solar-open-100b-p150x8

changh95/solar-open-100b-p150x8

solar-open-100b-p150x8 is a machine learning model from changh95. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

Upstage Solar-Open-100B (102B MoE, 12B active, 128 experts + 1 shared, 131K context) served on eight Blackhole P150 boards with tensor parallelism through vLLM.

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Public

Updated Sep 11, 2026

Repo size

1.7 GB

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Hugging Face

Repo makeup

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Other1.7 GB · 99%

At a glance

Access: Public
Created: Sep 11, 2026
Updated: Sep 11, 2026
SHA: fcbaab7f

Model card

From the Hugging Face model README

solar-open-100b-p150x8

Upstage Solar-Open-100B (102B MoE, 12B active, 128 experts + 1 shared, 131K context) served on eight Blackhole P150 boards with tensor parallelism through vLLM.

Runs on p150x8 (mesh P150x8) — 16,384-token context, up to 32 concurrent sequences.

Packaged and published with tt-model-manager 0.1.0 (manifest schema 5.1).

Quickstart

tt-model pull  changh95/solar-open-100b-p150x8 --with-weights
tt-model serve changh95/solar-open-100b-p150x8

pull --with-weights downloads the Docker image and the upstage/Solar-Open-100B weights at 1f591439b14055004d1a5d1a975608953a022fea (into your HF cache; they are not in the image). serve starts an OpenAI-compatible server on port 20000 (or the next free port, if that one is busy); the first start compiles kernels for your device, which takes several minutes, and the server is ready when it logs Application startup complete.

Performance

Measured on eight Blackhole P150 boards (logical 1x8 mesh, TP=8) with real weights, bfp8 experts and KV cache, greedy decoding, warm caches, on the tt-metal commit this image was built from (2026-09-10). Rows are concurrent users, columns are input/output tokens per user. The 128-token columns use 32 distinct prompts; the 1K-32K columns feed every user the same text and are upper bounds.

Per-user decode, tokens per second:

users \ in/out tokens	128/128	128/1024	1024/128	2048/128	4096/128	8192/128	8192/1024	16384/128	32768/128
1	74.6	74.1	74.1	72.5	71.4	69.0	68.0	64.5	57.5
2	53.2	53.2	56.5	55.9	54.3	52.9	52.4	49.5	45.5
4	47.6	48.1	54.9	53.8	52.6	50.0	51.0	48.8	44.1
8	41.5	40.7	54.1	53.2	50.3	47.4	47.4	42.9	33.0
16	35.1	35.3	52.4	49.8	46.5	42.9	41.7	33.6	20.8
32	29.0	28.3	51.0	48.1	42.6	35.6	33.6	20.5	14.1

Aggregate decode throughput, tokens per second:

users \ in/out tokens	128/128	128/1024	1024/128	2048/128	4096/128	8192/128	8192/1024	16384/128	32768/128
1	75	74	74	73	72	69	68	64	57
2	106	106	113	112	109	106	105	99	91
4	191	192	219	216	211	200	204	195	176
8	332	325	433	426	402	379	379	344	264
16	561	566	835	796	744	687	666	537	333
32	926	906	1632	1542	1361	1141	1074	657	451

Time to first token, mean over users, ms (sequential prefill: first user = one prefill, mean = (B+1)/2 prefills):

users \ in/out tokens	128/128	128/1024	1024/128	2048/128	4096/128	8192/128	8192/1024	16384/128	32768/128
1	163	166	416	1194	1604	2927	2942	7518	12562
2	232	235	596	1084	2135	5827	6514	10255	20064
4	384	386	1005	2197	3712	9200	8015	18341	31798
8	665	665	2324	3345	6407	13651	16280	26913	60260
16	1299	1258	3452	6424	12341	26017	26427	54661	145439
32	2431	2490	7078	12254	23506	52219	51151	127929	295550

Provenance

The exact sources the image was built from — code/ in this repo is byte-identical to the model code inside the image:

component	built from
tt-metal	`584d9f294781f2f9d9ce02d6d2b5db4be72d53a2`
vLLM	`v0.25.1`
vllm-tt-plugin	`ba73d09` (branch `solar-fixes`: async-decode EOS fix, plain-architecture registration)
`code/` digest	`312c02815cac2ae3` (sha256, first 16 hex digits)
built	2026-09-11T04:17:58+00:00 by tt-model 0.1.0

Created: Sep 11, 2026
Updated: Sep 11, 2026