Fahd Mirza

Subscribers 759K

Videos 5.4K

Views 18.1M

Channels Like Fahd Mirza

Fahd Mirza covers practical, hands-on AI engineering topics, including local deployment, edge inference, model evaluation, and tooling across multiple frameworks. Content appears as tutorials and real-world experiments, with titles emphasizing practical testing, schema-based extraction, and AI agent workflows. The channel posts with a weekly cadence and averages around 4.1K views per video, with videos typically around 8 minutes, published since Aug 7, 2020, from Sydney, Australia.

AI summary

Results last updated 3 months ago.

Similar Channels

We found 44 YouTube channels similar to Fahd Mirza

IBM Technology YouTube channel thumbnail

#1

IBM Technology view on YouTube ▶

90% relevance

Subscribers 1.6M

Videos 1.5K

Views 108.6M

Appearances 56

SERP 100%

Similarity 83%

ai model deployment local ai inference edge ai deployment

Both target enterprise AI topics like ai model deployment and on-device inference, with high search overlap (100% for IBM) but IBM’s content emphasizes enterprise and edge AI deployment, aligning with Fahd Mirza on technical deployment topics while diverging in audience focus.

AI summary

#2

Alex Ziskind view on YouTube ▶

55% relevance

Subscribers 462K

Videos 1.1K

Views 92.9M

Appearances 12

SERP 22%

Similarity 77%

local ai inference llm performance testing ai model quantization

Shares focus on local AI techniques such as local AI inference and llm performance testing (high content similarity at 77%), but Alex’s audience and content patterns lean more toward quantization and performance benchmarks than Fahd’s broader deployment emphasis.

AI summary

#3

AI Engineer view on YouTube ▶

54% relevance

Subscribers 375K

Videos 613

Views 12.3M

Appearances 9

SERP 9%

Similarity 84%

edge ai deployment ai model quantization on-device inference

Both cover edge AI deployment and on-device inference, with strong content alignment on on-device AI approaches, though AI Engineer emphasizes quantization and edge runtimes more, creating a similar technical niche with Fahd.

AI summary

#4

Google Cloud Tech

54% relevance

Subscribers 1.4M

Videos 2K

Views 53.4M

Appearances 13

SERP 16%

Similarity 79%

ai model deployment on-device inference enterprise ai platforms

Similar emphasis on ai model deployment and on-device inference in enterprise contexts (high content similarity), but Google Cloud Tech targets cloud-first enterprise platforms, whereas Fahd centers on on-device and edge scenarios.

AI summary

#5

NextGen AI Explorer

53% relevance

Subscribers 1.0K

Videos 10K

Views 471.0K

Appearances 6

SERP 14%

Similarity 79%

edge ai deployment gpu memory optimization real-time AI processing

Shares edge AI deployment and real-time AI processing topics, reflecting a similar technical niche, though NextGen leans more toward GPU memory optimization and streaming workloads than Fahd’s broader deployment guidance.

AI summary

#6

Matt Wolfe view on YouTube ▶

53% relevance

Subscribers 913K

Videos 627

Views 70.7M

Appearances 2

SERP 5%

Similarity 85%

open source ai models ai tooling review

Both discuss open source AI models and AI tooling reviews, resulting in overlapping audiences, but Matt Wolfe prioritizes open-source tooling critique whereas Fahd centers on deployment and optimization workflows.

AI summary

Content Landscape

Top competitors by match strength are IBM Technology (90% match) and Google Cloud Tech (54% match). IBM Technology shares overlapping queries such as ai model deployment, local ai inference, and edge ai deployment, while Google Cloud Tech overlaps on ai model deployment, on-device inference, and enterprise AI platforms. Other notable competitors include Alex Ziskind (55% match) and AI Engineer (54% match), who compete on local inference, llm performance testing, and ai model quantization. Fahd Mirza has 759K subscribers, which is smaller than IBM Technology (1.6M) and Google Cloud Tech (1.4M) but higher than Alex Ziskind (462K) and AI Engineer (375K). All identified competitors compete on searches related to model deployment, on-device or edge inference, and quantization, indicating overlap in practical deployment and performance topics.

AI summary

Video Highlights

Didn't Make the Cut

38 additional channels that were close

Rank	Relevance	Channel	Similarity	Subscribers	SERP	Appearances
#7	52%	Aiconomist	83%	47.9K	6%	1
#8	52%	Jeff Su ▶	83%	1.6M	6%	4
#9	52%	Matt Williams	76%	94.5K	16%	7
#10	52%	The Linux Foundation	82%	209K	6%	2
#11	52%	xCreate	83%	24.8K	5%	1
#12	51%	Parker Prompts ▶	82%	98.1K	6%	1
#13	51%	NeuralNine ▶	80%	461K	7%	7
#14	51%	TECHtalk	82%	69K	5%	1
#15	51%	AWS Developers	80%	1.7M	7%	3
#16	51%	Roboflow	81%	71.7K	4%	2
#17	50%	Mark Hennings	80%	10.3K	5%	1
#18	50%	Dan Martell ▶	81%	2.4M	4%	2
#19	50%	freeCodeCamp.org ▶	78%	11.5M	8%	7
#20	50%	codebasics	79%	1.5M	6%	5
#21	50%	Thu Vu	80%	332K	4%	3
#22	50%	Eye on Tech ▶	79%	128K	6%	1
#23	50%	Bloomberg Technology	78%	717K	8%	2
#24	50%	Digibase Media	78%	23.5K	7%	2
#25	49%	Tina Huang ▶	79%	1.1M	4%	3
#26	49%	Udacity	78%	646K	6%	1
#27	49%	Dave Ebbelaar ▶	79%	246K	5%	2
#28	49%	Trevor Spires	78%	21.7K	5%	1
#29	49%	Fireship ▶	77%	4.1M	6%	6
#30	48%	DigitalOcean	77%	60K	5%	2
#31	48%	Dr. Raj Ramesh	77%	104K	5%	1
#32	48%	Julia Turc	78%	69.9K	4%	3
#33	48%	Callstack	75%	21.4K	6%	2
#34	47%	DeepFindr	76%	45.3K	5%	4
#35	47%	Parallel Routines	76%	1.4K	4%	2
#36	47%	Edge Impulse	75%	17.2K	4%	4
#37	47%	Exponent ▶	74%	481K	6%	2
#38	47%	PropTech Founder	75%	2.9K	5%	1
#39	47%	Lex Fridman ▶	75%	5.0M	5%	1
#40	46%	Kotwel	75%	1.9K	4%	2
#41	45%	AWS Events	68%	172K	9%	6
#42	45%	Wondershare PDFelement	70%	86.8K	7%	2
#43	43%	Apryse	69%	65.5K	5%	2
#44	42%	RealPars ▶	67%	1.2M	6%	2

Search Queries Used

ai model deployment local ai inference edge ai deployment llm performance testing open source ai models ai agent frameworks pdf data extraction multilingual document processing schema-based extraction ai model quantization on-device inference gpu memory optimization ai hardware benchmarks ai tooling review enterprise ai platforms ai model optimization on-device AI pipelines edge computing workloads ai model serving distributed model inference real-time AI processing privacy preserving AI ai model auditing model monitoring tools data labeling best practices mlops best practices lora fine tuning guide risk assessment ai systems ai deployment patterns serverless inference model versioning strategy ai explainability methods data pipeline integration cost optimization ai ai latency benchmarks

Frequently Asked Questions

Which YouTube channels are most similar to Fahd Mirza?

IBM Technology — 90% match, 1.6M subscribers; Alex Ziskind — 55% match, 462K subscribers; AI Engineer — 54% match, 375K subscribers. These channels share a focus on AI, machine learning, and related technologies, creating technical content and tutorials aimed at developers and enthusiasts.

What type of content does Fahd Mirza make?

Fahd Mirza creates technical content about AI, data processing, and machine learning workflows, as evidenced by video titles like 'Let's Do AI Together', 'Lift: Schema-Based PDF Extraction Tested Locally on 10 Languages', 'vLLM + PegaFlow: KV Cache That Survives Restarts (Hands-On)', and 'NVIDIA Puzzle 75B: A 120B Model Squeezed onto ONE GPU'. The channel averages about 4.1K views per video, with multiple recent videos and an implied upload cadence of more than one video per week.

How do we determine which channels are similar to Fahd Mirza?

We analyze Fahd Mirza's recent videos, generate topic-relevant search queries, check YouTube search results, and compare the meaning of each channel's content to measure similarity. The result is a ranked list sorted by SERP overlap, semantic similarity, and search appearances.

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