Home/Compare/airllm vs kvcached

Comparison

airllm vs kvcached

Verdict

Pick airllm when airllm is primarily Jupyter Notebook; kvcached is Python; pick kvcached when kvcached is primarily Python; airllm is Jupyter Notebook.

Markdown twin · airllm alternatives · kvcached alternatives

GraphCanon updated today

airllm logo

airllm

lyogavin/airllm

22kpushed Jul 11, 2026
vs
kvcached logo

kvcached

ovg-project/kvcached

1.1kpushed Jul 2, 2026

Trust & integrity

Signalairllmkvcached
Maintenance
Very active (0d since push)
As of today · github_public_v1
Active (9d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
4 low (4 low)
As of 2d · osv@v1
No lockfile
As of today · none

Tagline

airllm
AirLLM 70B inference with single 4GB GPU
kvcached
Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond

Stars

airllm
22k
kvcached
1.1k

Forks

airllm
2.6k
kvcached
122

Open issues

airllm
106
kvcached
90

Language

airllm
Jupyter Notebook
kvcached
Python

Adopt for

airllm
AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
kvcached
-

Persona

airllm
-
kvcached
-

Runtime

airllm
-
kvcached
-

License

airllm
Apache-2.0
kvcached
Apache-2.0

Last pushed

airllm
Jul 11, 2026
kvcached
Jul 2, 2026

Categories

airllm
Inference & Serving
kvcached
LLM Frameworks, Inference & Serving

Trust and health

Maintenance

airllm
Very active (96%)
kvcached
Active (82%)

Days since push

airllm
0d
kvcached
9d

Open issues (now)

airllm
106
kvcached
90

Owner type

airllm
User
kvcached
Organization

Security scan

airllm
4 low (4 low)
kvcached
No lockfile

Full report

kvcached
Trust report

Choose airllm if…

  • airllm is primarily Jupyter Notebook; kvcached is Python.
  • Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
  • Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
  • Tags unique to airllm: llama, chinese llm, instruct-gpt, generative-ai.
  • If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.

When NOT to use airllm

  • Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
  • Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.

Choose kvcached if…

  • kvcached is primarily Python; airllm is Jupyter Notebook.
  • Tags unique to kvcached: kvcache-optimization, kvcached, elastic-kvcache, gpu-mutiplexing.
  • Also covers LLM Frameworks.

When NOT to use kvcached

  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: airllm 22k · kvcached 1.1k (synced Jul 11, 2026).

Common questions

What is the difference between airllm and kvcached?
airllm: AirLLM 70B inference with single 4GB GPU. kvcached: Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond. See the comparison table for live GitHub stats and shared categories.
When should I choose airllm over kvcached?
Choose airllm over kvcached when airllm is primarily Jupyter Notebook; kvcached is Python; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: llama, chinese llm, instruct-gpt, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
When should I choose kvcached over airllm?
Choose kvcached over airllm when kvcached is primarily Python; airllm is Jupyter Notebook; Tags unique to kvcached: kvcache-optimization, kvcached, elastic-kvcache, gpu-mutiplexing; Also covers LLM Frameworks.
When should I avoid airllm?
Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
When should I avoid kvcached?
LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Is airllm or kvcached more popular on GitHub?
airllm has more GitHub stars (22,399 vs 1,093). Stars measure visibility, not whether either tool fits your constraints.
Are airllm and kvcached open source?
Yes - both are open-source projects on GitHub (airllm: Apache-2.0, kvcached: Apache-2.0).
Where can I find alternatives to airllm or kvcached?
GraphCanon lists graph-backed alternatives at airllm alternatives and kvcached alternatives (airllm markdown twin, kvcached markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, airllm or kvcached?
airllm: Very active. kvcached: 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 airllm and kvcached?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airllm trust report; kvcached trust report.