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Multi-GPU inference engine for large language models on older hardware (RTX 2080 Ti, Ascend 910B)

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RelicLLM

A multi-GPU inference runtime for older accelerators, on PyTorch. It owns the model implementations, the serving adapters and the schedulers; the kernels come from relic-core.

What lives here

One package, relicllm/. It is the serving shell and the model side together:

Path What it is
relicllm/models/ the model implementations (deepseek_v4, deepseek_v4_1, glm_dsa, mimo_v2, minimax_m2, qwen4_exp, xing4_0)
relicllm/{loader,encoding,runtime,components}/ the weight loaders, the prompt encoders, the device plane and the MoE components
relicllm/{cli,server,api,backends,protocol}/ the CLI, HTTP server, public API, runtime adapters and the request protocol

src/ used to be a second top-level package holding the model side. It was merged in: the two called each other, and the name src in site-packages belonged to nobody.

What does NOT live here

  • No C++ engine. The runtime is PyTorch; the five runtimes (v41, mimo, xing4, torch, qwen4_exp) are all Python. The retired cpp_engine and the cpp backend that fronted it are gone.

  • No kernels. relic_core.kernels is imported, not vendored. Build relic-core first:

    pip install -e ../relic-core --no-build-isolation --no-deps
    pip install -e . --no-build-isolation --no-deps

    --no-deps on both: torch is resolved from the environment, and pip without the flag may reinstall a different one and rebuild the kernels against the wrong ABI. The relic-core dependency line is a local checkout, not a PyPI release.

  • Not the single-card / edge runtime — that is PocketLLM.

Provenance

Extracted with git-filter-repo from the PocketLLM monorepo (src/ minus src/csrc and src/kernels, plus pocketllm/), history preserved. The package was renamed pocketllm → relicllm, and src.kernels imports now resolve to relic_core.kernels. The src/ tree came across unchanged and was later merged into relicllm/ with git mv, so history survives at the new paths.

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Multi-GPU inference engine for large language models on older hardware (RTX 2080 Ti, Ascend 910B)

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