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Comparison

optillm vs airllm

optillm (Optimizing inference proxy for LLMs) vs airllm (AirLLM for large language model inference on lightweight GPUs) - live GitHub stats and typed graph relationships, not marketing.

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optillm

algorithmicsuperintelligence/optillm

★ 4.2kpushed Jul 5, 2026

airllm

lyogavin/airllm

★ 22kpushed Jul 8, 2026

Tagline

optillm: Optimizing inference proxy for LLMs
airllm: AirLLM for large language model inference on lightweight GPUs

Stars

optillm: 4.2k
airllm: 22k

Forks

optillm: 368
airllm: 2.6k

Open issues

optillm: 21
airllm: 106

Language

optillm: Python
airllm: Jupyter Notebook

Adopt for

optillm: OptiLLM is an optimizing inference proxy for Large Language Models (LLMs) that improves accuracy and performance on reasoning tasks by implementing state-of-the-art techniques without the need for model training.
airllm: AirLLM is a tool designed to dramatically reduce inference memory usage for large language models (LLMs), enabling them to run on lightweight GPUs. It supports running large models like AirLLM 70B, Llama3.1 (405B), and Q

Persona

optillm: -
airllm: -

Runtime

optillm: -
airllm: -

License

optillm: OptiLLM uses the Apache-2.0 license.
airllm: Apache-2.0

Last pushed

optillm: Jul 5, 2026
airllm: Jul 8, 2026

Categories

optillm: Inference & Serving
airllm: Inference & Serving

Trust and health

Days since push

optillm: 3d
airllm: 0d

Open issues (now)

optillm: 21
airllm: 106

Owner type

optillm: Organization
airllm: User

Security scan

optillm: 1 low (1 low)
airllm: 4 low (4 low)

Full report

optillm: Trust report
airllm: Trust report

Typed relationship

optillm alternative airllmOptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups.

Shared compatibility

Python · optillm: Python runtime · airllm: Python runtime

Trust and health
	optillm	airllm
Tagline	Optimizing inference proxy for LLMs	AirLLM for large language model inference on lightweight GPUs
Stars	4.2k	22k
Forks	368	2.6k
Open issues	21	106
Language	Python	Jupyter Notebook
Adopt for	OptiLLM is an optimizing inference proxy for Large Language Models (LLMs) that improves accuracy and performance on reasoning tasks by implementing state-of-the-art techniques without the need for model training.	AirLLM is a tool designed to dramatically reduce inference memory usage for large language models (LLMs), enabling them to run on lightweight GPUs. It supports running large models like AirLLM 70B, Llama3.1 (405B), and Q
Persona	-	-
Runtime	-	-
License	OptiLLM uses the Apache-2.0 license.	Apache-2.0
Last pushed	Jul 5, 2026	Jul 8, 2026
Categories	Inference & Serving	Inference & Serving
Days since push	3d	0d
Open issues (now)	21	106
Owner type	Organization	User
Security scan	1 low (1 low)	4 low (4 low)
Full report	Trust report	Trust report
Typed relationship	optillm alternative airllmOptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups.
Shared compatibility	Python · optillm: Python runtime · airllm: Python runtime

Choose optillm if…

optillm is primarily Python; airllm is Jupyter Notebook.
Requirements: Works with multiple LLM providers including OpenAI, Anthropic, Google, Cerebras, and supports over 100 models via LiteLLM..
OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups.
Tags unique to optillm: optimization, proxy-server, agentic-ai, openai.
optillm ships Docker support for self-hosted deployment.
You require significant improvements in the accuracy of LLMs on math, coding, or logical reasoning tasks as OptiLLM promises 2-10x better accuracy with no additional training.

When NOT to use optillm

If you are looking for a solution that requires minimal compute overhead at inference time, as OptiLLM achieves its improvements by performing additional computations during this phase.
When the core functionality or training of LLMs must be altered to achieve accuracy improvements, since OptiLLM operates as an external proxy and does not involve any direct modifications to the model

GitHub Details

Choose airllm if…

airllm is primarily Jupyter Notebook; optillm is Python.
OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups.
Tags unique to airllm: llama, chinese-llm, llm, instruct-gpt.
You should use AirLLM if you need to run very large models such as Qwen3-235B or DeepSeek-V3 (671B) on lower-end GPUs like a single 3GB, 8GB, or ~12GB card without resorting to quantization, distill

When NOT to use airllm

Avoid using AirLLM if you require running models that are not supported by the tool or if your inference environment does not align with its lightweight GPU requirements. If your infrastructure can n

GitHub Details

Explore

optillm trust report →airllm trust report →Inference & Serving category →All comparisons Stack workflows Trending tools

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Common questions

What is the difference between optillm and airllm?: optillm: Optimizing inference proxy for LLMs. airllm: AirLLM for large language model inference on lightweight GPUs. See the comparison table for live GitHub stats and shared categories.
When should I choose optillm over airllm?: Choose optillm over airllm when optillm is primarily Python; airllm is Jupyter Notebook; Requirements: Works with multiple LLM providers including OpenAI, Anthropic, Google, Cerebras, and supports over 100 models via LiteLLM.; OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups; Tags unique to optillm: optimization, proxy-server, agentic-ai, openai; optillm ships Docker support for self-hosted deployment; You require significant improvements in the accuracy of LLMs on math, coding, or logical reasoning tasks as OptiLLM promises 2-10x better accuracy with no additional training.
When should I choose airllm over optillm?: Choose airllm over optillm when airllm is primarily Jupyter Notebook; optillm is Python; OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups; Tags unique to airllm: llama, chinese-llm, llm, instruct-gpt; You should use AirLLM if you need to run very large models such as Qwen3-235B or DeepSeek-V3 (671B) on lower-end GPUs like a single 3GB, 8GB, or ~12GB card without resorting to quantization, distill.
When should I avoid optillm?: If you are looking for a solution that requires minimal compute overhead at inference time, as OptiLLM achieves its improvements by performing additional computations during this phase. When the core functionality or training of LLMs must be altered to achieve accuracy improvements, since OptiLLM operates as an external proxy and does not involve any direct modifications to the model
When should I avoid airllm?: Avoid using AirLLM if you require running models that are not supported by the tool or if your inference environment does not align with its lightweight GPU requirements. If your infrastructure can n
Is optillm or airllm more popular on GitHub?: airllm has more GitHub stars (22,274 vs 4,172). Stars measure visibility, not whether either tool fits your constraints.
Are optillm and airllm open source?: Yes - both are open-source projects on GitHub (optillm: Apache-2.0, airllm: Apache-2.0).
Where can I find alternatives to optillm or airllm?: GraphCanon lists graph-backed alternatives at /tools/algorithmicsuperintelligence-optillm/alternatives and /tools/lyogavin-airllm/alternatives (/tools/algorithmicsuperintelligence-optillm/alternatives.md, /tools/lyogavin-airllm/alternatives.md), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?: Yes. The markdown twin at /compare/algorithmicsuperintelligence-optillm-vs-lyogavin-airllm.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, optillm or airllm?: optillm: Very active. airllm: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for optillm and airllm?: GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: optillm: /tools/algorithmicsuperintelligence-optillm/trust; airllm: /tools/lyogavin-airllm/trust.