaults). 8 expert
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# layers back on GPU; 32 on CPU. Batch 8192 / ubatch 1024.
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# Spec: ngram-mod. n_min=48, n_max=64, n_match=24 (defaults). 5 expert
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# layers back on GPU; 35 on CPU. Batch 8192 / ubatch 3072.
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SPEC_TYPE=ngram-mod
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NGRAM_MOD_N_MIN=48
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NGRAM_MOD_N_MAX=64
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NGRAM_MOD_N_MATCH=24
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# Expert offload: 32 of 40 layers keep MoE weights on CPU; remaining
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# 8 layers have their expert weights resident on GPU for faster routing.
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# Benchmark-chosen (see frank-default-small-benchmark.md): max decode at
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# the headroom that stays above the historical OOM boundary.
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N_CPU_MOE=32
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# Expert offload: 35 of 40 layers keep MoE weights on CPU; remaining
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# 5 layers have their expert weights resident on GPU for faster routing.
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# Benchmark-chosen (see frank-default-small-benchmark.md): prefill-max.
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# gen ~45.4 t/s, pp ~1458 t/s, ~1.2 GiB headroom.
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N_CPU_MOE=35
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N_GPU_LAYERS=all
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# KV cache on GPU, K quantized to q8_0, V stays f16 (user-locked decision).
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CACHE_TYPE_K=q8_0
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@@ -27,10 +27,10 @@ CACHE_TYPE_V=f16
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# larger batch/ubatch (leaves headroom for ctx-checkpoints operation).
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CTX_CHECKPOINTS=4
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# Larger batches / ubatch for big prefill workloads.
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# ubatch 1024 (benchmark-chosen): decode is flat vs ubatch; 1024 frees
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# compute-graph VRAM to afford ncmoe 32 while keeping prefill ~766 t/s.
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# ubatch 3072 (benchmark-chosen): prefill is the priority; this is the top
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# prefill tier (~1458 t/s) with ~1.2 GiB headroom. Decode is independent.
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BATCH_SIZE=8192
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UBATCH_SIZE=1024
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UBATCH_SIZE=3072
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# Load the model into RAM rather than lazy-mapping it. The MoE experts live on
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# CPU, so eager RAM load avoids page faults during decode (uses ~31GB RAM).
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NO_MMAP=1
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