DATAandClaude Opus 5
f628eedbaa
Verifying a K-token draft costs ~K times the CPU expert work, because each token routes to its own 8-of-256 experts and nothing is amortized. The loss is largest on structured code output (-51% for ngram-simple), i.e. exactly where speculation should have won. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
177 lines
7.0 KiB
Markdown
177 lines
7.0 KiB
Markdown
# llama_qwen3.6_A3B
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llama.cpp CUDA runner for **Qwen3.6-35B-A3B** (UD-Q4_K_M) at **128k context** on 4n4rch02.
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Port **18008**, OpenAI-compatible API at `http://192.168.3.189:18008/v1`.
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## Host
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- 4n4rch02 (192.168.3.189), Arch Linux, driver 610.43.03
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- AMD Ryzen 7 3700X — **8 physical cores** / 16 SMT threads
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- 62 GiB RAM, 15 GiB swap
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- CUDA0: RTX 3060 12 GB — **shared with the KDE/Wayland desktop** (~1.1 GiB at idle)
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- CUDA1: RTX 3080 10 GB — dedicated
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## Modell
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`/mnt/2TSAM990nvme/docker-volume-outsource/llm-models/hf/hub/models--unsloth--Qwen3.6-35B-A3B-GGUF/snapshots/a483e9e6cbd595906af30beda3187c2663a1118c/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf`
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20.6 GiB, unsloth Dynamic Q4_K_M (imatrix). The compose mounts the **repo dir**, not
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the snapshot dir — the snapshot entry is a relative symlink into `../../blobs/`, so
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both have to be inside the mount.
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### Architektur (`qwen35moe`) — warum 128k hier billig ist
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This is a **hybrid SSM + attention MoE**, not a dense-attention model:
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| | |
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| Layers | 40 |
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| `full_attention_interval` | 4 → **only 10 layers use full attention** |
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| Remaining 30 layers | gated-delta SSM, constant-size recurrent state |
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| Attention heads | 16 Q / 2 KV, `key_length` = `value_length` = 256 |
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| Experts | 256 total, 8 active, expert FFN 512, shared expert 512 |
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| Native context | 262144 |
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Only the 10 full-attention layers grow a KV cache, so 128k of KV costs just
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```
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10 layers x 2 kv-heads x 256 dim x 2 (K+V) x 1.0625 B/elem (q8_0) x 131072 tok = 1.33 GiB
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```
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Measured: the whole runtime footprint with *all* experts on CPU is 4.65 GB at 128k ctx.
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**The context is not the constraint here — the 20.6 GiB of weights against 22 GiB of
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VRAM is.** That is what the tuning below is about.
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The model is multimodal-capable (the chat template emits `<|vision_start|>` /
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`<|image_pad|>` tokens), but **no mmproj file is present**, so this deployment is
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text-only. It is a reasoning model with `<think>` tags and XML-style tool calls, so
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`--jinja` is mandatory.
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## Tuning
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### 1. `--n-cpu-moe` collides with `-sm layer` — `-ts` is not optional
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`--n-cpu-moe N` moves the expert tensors of the **first N layers** to host RAM.
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`-sm layer` assigns the **first** layers to CUDA0. Those are the same layers, so
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CUDA0 gets the lightweight ones and CUDA1 ends up holding every heavy expert layer.
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With the default split, **every** value of `--n-cpu-moe` from 12 down to 4 died the
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same way — CUDA1 out of memory while allocating the KV cache:
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```
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ggml_backend_cuda_buffer_type_alloc_buffer: allocating 680.00 MiB on device 1: cudaMalloc failed: out of memory
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alloc_tensor_range: failed to allocate CUDA1 buffer of size 713031680
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llama_init_from_model: failed to initialize the context: failed to allocate buffer for kv cache
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```
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`-ts 24,16` moves the layer boundary back toward CUDA0 and fixes it. A heavy
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(expert-bearing) layer is ~469 MiB; a layer whose experts are on CPU is ~59 MiB.
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### 2. `--threads 8`, not 16
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The CPU-side expert matmuls are memory-bandwidth-bound, so the 8 SMT siblings only
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add contention. Measured at `ncmoe=8`, 8k depth:
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| threads | prefill | generation |
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| 8 | 556 t/s | **57.3 t/s** |
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| 16 | 556 t/s | 39.8 t/s |
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**+44% generation.** Prefill is unaffected because it runs on the GPU.
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### 3. Expert offload vs. VRAM headroom
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All at 128k ctx, q8_0 KV, `--threads 8`, `-ts 24,16`:
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| `--n-cpu-moe` | gen @512 | gen @8k | CUDA0 free | CUDA1 free |
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| 8 | 59.9 t/s | 57.3 t/s | 388 MiB | 531 MiB |
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| **10 (default)** | **55.6 t/s** | **53.3 t/s** | **1312 MiB** | **529 MiB** |
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| 12 | 51.3 t/s | 49.1 t/s | 2236 MiB | 527 MiB |
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| 40 (all experts on CPU) | 15.3 t/s | 16.1 t/s | — | — |
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`ncmoe=10` is the shipped default: it gives up 7% throughput for **~1.3 GiB of spare
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VRAM on CUDA0**, which the desktop shares. At `ncmoe=8` only 388 MiB is left there,
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and a browser opening a few video tabs is enough to OOM a `restart: unless-stopped`
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service into a crash loop. If the desktop is idle, `ncmoe=8` is the faster setting.
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CUDA1 is the binding constraint in every case — 529 MiB free is not enough for another
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469 MiB expert layer, which is why `ncmoe` cannot go below 8 at this context size.
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### 4. Throughput over depth (shipped config)
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`ncmoe=10`, `-ts 24,16`, `--threads 8`, 128k ctx allocated, q8_0 KV:
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| prompt depth | prefill | generation |
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| 512 | 420 t/s | 56.3 t/s |
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| 32k (27169 tok) | 486 t/s | 47.8 t/s |
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| ~97k (99109 tok) | 425 t/s | 34.1 t/s |
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Generation falls off ~40% between empty and ~97k, which is the attention cost on the
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10 full-attention layers. Prefill stays flat around 420–490 t/s, so filling the whole
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128k window takes roughly 4–5 minutes.
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### 5. Speculative decoding makes it slower — don't enable it
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Counter-intuitive but consistent, `--n-predict 400`, thinking disabled:
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| workload | none | ngram-mod | ngram-simple | ngram-cache |
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| rewrite (output ≈ copy of prompt) | **55.6** | 40.6 | 39.7 | 47.0 |
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| extend (structured code) | **55.3** | 32.7 | 27.2 | 38.1 |
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| prose (novel text, control) | **56.1** | 54.7 | 52.9 | 48.4 |
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Every mode loses, and it loses *most* on exactly the structured workloads
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speculation is supposed to win. The reason is the CPU-side experts: verifying a
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K-token draft costs about K times the CPU expert work, because each token routes to
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its own subset of 8 out of 256 experts, so there is no weight reuse to amortize.
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On a fully GPU-resident model batch verification is nearly free; with
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`--n-cpu-moe` it is not. Reconsider only if the model ever fits entirely in VRAM.
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### 6. Rejected
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- `-ub 256`: frees only ~80 MiB per GPU (not the 469 MiB an extra layer needs) and
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costs **38% prefill** (556 → 347 t/s). Keep the default `-ub 512`.
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- Lower `-ts` toward CUDA1 (e.g. `23,17`): would leave CUDA1 at ~60 MiB free.
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### A note on the CUDA0 headroom
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Free VRAM on CUDA0 was observed drifting between ~1100 and ~1300 MiB across restarts
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purely from desktop compositor churn — a ~200 MiB swing with nothing else changing.
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That is why the default keeps >1 GiB spare there rather than the 388 MiB that
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`ncmoe=8` leaves.
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## Deploy
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```bash
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cd ~/projects/llama_qwen3.6_A3B
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docker compose up -d
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```
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Loads in ~15 s (`--no-mmap`, weights read from NVMe).
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## Benchmarks
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```bash
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python3 bench/bench.py --port 18008 --depths 512,8192,32768 --n-predict 128
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./bench/sweep.sh 10:24,16:8 8:24,16:8 # <ncmoe>:<tensor-split>:<threads>
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python3 bench/spec_bench.py --no-think # copy/code/prose generation workloads
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```
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`bench.py` issues a warmup request first — without it the first measurement reads
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~30% low because of one-off CUDA graph setup.
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## Image
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`ghcr.io/ggml-org/llama.cpp:server-cuda`, tested at build **b10121**
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(commit `555881ebc8b0`, 2026-07-25). Updated from b10068 during this deployment;
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b10121 is the first build here that supports the `qwen35moe` architecture end to end.
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## VRAM-Exklusivitaet
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Uses ~20 GB of the 22 GB total. It **cannot** run alongside `llama-gemma4` (18006) or
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qwen-prism (18004). Ollama on 11434 loads models on demand and will fight for VRAM —
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stop it or let its `keep_alive` expire before starting this runner.
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