mudler / mudler/vllm.cpp

ROCm: evaluate incremental dual-GPU local-KV sharding for Gemma-4 decode

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Dominant language
C++
Stars
423
Forks
53
Avg merge
20h 26m
Merged PRs (30d)
310

Description

Goal

Evaluate an opt-in Gemma-4 ROCm T=1 full-attention decode path that shards the two BF16 KV heads across dual RDNA4 GPUs with local historical KV residency and incremental new-token mirroring.

Scope

  • Gemma-4 full-attention layers only
  • T=1, one request, BF16 KV, Hkv=2, d=512
  • GPU0 owns KV head 0; GPU1 owns a persistent local mirror of KV head 1
  • Mirror only newly written K/V entries after initial backfill
  • Run each head against local HBM and merge only peer Q-head outputs
  • Default OFF with synchronous fallback to the existing single-GPU split-KV path

Exclusions

  • No FP8 KV in the first experiment
  • No sliding-window layers
  • No remote reads of historical KV
  • No full-cache copy per decode token
  • No production-default change without measured KEEP evidence

Gates

  • Build and default/fallback smoke
  • Positive candidate breadcrumb
  • Greedy output identity and Paris/arithmetic quality
  • Short plus long-context paired, order-alternated A/B
  • No GPU faults, page faults, invalid-device errors, NaNs, progressive memory growth, or hidden CPU/offload path
  • KEEP only for at least 10% long-context end-to-end decode improvement with no material short-context regression

Evidence origin

Local architecture audit: .agents-external lab reference gemma4-decode-hotpath-architecture-audit-2026-08-12.md; implementation must validate source assumptions against the current dirty lab stack before editing.

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First steps

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  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with .agents-external lab reference gemma4-decode-hotpath-architecture-audit-2026-08-12.md, then validate its assumptions against the current dirty lab stack before editing. Run the build and default/fallback smoke checks, followed by the specified paired A/B tests; done requires the listed quality and fault gates plus at least 10% long-context improvement without material short-context regression.

Written by the indexing model from the issue text.

Assessment

Tech stack
cpp
Domain
machine-learning, performance
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
25/100

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