frank-default-small: ncmoe 35->36 for prefill stability
Fix a live crash: cuBLAS cudaErrorInvalidValue in ggml_cuda_mul_mat_cublas killed the service during a 145k-token prefill burst at ncmoe35/ub3072. ncmoe 36 moves one more expert layer to CPU: frees ~0.75 GiB peak VRAM (grid: peak 8606 vs 9360 MiB at ub3072) for -2% prefill / -2% decode. Keeps prefill priority (top ubatch tier). Docs synced; noted fallback GGML_CUDA_FORCE_MMQ=1 if pressure persists.
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Frank
@@ -51,11 +51,11 @@ Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.0
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### Active Services
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The active `frank-default-small` service (llama.cpp, `b10108+`) serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model on port 7770 with **ngram-mod** speculative decoding. It runs **4 parallel slots** at 200000 context each (pool 800000). Of the 40 layers' MoE expert weights, **35 sit on CPU** (`--n-cpu-moe 35`) while the remaining **5 layers' expert weights** are resident on GPU along with all non-expert tensors (`--gpu-layers all`). KV stays on GPU with K=`q8_0`, V=`f16`. Batch 8192 / ubatch 3072, `--ctx-checkpoints 4`, `--no-mmap`. At idle ~22.8 GB VRAM used, leaving an estimated **~1.2 GiB** headroom (chosen by benchmark for prefill speed, see [[frank-default-small-benchmark]]). Idle RAM ~32 GB of 62 GB.
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The active `frank-default-small` service (llama.cpp, `b10108+`) serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model on port 7770 with **ngram-mod** speculative decoding. It runs **4 parallel slots** at 200000 context each (pool 800000). Of the 40 layers' MoE expert weights, **36 sit on CPU** (`--n-cpu-moe 36`) while the remaining **4 layers' expert weights** are resident on GPU along with all non-expert tensors (`--gpu-layers all`). KV stays on GPU with K=`q8_0`, V=`f16`. Batch 8192 / ubatch 3072, `--ctx-checkpoints 4`, `--no-mmap`. At idle ~21.05 GB VRAM used, leaving an estimated **~3.0 GiB** headroom (chosen for prefill stability, see [[frank-default-small-benchmark]]). Idle RAM ~32 GB of 62 GB.
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## Hardware Relevance
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- 24 GB VRAM is close to saturated with this config. At idle ~22.8 GB used, leaving an estimated **~1.2 GiB** headroom at `--n-cpu-moe 35` (5 layers' experts on GPU) with `ubatch 3072`. A llama-bench sweep ([[frank-default-small-benchmark]]) chose this state because prefill is the priority: `ubatch 3072` reaches ~1458 t/s prefill, the top tier, and generation is independent of ubatch. If decode speed matters more, use `ncmoe 32 - ubatch 1024` (gen ~48.7 t/s). By editing `scripts/frank-default-small.env`.
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- 24 GB VRAM is close to saturated with this config. At idle ~21.05 GB used, leaving an estimated **~3.0 GiB** headroom at `--n-cpu-moe 36` (4 layers' experts on GPU) with `ubatch 3072`. A llama-bench sweep ([[frank-default-small-benchmark]]) guided this state: prefill is the priority, so `ubatch 3072` stays (top prefill tier), and `ncmoe 36` was raised from 35 to fix a cuBLAS OOM-class crash during big prefills by freeing ~0.75 GiB peak VRAM at only -2% prefill. If it still crashes under load, add `GGML_CUDA_FORCE_MMQ=1`. If decode speed matters more, use `ncmoe 32 - ubatch 1024` (gen ~48.7 t/s). By editing `scripts/frank-default-small.env`.
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- 62 GB system RAM holds the ~29 GB of MoE expert weights with room to spare (~57 GB available after load).
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- 127 GB swap is available for CPU KV cache paging if GPU KV offload is disabled.
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- Root disk has approximately 140 GB free. Store the GGUF models under `/home/dank/models` (about 32 GB total).
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@@ -143,7 +143,7 @@ The active service on Dankclaw runs llama.cpp serving the HauhauCS Qwen3.6-27B u
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### Dankserver — frank-default-small
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The active service on Dankserver runs llama.cpp (upstream, `b10108+`) and serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model under the alias `frank-default-small` on port 7770. It uses **ngram-mod** speculative decoding (`--spec-ngram-mod-n-min 48 / n_max 64 / n_match 24`), **4 parallel slots** at 200000 context each (pool 800000), and mmproj vision. **35 of 40 MoE expert layers** stay on CPU (`--n-cpu-moe 35`); 5 expert layers' weights are back on GPU alongside all non-expert tensors (`--gpu-layers all`). KV stays on GPU (K=`q8_0`, V=`f16`). Batch 8192 / ubatch 3072, `--ctx-checkpoints 4`, `--no-mmap`. The unit file is `scripts/frank-default-small.service`, symlinked to `/etc/systemd/system/`. It launches through `scripts/serve-llama-frank-small.sh`, which reads tunables from `scripts/frank-default-small.env`.
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The active service on Dankserver runs llama.cpp (upstream, `b10108+`) and serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model under the alias `frank-default-small` on port 7770. It uses **ngram-mod** speculative decoding (`--spec-ngram-mod-n-min 48 / n_max 64 / n_match 24`), **4 parallel slots** at 200000 context each (pool 800000), and mmproj vision. **36 of 40 MoE expert layers** stay on CPU (`--n-cpu-moe 36`); 4 expert layers' weights are back on GPU alongside all non-expert tensors (`--gpu-layers all`). KV stays on GPU (K=`q8_0`, V=`f16`). Batch 8192 / ubatch 3072, `--ctx-checkpoints 4`, `--no-mmap`. The unit file is `scripts/frank-default-small.service`, symlinked to `/etc/systemd/system/`. It launches through `scripts/serve-llama-frank-small.sh`, which reads tunables from `scripts/frank-default-small.env`.
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> Note: the Dankserver process currently observed runs `--spec-type ngram-mod` with `--parallel 4`, batch 8192 / ubatch 2048, and `--n-cpu-moe 34` (this is being bench-tuned by the operator; reconcile against `scripts/frank-default-small.env`).
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@@ -101,24 +101,31 @@ and fails, which matches the recorded OOMs at `ncmoe 32` and `33`.
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## Chosen State
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Prefill speed is the priority for this workload, so the final state takes
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the top prefill tier while keeping a safe headroom margin:
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Prefill speed is the priority, so the state keeps the top prefill tier and
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moves one expert layer to CPU for stability:
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```
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--n-cpu-moe 35 # 5 expert layers on GPU
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--n-cpu-moe 36 # 4 expert layers on GPU
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--batch-size 8192
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--ubatch-size 3072
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```
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This gives prefill about 1458 t/s at 16384 prompt tokens, generation about
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45.4 t/s, and about 1.2 GiB of headroom at the 800000-token pool. Both are
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chosen because generation does not depend on `ubatch`: `ubatch 3072` reaches
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the best prefill speed with no cost to decode.
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This gives prefill about 1427 t/s at 16384 prompt tokens, generation about
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44.4 t/s, and about 3.0 GiB of headroom at the 800000-token pool.
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`ncmoe 36` came from a live crash rather than this sweep. At `ncmoe 35` with
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`ubatch 3072`, a big prefill burst failed a cuBLAS call under memory
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pressure (a `cudaErrorInvalidValue` in `ggml_cuda_mul_mat_cublas`), which
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killed the service. Raising `ncmoe` one step frees about 0.75 GiB of peak
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VRAM (the grid shows `ncmoe 36 - ubatch 3072` peak 8606 MiB versus 9360 MiB
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at `ncmoe 35`) for about 2 percent less prefill and 2 percent less decode.
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If a crash still occurs under load, set `GGML_CUDA_FORCE_MMQ=1` to bypass
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the cuBLAS path for quantized matmuls, or drop `ubatch`, or shrink the
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context pool.
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If decode speed matters more than prefill, swap to `ncmoe 32 - ubatch 1024`
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(generation about 48.7 t/s, prefill about 766 t/s, about 1.0 GiB headroom).
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If stability headroom matters most, use `ncmoe 34 - ubatch 1536` (about 2.0
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GiB headroom).
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## Limitations
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@@ -8,17 +8,18 @@ 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). 5 expert
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# layers back on GPU; 35 on CPU. Batch 8192 / ubatch 3072.
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# Spec: ngram-mod. n_min=48, n_max=64, n_match=24 (defaults). 4 expert
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# layers back on GPU; 36 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: 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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# Expert offload: 36 of 40 layers keep MoE weights on CPU; remaining
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# 4 layers have their expert weights resident on GPU for faster routing.
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# Chosen for prefill-max stability at ubatch 3072: frees ~0.75 GiB peak
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# VRAM vs ncmoe 35 (grid: peak 8606 vs 9360 MiB) for -2% prefill, -2%
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# decode. Fixes the cuBLAS OOM crash class during big prefills.
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N_CPU_MOE=36
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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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@@ -32,7 +32,7 @@ fi
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: "${NGRAM_MOD_N_MATCH:=24}"
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: "${CACHE_TYPE_K:=q8_0}"
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: "${CACHE_TYPE_V:=f16}"
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: "${N_CPU_MOE:=35}" # 35 of 40 layers: MoE expert weights stay on CPU; 5 on GPU
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: "${N_CPU_MOE:=36}" # 36 of 40 layers: MoE expert weights stay on CPU; 4 on GPU
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: "${N_GPU_LAYERS:=all}" # every non-expert tensor goes to GPU
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: "${BATCH_SIZE:=2048}"
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: "${UBATCH_SIZE:=3072}"
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