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Muse Glimmer on Tenstorrent p150
A correctness-first bring-up of Muse Glimmer 30B on a single Tenstorrent p150 card, with paged KV cache, native DFlash speculative decoding and an OpenAI-compatible server.
# Muse Glimmer 30B This bring-up supports the correctness-first production path only: - batch size 1 on one TT device; - 2,048-token chunked causal prefill into a 64-token paged KV cache; - the checkpoint's native DFlash assistant; - a native 16-token DFlash block in a 32-token physical target verify tile; - greedy decoding through the OpenAI-compatible server; - Muse's native addressed-message and ATEM tool-call protocol. Production weights use BFP8 for attention/LM-head projections and BFP4 for all target and DFlash MLP projections. Norms and KV caches remain BF16. Packed verification writes only its real rows with one page-level fill per touched page; physical padding rows never enter the target cache. Prompt-only alignment and long-context tails may use all 32 physical rows, while native speculative decode remains 16 tokens. A persistent single-session prompt snapshot reuses an exact chat prefix on the next turn; only the newly serialized assistant, tool, and user suffix is appended. On the local single-device Blackhole server, synchronized prefill of an exactly 128,000-token prompt measured **1,549.38 prompt tok/s**; all 128,000 tokens used the chunked paged path. A cached 64-token tool-call suffix at that position measured 184.30 prefill tok/s and 56.01 true AR tok/s. The following 145-token tool-response suffix (128 chunked plus a 17-token packed tail) measured 246.77 prefill tok/s and 20.13 true AR tok/s. The complete tool call and response were parsed correctly and remained coherent. At short context, the sustained 256-token workload produced 255 true AR tokens in 2.121 seconds: **119.99 AR tok/s**, with 240 accepted drafts across 18 packed verifies. `tokens_per_second` is deliberately the end-to-end completion rate: completion tokens divided by the entire request latency. It must not be interpreted as prefill throughput. `ar_decode_tokens_per_second` (also exposed under the backward-compatible `decode_tokens_per_second` name) counts only tokens produced after the captured AR replay loop starts. It excludes tokenization, cold or cached prefill, cache snapshots, tool-call parsing, tool-response prompt tokens, and the first bonus token already computed by the final prefill logits. Its denominator includes trace-input refreshes, DFlash replay, on-device greedy sampling, packed target verification, the tiny ID readback, and anchor-commit replay. DF



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