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open-webui/docker-compose.mtp.yaml
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bhethermanandClaude Sonnet 5 966140c3ef
Create and publish Docker images with specific build args / build (map[arch:linux/arm64 runner:ubuntu-24.04-arm], map[build_args:USE_OLLAMA=true free_disk:false name:ollama suffix:-ollama]) (push) Canceled after 0s
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Give the MTP llama-server 4 parallel slots instead of 1
With -np 1, open-webui's browser chat and the voice assistant (and
anything else hitting this OpenAI-compatible endpoint) contended for
a single slot -- whichever touched it last evicted the other's live
KV cache, forcing a multi-second full prompt reprocess on the next
request from the loser.

n_ctx_slot = n_ctx / n_parallel, so ctx is quadrupled to 131072 (this
model's native n_ctx_train) to keep each of the 4 slots at the same
32768 budget as before. Measured KV cache cost is only 271 MiB per
slot, so this only costs ~810 MiB more VRAM than the old config.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-09-05 13:40:50 -04:00

73 lines
3.1 KiB
YAML

services:
llama-mtp:
build:
context: ../mtp-relaxed-decoding
container_name: llama-mtp
restart: unless-stopped
ports:
- '8030:8030'
volumes:
# Same host dir ollama's /gguf-import mount points at -- target +
# MTP drafter already live there.
- /home/brian-llm/models/gguf:/models:ro
environment:
- 'MTP_AUTH_TOKEN=${MTP_AUTH_TOKEN}'
- 'MTP_TARGET_GGUF=/models/gemma-4-E2B-it-Q4_K_M.gguf'
- 'MTP_DRAFT_GGUF=/models/mtp-gemma-4-E2B-it.gguf'
# n_max beyond ~2 wasn't worth it for this drafter, and relaxed_top_n
# 2-20 all performed similarly -- see ../mtp-relaxed-decoding/README.md
# §6. Benchmarked on an RTX 5090 there; re-check on this Titan Xp
# before trusting these as tuned rather than just reasonable defaults.
- 'MTP_N_MAX=2'
- 'MTP_RELAXED_TOP_N=5'
# Titan Xp has 12GB VRAM shared with asr/tts/ollama -- batch size kept
# below the README's RTX-5090 sizing (1024) for headroom, but context
# was raised to match the README since Gemma-4's hybrid SWA
# architecture (most layers use a small fixed attention window, only
# every 5th layer is full-context) keeps KV cache growth with n_ctx
# much cheaper than a plain transformer's -- measured at only 271 MiB
# of KV cache (target + draft) per 32768-token slot.
#
# -np 4 gives open-webui's browser chat and the voice assistant (and
# anything else hitting this OpenAI-compatible endpoint) their own
# slot each instead of one contending for a single shared slot --
# with -np 1, whichever of them touched the slot last would evict the
# other's live KV cache, forcing a full prompt reprocess (multi-second
# stall) on the next request from the loser. n_ctx_slot = n_ctx /
# n_parallel, so total ctx is quadrupled to keep each slot at the same
# 32768 budget as before -- 131072 matches this model's native
# n_ctx_train exactly, and only costs ~810 MiB more KV cache VRAM
# than the old -np 1 config (still well under the ~7GB that's free).
- 'MTP_CTX_SIZE=131072'
- 'MTP_N_PARALLEL=4'
- 'MTP_BATCH_SIZE=512'
# Server-side default -- only applies when the client doesn't send its
# own temperature (Open WebUI won't unless you set it in the model's
# Advanced Params).
- 'MTP_TEMP=0.6'
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
open-webui:
depends_on:
- llama-mtp
environment:
# Registers this alongside (not instead of) the Ollama connection --
# Open WebUI lists both in the model picker.
- 'OPENAI_API_BASE_URLS=http://llama-mtp:8030/v1'
- 'OPENAI_API_KEYS=${MTP_AUTH_TOKEN}'
ollama-auth:
depends_on:
- llama-mtp
environment:
# ollama-auth/default.conf.template now proxies to llama-mtp (Ollama
# itself serves no models anymore) and swaps the client's
# OLLAMA_AUTH_TOKEN for this before forwarding upstream.
- 'MTP_AUTH_TOKEN=${MTP_AUTH_TOKEN}'