Entries matching “amd”

@alpharomercoma

@alpharomercoma

A reproducible setup serving Muse Glimmer 30B on one AMD MI300X with a custom 11 GB vLLM 0.28.0 ROCm image, exposed as an authenticated OpenAI-compatible endpoint and driven from the pi CLI.

ROCmFP4 and ROCmFP8 builds of Muse Glimmer 30B and its drafter, targeted and tested on AMD Strix Halo (gfx1151).

Eleven matched on/off pairs across Gemma 4 and Qwen3.6, holding model, quant, card, corpus and concurrency fixed inside each pair. Speed: 1.65x to 2.54x, every pair. Accuracy: nothing the paired intervals could separate from ordinary run-to-run movement. Muse Glimmer is the one that lost. Meta's matching DFlash drafter made the same 7900 XTX 9% slower, keeping 24.55% of drafted tokens against roughly four in five for the Gemma and Qwen heads. Acceptance fell across the run instead of warming up. Meta's model card reports 3.1x on an RTX 5090, and there are open llama.cpp issues for DFlash on AMD and under Vulkan, so I read it as the backend rather than the model. Acceptance turned out to be a poor predictor of speed. It moved under four points across five models while the multiple nearly doubled. What tracks the multiple is how bandwidth-bound the target is: a heavier quant gains more, and the two mixture-of-experts pairs gained least. Worth knowing before you benchmark anything: -md mtp-head.gguf silently disables speculation. Use -hf REPO:QUANT -hfd REPO, then read speculative from /slots and confirm it is true. Per-pair table, intervals, acceptance counters and the raw predic

Meta's launch post for Muse Glimmer, an Apache 2.0 30B model for local agents that fits in ~20GB at 4-bit and runs on M4/M5 Max Macs, RTX 5090s or 24–32GB GPUs.

Hey. Just tried it on my old ass gpus 😄 Surprisingly Tensor Split is working on 2 gpus almost doubling PP (wonder how it will work with 4 gpus) Q6 — 1 GPU llama-server \ --model <MODEL_DIR>/Muse-Glimmer-30B-GGUF/Muse-Glimmer-30B-UD-Q6_K_XL.gguf \ --mmproj <MODEL_DIR>/Muse-Glimmer-30B-GGUF/mmproj-kquant.gguf \ --spec-draft-model <MODEL_DIR>/Muse-Glimmer-30B-GGUF/dflash-kquant.gguf \ --spec-type draft-dflash \ --spec-draft-ngl 999 \ --spec-draft-n-max 3 \ --spec-draft-type-k f16 \ --spec-draft-type-v f16 \ --ctx-size 65536 \ --override-kv muse-glimmer.context_length=int:65536,dflash.context_length=int:65536 \ --n-gpu-layers 999 \ --device ROCm0 \ --device-draft ROCm0 \ --split-mode layer \ --flash-attn on \ --fit off \ --parallel 1 \ --kv-unified \ --batch-size 2048 \ --ubatch-size 512 \ --threads 32 \ --threads-batch 32 \ --cache-type-k f16 \ --cache-type-v f16 \ --image-min-tokens 1024 \ --image-max-tokens 4096 \ --reasoning-preserve \ --temp 0.7 \ --top-p 0.95 \ --top-k 64 \ --min-p 0.0 \ --jinja Q8 — 2 GPUs with tensor split bash llama-server \ --model <MODEL_DIR>/Muse-Glimmer-30B-GGUF/Muse-Glimmer-30B-UD-Q8_K_XL.gguf \ --mmproj <MODEL_DIR>/Muse-Glimmer-30B-GGUF/mmproj-kquan

ROCmFP4 GGUF of Muse Glimmer 30B with DFlash for AMD Strix Halo, requiring a ROCmFPX llama.cpp fork that adds the muse-glimmer architecture.