escha-runtime-qwen3moe

Escha Runtime — qwen3moe

By Escha Labs Inc.

Serving runtimes for Escha 2-/3-bit (eschamoe) quantized models of the qwen3_5_moe architecture (Qwen3.5 / Qwen3.6 Mixture-of-Experts, 256 experts). One repo per model architecture, one directory per engine — pick the engine that fits your workload:

Neither engine is uniformly faster. ZML wins sustained decode on long single-stream answers and is far simpler to deploy; SGLang wins short replies, wins time-to-first-token on long prompts, and is the only option for concurrency, tool calling or structured output. If unsure: multi-user, agents-at-scale, or structured output → SGLang; long-form single-user generation on your own GPU → ZML.

The ZML engine is built on ZML (Zig + MLIR/XLA); the SGLang engine on a fork of SGLang. Both run the same Escha CUDA kernels and serve the same model files.

Apple Silicon

Both engines above are NVIDIA/Linux only. On a Mac, use escha-mlx (separate repo, Apache-2.0): an MLX runtime with Metal kernels, continuous batching, prefix caching and the same OpenAI-compatible endpoint. It serves the same model files with no conversion step.

Requires Apple Silicon M1–M5, macOS 14+, Python 3.10–3.13 and 24 GB of unified memory. Measured resident footprint is 12.25 GB; single-stream decode is 27.3 tok/s on an M4 base and 59.7 tok/s on an M5 Pro, with aggregate throughput of 185.6 and 539.3 tok/s respectively at batch 128. Those are not comparable to the CUDA figures above: an M4 base moves ~120 GB/s against a 4090's 1008 GB/s, and decode here is memory-bound. Full tables, install steps and a head-to-head against stock MLX 4-bit are in that repo.

Compatible models

<!-- add each qwen3_5_moe Escha model you publish here (e.g. Ornith-1.0-35B, W3 variants) -->

These runtimes target qwen3_5_moe. A model of a different architecture will not load — use the matching escha-runtime-<arch> repo.

Quickstart

SGLang engine (full detail: sglang/INSTALL.md, incl. the per-GPU cookbook):

python3.12 -m venv .venv && source .venv/bin/activate
pip install -U pip wheel
pip install "torch>=2.9" --index-url https://download.pytorch.org/whl/cu128   # cu12 torch first
pip install ./sglang/escha-*.whl   # pulls the bundled sglang fork + its full dep closure

hf download EschaLabs/Qwen3.6-35B-A3B-Escha-W2 --local-dir ./Qwen3.6-35B-A3B-Escha-W2
MODEL=./Qwen3.6-35B-A3B-Escha-W2 bash sglang/serve.sh

ZML engine (full detail: zml/INSTALL.md) — no Python at all:

tar xf zml/escha-zml-serve-*-linux-x86_64.tar.gz && cd escha-zml-serve-*
hf download EschaLabs/Qwen3.6-35B-A3B-Escha-W2 --local-dir ./model
./escha-serve

Long context and agentic use (ZML engine)

ESCHA_CTX reaches the model's native 262,144 tokens. Measured on one 4090 with facts planted at 10%, 50% and 90% depth and all three asked for at the end — the check that positions and recurrent state thread correctly across the whole prompt:

Prefill is ~N²/2 work, so the rate falls with length. The comfortable band on a 24 GB card is ~8k–130k, where TTFT is seconds; 262k answers correctly but takes ~4.3 minutes to read.

Multi-turn: a conversation whose prompt grows each turn reuses the previous prefix. On a 14k-token conversation the first turn takes 4.11 s and each following turn 1.27 s (~99.8% of the prompt reused). Reuse requires an exact append — editing earlier history re-reads the prompt — and only one conversation is cached. SGLang's radix cache is faster in absolute terms here and handles branching and multiple sessions; the ZML cache exists to make single-session agent loops practical, not to match it.

Decode slows as position grows on both engines, because decode attention is O(position). On ZML, measured with prefill excluded: 227 tok/s at position ~1k, 163 at 3k, 124 at 7.5k. (That measurement divides total request time and so reads ~5% below the streamed grid above — method, not configuration.)

Requirements (shared)

• NVIDIA GPU, compute capability 8.0–12.0 (Ampere → Blackwell), Linux x86-64. Kernel launch route auto-selects per GPU. Per-architecture and per-VRAM launch recipes: sglang/INSTALL.md → Running on your GPU.
• SGLang engine: Python 3.12 + CUDA-12 PyTorch 2.9 or newer (the wheel handles every other dependency). 24 GB VRAM recommended (16 GB works with reduced context). As of 1.0.3 the wheel is built against PyTorch's stable ABI, so one binary serves torch 2.9 and every later release — earlier wheels were locked to a single torch minor. Verified byte-identical output and equal decode speed (205.9 / 227.7 tok/s bs1 on a 4090, shipped default / 24 GB config) on torch 2.9.1 and 2.11.0. On torch 2.10+ install sglang-kernel from sglang's CUDA-12 index first; see sglang/INSTALL.md → Supported PyTorch versions.
• ZML engine: no runtime dependencies — the archive bundles the CUDA runtime; just an NVIDIA driver (R550+) and glibc 2.27+ (RHEL/Rocky/Alma 8 and newer). 24 GB VRAM; one request at a time.

Known limitations

• DETERMINISTIC=1 fails on consumer Blackwell (sm_120). The deterministic attention kernel requests 104 KB of shared memory per block, above the sm_120 limit, and the server exits during startup. It works on Ampere, Ada and Hopper. Greedy output is in any case not bit-reproducible across requests on either engine — batch composition changes fp16 accumulation order, so a near-tie can flip and a long reasoning chain diverges from there.
• The ZML engine serves no /v1/completions (HTTP 404). Use /v1/chat/completions.
• The ZML engine does not validate requests. An unknown model field is served anyway instead of returning model_not_found, and usage.prompt_tokens is wrong when a prompt is truncated.
• The ZML engine can wedge on an over-long prompt on a 16 GB card, requiring kill -9 rather than returning an error. This is why it asks for 24 GB — use the SGLang engine on 16 GB, where the same prompt returns a clean HTTP 400.

License

Everything here is released under the Apache License, Version 2.0 — see LICENSE. All bundled third-party code is permissive (Apache-2.0 / MIT / BSD-3-Clause; NVIDIA runtime libraries in the ZML bundle under the NVIDIA EULA's redistributable-runtime terms) — no copyleft. Full texts and the component inventory: THIRD_PARTY_LICENSES/. Model weights are not in this repo and carry their own license in the model repository.

Footnotes

1. Same-harness measurement, 2026-07-26: one RTX 4090, the same model files, the same streaming OpenAI client, greedy, batch 1, medians of 2 reps over an ISL×OSL grid (128–1890 in, 128–1890 out). Decode = steady-state tokens/s between the first and last streamed token. Per-cell decode: SGLang 176.8–230.9 (median 218.2), ZML 184.6–240.7 (median 234.8); ZML leads every cell with ≥256 output tokens and trails on 128-token replies, where its 16-token fused decode chunk dominates. TTFT by input length (ZML / SGLang): 128 tok 55/55 ms · 500 tok 95/91 ms · 1000 tok 178/143 ms · 1890 tok 353/237 ms. ↩ ↩²

2. Measured 2026-07-26 on one RTX 4090 (24 GB) with Qwen3.6-35B-A3B-Escha-W2. Both engines ship a conservative default context and let you raise it. SGLang reports its KV pool at startup; launched with CTXLEN=262144 it allocated 159,480 tokens at its shipped MEM=0.78 and 234,796 at MEM=0.90 — that pool size is the ceiling for a single sequence. ZML serves a 261,966-token prompt at ESCHA_CTX=262144. Both are memory-bound near the cap, not architecture-bound: KV on this hybrid model is only ~20 KiB per token (10 attention layers; the 30 gated-delta-net layers hold a fixed ~66 MB recurrent state), so 262,144 tokens is 5.37 GB beside 12.3 GB of weights. In fairness to SGLang: its 234,796-token pool at MEM=0.90 still left 1.38 GB of VRAM free, so it would very likely also reach the 262,144 cap at a higher mem-fraction-static — we did not test that, and 0.78 is simply the conservative default the runtime ships. Read this row as "both engines reach the model's native context on a 24 GB card", not as a ZML advantage. ↩