Home/Compare/DeepSpeed vs fastDeploy

Comparison

DeepSpeed vs fastDeploy

Verdict

Pick DeepSpeed when license: DeepSpeed is Apache-2.0, fastDeploy is MIT; pick fastDeploy when license: fastDeploy is MIT, DeepSpeed is Apache-2.0.

Markdown twin · DeepSpeed alternatives · fastDeploy alternatives

GraphCanon updated today

DeepSpeed logo

DeepSpeed

deepspeedai/DeepSpeed

43kpushed Jul 13, 2026
vs
fastDeploy logo

fastDeploy

notAI-tech/fastDeploy

105pushed Feb 10, 2026

Trust & integrity

SignalDeepSpeedfastDeploy
Maintenance
Very active (0d since push)
As of 1d · github_public_v1
Slowing (154d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 4d · osv@v1
No lockfile (source not queried)
As of today · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

DeepSpeed
Deep learning optimization library for efficient distributed training and inference
fastDeploy
Deploy DL/ ML inference pipelines with minimal extra code.

Stars

DeepSpeed
43k
fastDeploy
105

Forks

DeepSpeed
4.9k
fastDeploy
17

Open issues

DeepSpeed
1.3k
fastDeploy
0

Language

DeepSpeed
Python
fastDeploy
Python

Adopt for

DeepSpeed
Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
fastDeploy
-

Persona

DeepSpeed
-
fastDeploy
-

Runtime

DeepSpeed
-
fastDeploy
-

License

DeepSpeed
Apache-2.0
fastDeploy
MIT

Last pushed

DeepSpeed
Jul 13, 2026
fastDeploy
Feb 10, 2026

Categories

DeepSpeed
Inference & Serving, Model Training
fastDeploy
Inference & Serving, Model Training, Speech & Audio

Trust and health

Maintenance

DeepSpeed
Very active (96%)
fastDeploy
Slowing (36%)

Days since push

DeepSpeed
0d
fastDeploy
154d

Open issues (now)

DeepSpeed
1.3k
fastDeploy
0

Full report

DeepSpeed
Trust report
fastDeploy
Trust report

Choose DeepSpeed if…

  • License: DeepSpeed is Apache-2.0, fastDeploy is MIT.
  • Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu.
  • - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)

When NOT to use DeepSpeed

  • - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
  • - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively

Choose fastDeploy if…

  • License: fastDeploy is MIT, DeepSpeed is Apache-2.0.
  • Tags unique to fastDeploy: docker, falcon, gevent, gunicorn.
  • Also covers Speech & Audio.

When NOT to use fastDeploy

  • Last GitHub push was 154 days ago (slowing maintenance, Feb 10, 2026). Validate activity before betting a new project on fastDeploy.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

Explore

Sources

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

GitHub stars on cards: DeepSpeed 43k · fastDeploy 105 (synced Jul 14, 2026).

Common questions

What is the difference between DeepSpeed and fastDeploy?
DeepSpeed: Deep learning optimization library for efficient distributed training and inference. fastDeploy: Deploy DL/ ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories.
When should I choose DeepSpeed over fastDeploy?
Choose DeepSpeed over fastDeploy when License: DeepSpeed is Apache-2.0, fastDeploy is MIT; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters).
When should I choose fastDeploy over DeepSpeed?
Choose fastDeploy over DeepSpeed when License: fastDeploy is MIT, DeepSpeed is Apache-2.0; Tags unique to fastDeploy: docker, falcon, gevent, gunicorn; Also covers Speech & Audio.
When should I avoid DeepSpeed?
- When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
When should I avoid fastDeploy?
Last GitHub push was 154 days ago (slowing maintenance, Feb 10, 2026). Validate activity before betting a new project on fastDeploy. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is DeepSpeed or fastDeploy more popular on GitHub?
DeepSpeed has more GitHub stars (42,700 vs 105). Stars measure visibility, not whether either tool fits your constraints.
Are DeepSpeed and fastDeploy open source?
Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, fastDeploy: MIT).
Where can I find alternatives to DeepSpeed or fastDeploy?
GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and fastDeploy alternatives (DeepSpeed markdown twin, fastDeploy 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, DeepSpeed or fastDeploy?
DeepSpeed: Very active. fastDeploy: Slowing. 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 DeepSpeed and fastDeploy?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; fastDeploy trust report.

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