frank-default-small: ncmoe 32 + ubatch 1024 from llama-bench sweep
Swept ncmoe(30..40) x ubatch(256..4096) on the RTX 3090 (Qwen3.6-35B-A3B Q6_K_P, pp16384/tg512, fit-target 0). Key findings: - Generation depends only on ncmoe: 40.9 (ncmoe40) .. 51.1 (ncmoe30); ubatch/batch do not affect decode at all. - Prefill + graph VRAM scale with ubatch; logical batch is irrelevant. - VRAM model (peak + 11.44GiB KV pool + 2.2GiB overhead) reproduces the historical OOMs: ncmoe32-ub2048 sums ~24.4GiB > 24. Chosen state: ncmoe 32 (8 expert layers on GPU), batch 8192, ubatch 1024. gen ~48.7 t/s (+7% vs prior ncmoe 35), prefill ~766 t/s, ~1.0GiB headroom. Alternates: ncmoe34-ub2048 (gen 46.4, pp 1221, 1.5GiB) for prefill-heavy; ncmoe32-ub768 (1.3GiB) if headroom matters most. Docs: new frank-default-small-benchmark.md; synced deployment.md + specs.
This commit is contained in:
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 2048, `--ctx-checkpoints 4`, `--no-mmap`. At idle ~22.2 GB VRAM used, leaving an estimated **~1.8 GiB** headroom (up from ~870 MiB at `--n-cpu-moe 32`). 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, **32 sit on CPU** (`--n-cpu-moe 32`) while the remaining **8 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 1024, `--ctx-checkpoints 4`, `--no-mmap`. At idle ~23.0 GB VRAM used, leaving an estimated **~1.0 GiB** headroom (chosen by benchmark, 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 ~22.2 GB used, leaving an estimated **~1.8 GiB** headroom at `--n-cpu-moe 35` (5 layers' experts on GPU). The move from `--n-cpu-moe 32` to 35 traded a little GPU-expert speed for OOM safety after high-load sessions OOM'd at 32 and 33. If headroom proves too thin for production bursts, dial back `--n-cpu-moe` to 37 (only 3 layers on GPU, ~0.9 GiB more headroom) by editing `scripts/frank-default-small.env`.
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- 24 GB VRAM is close to saturated with this config. At idle ~23.0 GB used, leaving an estimated **~1.0 GiB** headroom at `--n-cpu-moe 32` (8 layers' experts on GPU) with `ubatch 1024`. A llama-bench sweep ([[frank-default-small-benchmark]]) chose this state: it maximises decode (~48.7 t/s) while staying above the OOM boundary that `ncmoe 32 - ubatch 2048` crossed (24.4 GiB). If headroom proves too thin for production bursts, reduce `ubatch` to 768 (~1.3 GiB headroom) or dial `--n-cpu-moe` back to 34 at `ubatch 1536` (~2.0 GiB headroom) 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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+1
-1
@@ -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 2048, `--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. **32 of 40 MoE expert layers** stay on CPU (`--n-cpu-moe 32`); 8 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 1024, `--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: this service replaced the retired `vllm-awq-qwen36` (AWQ 4-bit, port 34401) and the earlier `vllm-qwen36` (FP8) / `llama-server` (ROCmFPX) units. The repo tracks `main` on Dankserver at `/home/dank/vLLM`.
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@@ -0,0 +1,129 @@
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---
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type: reference
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title: frank-default-small Benchmark — ncmoe and Batch Tuning
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description: llama-bench sweep of MoE CPU offload (ncmoe) and batch/ubatch on the RTX 3090 for Qwen3.6-35B-A3B Q6_K_P.
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tags:
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- benchmark
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- llamacpp
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- ncmoe
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- ubatch
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- dankserver
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---
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# frank-default-small Benchmark — ncmoe and Batch/Ubatch Tuning
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This page records a llama-bench sweep on Dankserver (RTX 3090, 24 GiB) for
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the HauhauCS Qwen3.6-35B-A3B Q6_K_P model. The sweep decided the final
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`--n-cpu-moe` and `--ubatch-size` values for `frank-default-small`.
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Run date: 2026-08-13. Build: llama.cpp `555881ebc` (10121). CUDA, 7 threads,
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`--cache-type-k q8_0 --cache-type-v f16`, `--fit-target 0` (KV auto-sized to
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fill VRAM). Prompt 16384 tokens, generation 512 tokens, 2 repetitions.
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## Headline Findings
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### Generation does not depend on batch or ubatch
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For every `ncmoe` value, generation throughput stayed flat across all
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`ubatch` values from 256 to 4096. The delta between the lowest and highest
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was under one token per second. Generation depends only on how many MoE
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expert layers stay on the GPU as `ncmoe` gets smaller.
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| `ncmoe` | Expert layers on GPU | Generation (t/s) |
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| ------- | -------------------- | ---------------- |
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| 40 | 0 | 40.9 |
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| 38 | 2 | 42.6 |
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| 36 | 4 | 44.4 |
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| 35 | 5 | 45.5 |
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| 34 | 6 | 46.4 |
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| 32 | 8 | 48.7 |
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| 30 | 10 | 51.1 |
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Each pair of expert layers moved to the GPU adds roughly 1.1-2.0 t/s of
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generation. Moving from `ncmoe 35` to `ncmoe 32` adds about 3.2 t/s, or 7
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percent.
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### Prefill and graph memory scale with ubatch
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Prefill throughput climbs with `ubatch` because a prompt splits into
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`prompt / ubatch` micro-batches. For `ncmoe 35` at 16384 prompt tokens:
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| `ubatch` | Prefill (t/s) | Peak graph VRAM (MiB) |
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| -------- | ------------- | --------------------- |
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| 256 | 245 | 6838 |
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| 512 | 416 | 6852 |
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| 768 | 556 | 7074 |
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| 1024 | 710 | 7330 |
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| 1536 | 895 | 7846 |
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| 2048 | 1188 | 8574 |
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The logical `--batch-size` does not matter. At a fixed `ubatch`, `batch`
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values of 2048, 4096, and 8192 gave identical prefill speed and peak VRAM.
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## Peak VRAM by ncmoe and ubatch
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Peak VRAM (llama-bench, single context) rises both when `ncmoe` drops
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(more experts on GPU) and when `ubatch` rises (bigger compute graph):
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| `ncmoe` | ub 512 | ub 1024 | ub 1536 | ub 2048 |
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| ------- | ------ | ------- | ------- | ------- |
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| 38 | 4590 | 5068 | 5584 | 6086 |
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| 36 | 6098 | 6576 | 7092 | 7594 |
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| 35 | 6852 | 7330 | 7846 | 8574 |
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| 34 | 7606 | 8084 | 8600 | 9102 |
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| 32 | 9114 | 9592 | 10108 | 10610 |
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| 30 | 10622 | 11100 | 11616 | 12118 |
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## VRAM Budget Model
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The live service holds a fixed 800000-token KV pool plus the mmproj vision
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tower plus CUDA overhead. The model below fits llama-bench peak VRAM into a
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24 GiB budget:
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```
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service VRAM = llama-bench peak + KV pool (11.44 GiB) + overhead (2.2 GiB)
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```
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This model reproduces the historical OOMs. Configurations whose sum exceeds
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24 GiB fail. For example, `ncmoe 32 - ubatch 2048` sums to about 24.4 GiB
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and fails, which matches the recorded OOMs at `ncmoe 32` and `33`.
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## Candidate Configurations
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| Configuration | Gen (t/s) | Prefill (t/s) | Headroom (MiB) | Fit |
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| ------------------ | --------- | ------------- | -------------- | --- |
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| ncmoe 35, ub 2048 | 45.5 | 1188 | 2035 | OK |
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| ncmoe 34, ub 2048 | 46.4 | 1221 | 1507 | OK |
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| ncmoe 34, ub 1536 | 46.5 | 914 | 2009 | OK |
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| ncmoe 32, ub 1024 | 48.7 | 766 | 1017 | OK |
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| ncmoe 32, ub 768 | 48.8 | 600 | 1273 | OK |
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| ncmoe 30, ub 1536 | 51.2 | 1009 | under | OOM |
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## Chosen State
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The chosen state balances generation speed against prefill speed and OOM
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headroom:
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```
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--n-cpu-moe 32 # 8 expert layers on GPU
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--batch-size 8192
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--ubatch-size 1024
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```
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This gives generation about 48.7 t/s (7 percent above the previous
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`ncmoe 35`) while keeping the box above the OOM boundary. If prefill speed
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matters more than generation, use `ncmoe 34 - ubatch 2048`. If stability
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headroom matters most, use `ncmoe 34 - ubatch 1536`.
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## Limitations
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llama-bench measures raw decode and prefill. It does not run speculative
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decoding, parallel slots, or the mmproj vision tower. The live service adds
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those on top. The VRAM model uses a fixed 2.2 GiB overhead estimate; the
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real overhead varies a little with load.
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## See Also
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- [[Dankserver System Specs]]
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- [[deployment]]
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- [[DFlash DSpark Speculative Decoding]]
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@@ -52,6 +52,7 @@ DFlash draft, port 7770]
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- [[Running Qwen3.6 35B A3B FP8 on Dankserver]] — MoE model (35B total, 3B active) on RTX 3090
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- [[Running Qwen3.6 27B FP8 on Dankclaw]] — Dense model (27B) on Strix Halo APU
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- [[frank-default-large-benchmark]] — backend/spec benchmark that drove the Dankclaw llama.cpp deployment
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- [[frank-default-small-benchmark]] — ncmoe and batch/ubatch sweep that drove the Dankserver state
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- [[Dankclaw Improvements]] — researched enhancements: self-hosted agentic tools, runtime tuning, server/OS settings
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## Speculative Decoding & Quantization
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@@ -8,15 +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). 5 expert
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# layers back on GPU; 35 on CPU. Batch 8192 / ubatch 2048.
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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_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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N_CPU_MOE=35
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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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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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@@ -25,8 +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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BATCH_SIZE=8192
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UBATCH_SIZE=2048
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UBATCH_SIZE=1024
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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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@@ -32,10 +32,10 @@ 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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: "${N_CPU_MOE:=32}" # 32 of 40 layers: MoE expert weights stay on CPU; 8 on GPU
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: "${N_GPU_LAYERS:=all}" # every non-expert tensor goes to GPU
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: "${BATCH_SIZE:=2048}"
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: "${UBATCH_SIZE:=512}"
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: "${UBATCH_SIZE:=1024}"
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: "${CTX_CHECKPOINTS:=0}"
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: "${NO_MMAP:=0}"
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: "${TEMPERATURE:=1.0}"
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