Working my way up to build a AI Model from scratch

[deicool]

Hello ALL

I know some AI basics like how to use StableDiffusion, but now want to build a LLM of my own from scratch (say a prompt to video LLM!)

Any ideas would be appreciated!

Thank you.

[John6666]

build a LLM of my own from scratch (say a prompt to video LLM!)

If this refers to T2V models, building it from scratch would be financially unfeasible…

---

The best way to work up to “my own AI model” today is not to jump straight into training a full prompt-to-video model from random weights. The effective path in 2026 is: train small models from scratch to learn the mechanics, then build the real system by adapting strong open models. That is not a compromise. It is the fastest route to both real understanding and something usable. Stanford’s CS336 is still explicitly about language modeling from scratch, Hugging Face still teaches tokenizer/model training separately, and Open-Sora 2.0 still shows that serious video pretraining is a large-scale engineering project, not a normal first build. (Stanford CS336)

First, fix the mental model

A “prompt-to-video LLM” is usually not one model. In the current open stack, the system is usually split into: a text model or text encoder that understands the prompt, a video latent compressor such as a 3D VAE, a video generator built with diffusion or flow matching, and then a decoder that turns latent video back into pixels. HunyuanVideo describes an LLM-based text encoder plus a Causal 3D VAE, and LTX-2 describes itself as an audio-video foundation model rather than a plain language model. (GitHub)

That means there are really three different goals hidden inside “build a model from scratch”:

1. Learn the internals deeply.
2. Build a useful product.
3. Pretrain your own foundation model.

Those are different projects with different budgets, different timelines, and different failure modes. Most confusion starts when people mix them together. (Stanford CS336)

What “from scratch” should mean for you

For your stage, “from scratch” should mainly mean:

• write and train a small language model yourself
• write and train a small diffusion model yourself
• then build the real prompt-to-video system with pretrained parts

That gives you the concepts without forcing you into research-lab-scale compute on day one. Hugging Face’s course still says training from scratch makes sense mainly when you have a lot of data and it is very different from the data used by existing models. (Hugging Face)

What to build from scratch first

1. A tiny text model

Start with a tokenizer and a small decoder-only transformer. The important lessons here are not “how to get a great model,” but:

• what tokenization actually does
• how next-token prediction works
• how attention works
• how sampling works
• how training loss behaves
• how evaluation differs from vibes

Hugging Face’s tokenizer chapter is useful because it states the key point clearly: training a tokenizer is not the same as training a model. CS336 is the best current full-stack course for this path. (Hugging Face)

2. A tiny diffusion model

Since your end goal is video, you should learn diffusion early. Hugging Face’s Diffusion Course and the official basic training tutorial still provide the cleanest entry. The tutorial explicitly walks through training a UNet2DModel from scratch, which is exactly the right scale for learning denoising, conditioning, and sampling before you touch video. (Hugging Face)

This is why I would not treat “LLM” as the whole problem. For text-to-video, the hard part is usually not just language understanding. It is latent video generation, temporal consistency, and the data/training pipeline around that. Open-Sora and HunyuanVideo make that structure very clear. (arXiv)

The better way to build something real today

If your real goal is “I want a system that turns prompts into videos,” the strongest 2026 approach is this:

1. Use a small text or multimodal model for planning and prompt rewriting.
2. Use an open video model as the actual generator.
3. Adapt it with LoRA or the model’s official trainer.
4. Add ranking, filtering, and post-processing.

That is much closer to how modern open systems are actually used. HunyuanVideo has an official prompt-rewrite component, LTX-2 emphasizes production-ready audio+video workflows, and Wan provides practical local inference paths. (GitHub)

Which current base models are worth considering

For planning, rewriting, and control

Use a small open text or multimodal model for the controller side.

Gemma 3n is a good choice if you want a compact multimodal controller that can run on everyday devices. Google’s docs say it is optimized for phones, laptops, and tablets, and it supports text, vision, and audio input. (Google AI for Developers)

Qwen3.5 is a good choice if you want a small modern open text family for prototyping and task-specific fine-tuning. The official model cards explicitly position the small Qwen3.5 models for prototyping and task-specific tuning. (Hugging Face)

Qwen3-VL is useful if you want the planner to inspect images, reference frames, or storyboards and then output prompts or structured instructions. The official collection and model card present it as a text-image-video-capable family. (Hugging Face)

These models are good for:

• prompt cleanup
• shot planning
• storyboard text
• structured output
• caption expansion
• tool calls

They are not the actual video engine. (Google AI for Developers)

For actual video generation

Wan2.1 is still one of the easiest low-barrier entries. Its official repo says the T2V-1.3B model needs only 8.19 GB VRAM and can generate a 5-second 480p clip on a 4090 in about 4 minutes. If your main goal is “get a local open video model running,” this is still a strong starting point. (GitHub)

Wan2.2 is the more current Wan line. The official repo adds newer task branches such as image-to-video and text+image-to-video, and it documents single-GPU inference paths for the released models. If your hardware is decent and you want the newer stack, prefer 2.2 over 2.1. (GitHub)

HunyuanVideo-1.5 is the stronger current open base if you care more about quality and official training support. Its repo says training code and a LoRA tuning script were released in December 2025, and it supports distributed training, FSDP, context parallelism, and gradient checkpointing. (GitHub)

LTX-2 is the most interesting if your long-term goal is a controllable creative system rather than just raw generation. The official repo positions it as the first DiT-based audio-video foundation model with synchronized audio/video, multiple performance modes, and production-oriented outputs. The trainer package supports LoRA, full fine-tuning, and IC-LoRA/video-to-video training on custom datasets. (GitHub)

Open-Sora is the research reference, not the starter project. It is the clearest open example of what a full video training stack looks like. Open-Sora 1.2 describes a reproducible setup with about 30 million video clips totaling about 80k hours, and Open-Sora 2.0 says a commercial-level model was trained for about $200k. That is the right thing to study if you want to understand the frontier, but not the right first thing to build. (arXiv)

Fine-tune or train from scratch

Today, the default answer should be:

• use a pretrained base
• adapt with LoRA
• use QLoRA or other memory-saving approaches if hardware is tight
• only consider full pretraining after you have evidence the base model is the bottleneck

PEFT exists exactly for this. Its official docs say PEFT methods adapt large pretrained models by training only a small number of extra parameters, cutting compute and storage cost. The LoRA docs explain the core idea directly: low-rank adapters reduce the number of trainable parameters drastically. TRL’s SFTTrainer is the standard supervised fine-tuning path on top of that. (GitHub)

That means your practical path is usually:

• pick a current base
• define the behavior you want
• create a focused dataset
• fine-tune adapters
• evaluate
• only then decide if you need something heavier

How I would structure the roadmap

Stage 1. Learn the mechanics

Build:

• a tokenizer
• a tiny GPT-like model
• a tiny diffusion model

Resources:

• CS336
• Hugging Face LLM Course
• Hugging Face Diffusion Course
  (Stanford CS336)

Stage 2. Build a usable pipeline

Use:

• Gemma 3n or Qwen3.5/Qwen3-VL for planning
• Wan2.1 or Wan2.2 for the easiest video start
• HunyuanVideo-1.5 if you want a stronger base with official training support
• LTX-2 if you care about audio-video and stronger control
  (Google AI for Developers)

Stage 3. Scale only when needed

When your single-GPU scripts stop being enough, move to:

• Accelerate
• FSDP
• DeepSpeed

The official Accelerate docs explicitly cover FSDP and DeepSpeed integration for scaling training. (Hugging Face)

The two biggest pitfalls right now

1. Following stale tutorials

This is a real 2026 problem. Hugging Face Transformers v5 is a major release with meaningful API and architecture-handling changes, and the official blog presents it as a major simplification and modernization step. Separately, datasets changed enough that many older tutorials now fail with “Dataset scripts are no longer supported.” If you follow random 2023–2024 tutorials uncritically, some of them will simply be broken. (Hugging Face)

2. Confusing inference with training

A model that is easy to run can still be hard to tune. Wan gives accessible inference paths, but HunyuanVideo-1.5’s training release makes clear that serious tuning workflows involve distributed training features and a specific optimizer recommendation. LTX-2’s trainer also signals a more demanding setup than “download and click run.” (GitHub)

My recommendation, plainly

If your goal is understanding, train small models from scratch.

If your goal is a usable prompt-to-video system today, do this instead:

• use a small controller model such as Gemma 3n or Qwen3.5/Qwen3-VL
• use Wan2.1/2.2, HunyuanVideo-1.5, or LTX-2 as the generation base
• adapt with LoRA/PEFT
• train with TRL or the model’s official trainer
• spend a lot of effort on prompts, data quality, and evaluation

If your goal is your own full video foundation model from zero, treat Open-Sora as the benchmark for what that really implies in data, cost, and engineering. (Google AI for Developers)

The shortest honest summary is:

Learn from scratch. Ship with pretrained bases. Fine-tune before you pretrain.

[deicool]

approx how much time do i have to build a fully functional text-to-video before this area becomes stagnant and useless to come out with something meaningful?

[John6666]

If the motivation is purely academic—with no regard for profitability—or if it’s a hobby for a wealthy individual like Elon Musk, there’s no problem at all. But for anyone else, building a competitive T2V model from scratch is quite unrealistic. Money and resources are the biggest hurdles.

A T2V model is a collection of many models. Stable Diffusion is also a collection, but a T2V model is even more so. The knowledge required to build it is so extensive that, even with the help of advanced AI, it would be difficult for a single expert to construct all the model architectures and datasets, so hiring additional staff would likely be necessary. The equipment is not only expensive but also requires data center-level power consumption.

If an individual aims for some kind of “tangible results,” I think it’s more realistic to try to shine in niche areas—such as fine-tuning existing models using LoRA, creating user-friendly frontends or services, or building workflows and pipelines that combine existing models in innovative ways to produce compelling outputs…

While the following figures are merely estimates, given current market trends, it is reasonable to assume that prices will continue to rise:

---

These are planning estimates, not hard deadlines. The time windows are an inference from three things: the open video stack is still improving with releases like Wan2.2 and HunyuanVideo-1.5; the market is still fragmented rather than winner-take-all; and application-layer startups are getting traction by focusing on workflow and consistency rather than training new foundation models. (GitHub)

1) Time window by goal

Goal	What it really means	Time to build something credible	How long the opportunity likely stays meaningful	My view
Prototype on open bases	Prompt → video → selection → export	4–10 weeks	Open now	Good bet
Niche workflow product	Ads, storyboard, avatar, ecommerce, brand consistency	3–9 months	12–36 months	Best bet
Generic consumer T2V app	“Type prompt, get cool clip”	2–6 months	6–12 months before standing out gets much harder	Weakening fast
New T2V foundation model from scratch	Full pretraining, data pipeline, eval, infra	12–24+ months	Bad race already for most solo builders	Poor bet

Why this table looks like this: a16z says enterprise image/video deployments use a median of 14 models, which implies room for orchestration and workflow products, not just raw generation. Reuters’ Higgsfield story points the same way: they integrate third-party models and add a proprietary reasoning/workflow layer. (Andreessen Horowitz)

2) Hardware and budget by path

Path	Practical goal	Minimum workable hardware	Comfortable hardware	Rough GPU budget	Other resources	Source / basis
A. Build on open bases	Get a functional T2V system running	1× 24GB GPU	1× 48GB GPU	$100–$1,000	64GB RAM, 200GB+ SSD is a good working assumption	Wan2.2 says 720p/24fps can run on consumer cards like 4090; LTX docs recommend 64GB+ RAM and 200GB+ SSD. (GitHub)
B. Fine-tune / LoRA-tune video bases	Better control, style, consistency, niche adaptation	1× 32–48GB GPU	1× 80GB GPU	$500–$5,000+	64GB+ RAM, 200GB+ SSD, more storage for datasets/checkpoints	LTX-2 trainer recommends 80GB+ VRAM, with a low-VRAM config for 32GB GPUs; HunyuanVideo-I2V says 60GB minimum for 720p inference and 80GB recommended. (GitHub)
C. From-scratch T2V pretraining	New base model	Cluster	Large H100/H200 cluster	$70k–$200k+	Multi-TB storage, large data pipeline, engineering time, eval stack	Open-Sora 1.2 reports 35k H100 GPU-hours on >30M clips / ~80k hours; Open-Sora 2.0 reports $200k for a commercial-level model. (GitHub)

3) GPU tier cheat sheet

GPU tier	Best use	What it can realistically do	What it usually cannot do comfortably
24GB	Cheapest serious entry	Run lighter open T2V stacks, build prototypes, tune small controller LLMs	Serious video LoRA tuning on heavier stacks is tight
32GB	Low-VRAM tuning tier	Some low-VRAM video fine-tuning paths	Heavy official video tuning remains constrained
48GB	Practical sweet spot	Better local iteration, more breathing room for video tuning	Still below the “official comfortable” tier for many heavy video stacks
80GB	Serious single-GPU work	Hunyuan/LTX-class serious tuning and high-end inference	Still not enough for true from-scratch frontier training
8× 80GB / 8× H200	Serious distributed work	Official-style training workflows, bigger ablations	Still expensive and overkill for first projects
~200 H200-class GPUs	Frontier pretraining	Real from-scratch T2V base-model training	Not a solo-builder path

This tiering is anchored by official docs: HunyuanVideo-1.5 supports consumer inference with 14GB minimum when offloading is enabled; HunyuanVideo-I2V recommends 80GB; LTX recommends A100 80GB or H100 and 64GB+ RAM; Open-Sora 2.0 used large H200 clusters. (GitHub)

4) Current public cloud price anchors

GPU	Public price anchor	Notes	Source
RTX 4090 24GB	from $0.34/hr	Cheap prototype tier	(Runpod)
L4 24GB	about $0.43–$0.44/hr	Useful 24GB cloud option	(Runpod)
RTX 6000 Ada 48GB	$0.74/hr	Good 48GB option	(Runpod)
L40S 48GB	$0.79/hr	Strong 48GB option	(Runpod)
A100 80GB	$1.19/hr	Strong budget training tier	(Runpod)
H100 80GB	from $1.99/hr, often $2.39/hr	Faster, but a step up in cost	(Runpod)
H200 141GB	from $3.59/hr on budget cloud; $4.975/hr per GPU on AWS Capacity Blocks (Tokyo p5e)	Premium tier	(Runpod)

5) What those rates mean in actual project terms

These are simple arithmetic estimates from the public hourly prices above.

Scenario	Example hardware	Rough wall-clock	Approx GPU cost
Prototype sprint	1× RTX 4090	1 week continuous	~$57
Prototype sprint, more headroom	1× RTX 6000 Ada	1 week continuous	~$124
Prototype sprint, strong 48GB	1× L40S	1 week continuous	~$133
Small serious tuning run	1× A100 80GB	3 days continuous	~$86
Small serious tuning run	1× H100 80GB	3 days continuous	~$143–$172
1-week serious tuning	1× A100 80GB	7 days continuous	~$200
1-week serious tuning	1× H100 80GB	7 days continuous	~$334–$401
1-week premium run	1× H200	7 days continuous	~$603–$836
Open-Sora 1.2-scale pretraining anchor	35,000 H100 GPU-hours	n/a	~$70k–$84k on low-cost H100 pricing
Open-Sora 2.0 commercial-level anchor	large H200 cluster	n/a	~$200k reported

Notes:

• The H100 row is shown as a range because public references differ between low-cost and more secure/retail pricing. (Runpod)
• The Open-Sora rows are the clearest public anchors for what “real” from-scratch T2V training costs. (GitHub)

6) Non-GPU resources

Resource	Prototype / fine-tune assumption	From-scratch assumption	Source
System RAM	64GB+ is a good working target	More if preprocessing on the same box	(LTX Documentation)
Fast local SSD	200GB+	More if storing datasets/checkpoints locally	(LTX Documentation)
Object storage	1–5TB is often enough to start	Many TB becomes normal	S3 Standard is $0.023/GB-month for the first 50TB. (Amazon Web Services, Inc.)
Example storage cost	200GB SSD ≈ $34/month on GCP US example	n/a	(Google Cloud)
Data engineering	Helpful	Mandatory	Open-Sora 1.2 scale: >30M clips / ~80k hours. (GitHub)

7) Decision table

Your budget / setup	Best move
<$500	Do not try to train a new video model. Build a prototype on open bases using 24GB cloud GPUs.
$500–$2k	Build a real workflow prototype. Maybe one or two narrow LoRA experiments.
$2k–$10k	Serious niche fine-tuning and repeated iteration become realistic.
$10k–$50k	You can run a small team-style tuning program, but still not a serious from-scratch frontier race.
$70k+	From-scratch pretraining enters the conversation, but only as a serious engineering project.
$200k+	Now you are in the same broad budget class as Open-Sora 2.0’s reported commercial-level training effort.

8) Bottom line

Question	Best answer
How much time do I have?	Enough time to ship a meaningful niche/workflow product. Not much time to stand out with a generic “prompt in, video out” app.
How much hardware do I need?	24GB to start, 48GB for a practical sweet spot, 80GB for serious single-GPU tuning, and a cluster for from-scratch pretraining.
How much money do I need?	$100–$1,000 for a prototype, $500–$5,000+ for serious fine-tuning, $70k–$200k+ for real from-scratch T2V pretraining.
What is the best bet?	Build on open bases, fine-tune for a niche, and compete on control, consistency, and workflow rather than raw generation.

[deicool]

so it is best to become a part of the system and create niche value?

[John6666]

Yeah. If your goal is to use or contribute to the AI ecosystem, that’s probably a safe bet.

It’s reasonable to assume that building an AI model from scratch—one that has reached the latest practical level—requires the capital and resources of a massive startup or a global corporation. It’s more of a sophisticated industrial product than a work of art created by a single artisan.

Alternatively, if AI itself isn’t part of your objective, you could simply choose not to participate in the AI ecosystem at all. You could focus on something completely unrelated, for example.