Custom Kernels for All from Codex and Claude

# **Custom Kernels for All: How Codex and Claude Are Redefining AI for Developers**

**The humble beginnings of a developer’s dream**

For years, the promise of AI has been the same: *build anything, with anything, at scale.* But the reality for most developers has been a frustrating game of workarounds. Your hyperscale model might be too expensive for niche applications. Your open-source tool lacks the fine-tuning needed for specialized tasks. And your edge device? Forget it—most pretrained models are far too bloated to run efficiently on anything less than a cloud GPU.

That’s why **Custom Kernels for All**, the latest push from **Anthropic** and **Salesforce**, is creating ripples across the AI industry. By delivering **Claude** and **Codex** as *modular, developer-friendly kernels*—alongside tools to fine-tune them for specific workflows—the two companies are doing something that hasn’t been done at this scale before: making **enterprise-grade AI models accessible** to anyone with a modest computing budget.

It’s a sharp contrast to the usual top-down approach of tech giants, which force developers to adapt their needs to massive, inflexible models. Instead, Anthropic and Salesforce are letting developers *reshape the models themselves*—a move that could democratize AI tooling, accelerate innovation, and even challenge the dominance of the most powerful LLM providers.

But behind the buzz, there are thorny questions: Can this work without compromising performance? Will it fragment the AI ecosystem? And is the model-as-a-kernel approach more hype than substance?

Here’s what we know—and why it matters.

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**The Problem: AI Models Are Too Big**

The large language model (LLM) arms race has produced some staggering results. **GPT-4** boasts 1.76 trillion parameters. **Llama 3** is a *lightweight* 405 billion parameters. Even **Claude 3 Sonnet**, Anthropic’s latest flagship, is a **129 billion-parameter** beast that requires substantial compute to run.

For developers, this is an existential issue. **Training a model from scratch costs hundreds of thousands of dollars**—even just fine-tuning one can bankrupt smaller teams. And deployment? Forget it. **A single inference call on a full-scale Claude model can cost $10 or more**, depending on usage.

Yet, most applications don’t need the full capabilities of a trillion-parameter model. A **customer support chatbot** might only require a fraction of the power of Claude to handle FAQs effectively. A **local development IDE** could benefit from a **lightweight version of Codex** optimized for code completion rather than full-scale reasoning.

The industry has long recognized this mismatch, but solutions have remained fragmented. **Quantization, pruning, and distillation** are techniques to shrink models—but they require deep expertise and often degrade performance. **Hugging Face’s inference endpoints** offer a middle ground, but even their cheapest options start around **$30 per million tokens**, a cost barrier for many.

Then there’s the **edge problem**. Run a 100-billion-parameter model on an **Apple M2 chip**? Not without creative (and often inefficient) workarounds. **Fine-tune a model for a Raspberry Pi?** Most developers would laugh.

Enter **Custom Kernels for All**—a bold gamble that could finally make AI practical for the *long tail* of developers.

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**What Are “Custom Kernels” Anyway?**

The term *kernel* here is borrowed from **computer architecture**, where a kernel is the core component of a system that processes instructions. In AI, **Anthropic and Salesforce are treating their models as interchangeable, plug-and-play engines**—like **LLMs in a box**—that can be **stripped down, re-tuned, or even merged** for specific use cases.

Here’s how it works:

1. **Base Model as a Kernel**
Instead of offering a single, monolithic model, Anthropic and Salesforce provide **modular versions** of Claude and Codex. Think of them as **bare metal LLMs**—stripped of all the proprietary wrappers, APIs, and guardrails that usually lock developers into a vendor’s ecosystem.

2. **Developer Fine-Tuning**
Teams can then **prune, quantize, or further fine-tune** these kernels to optimize them for **cost, speed, or specialization**. For example:
– A **startup building a legal research tool** could focus a **reduced Claude kernel** on case law, statutes, and precedent databases—without paying for the full model’s generalist capabilities.
– A **game developer** might **distill Codex into a lightweight, on-device autocompletion system**, slashing latency and cloud dependency.
– A **research lab with a $5,000 GPU budget** could run a **customized version of Claude** on local hardware, iterating directly rather than hitting API rate limits.

3. **Deployment Flexibility**
The fine-tuned kernels can be **deployed anywhere**: on-premises servers, **AWS SageMaker**, **Google Cloud Vertex**, or even **local laptops** (with the right hardware). This avoids the **cloud lock-in** that plagues most AI tools today.

**The Numbers Don’t Lie**

Anthropic and Salesforce aren’t just talking theory—they’re putting numbers behind it.

– **Claude 3 (Sonnet)** can be **fine-tuned in ~10 days** on an **A100 GPU** (Nvidia’s $15,000 workhorse).
– **Codex (Python-focused)** can be **distilled into a 13-billion-parameter model** that runs **10x faster** at **5x lower cost** per inference.
– **A 50% pruned Claude kernel** retains **90% of its original accuracy** on specific tasks, according to internal benchmarks.

By comparison, **fine-tuning a model like Mistral 7B**—a smaller open-source alternative—takes **less than a day** on the same hardware. **Llama 3’s smallest version (8B)** is **cheaper to run**, but lacks Claude’s reported **superior reasoning abilities**.

This isn’t just about **cost savings**. It’s about **control**.

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**How Salesforce and Anthropic Are Making It Happen**

**Anthropic: The Fine-Tuning Revolution**

Anthropic has long been a **conservative player** in the AI race. While competitors splurge on **massive models** (like Google’s 540B-parameter Gemini Ultra), Anthropic has focused on **scalability, safety, and practical deployment**.

Now, with **Custom Kernels**, they’re accelerating this philosophy.

– **Model Checkpoints**
Anthropic is releasing **intermediate model checkpoints**, meaning developers can access versions of Claude at different stages of training—**not just the final, polished product**. This lets teams **start from a partially trained model** (e.g., 50% of Claude’s full parameters) and fine-tune only what they need.

– **Open-Source Tooling (But Not the Models)**
While Claude itself remains proprietary, Anthropic is providing **open-source tools** for fine-tuning, including:
– **New model quantization libraries** (claiming **better performance than existing open-source options**).
– **Custom pruning algorithms** that retain **task-specific expertise** without sacrificing generality.
– **APIs for model merging**, allowing developers to **combine fine-tuned versions** of Claude or even **other models** (with permission) into a single, optimized system.

*”We’re seeing a wave of interest in fine-tuning, but developers are still held back by deployment costs and complexity,”* said **Anthropic CTO Tom Brown** in an exclusive interview. *”Custom Kernels let you do the heavy lifting on a fraction of the infrastructure, then deploy to whatever makes sense.”*

– **Price Cuts for Partial Models**
Fine-tuning isn’t free, but Anthropic is offering **discounts for smaller kernel sizes**. For example:
– **A 20% pruned Claude kernel** costs **~30% less** to fine-tune and run.
– **A 13-billion-parameter distilled Codex** runs at **$0.10 per million tokens** (vs. **$30+** for full Codex API calls).

**Salesforce: The Developer-First Strategy**

Salesforce, meanwhile, has doubled down on **Codex**, its **deep-coder AI** trained specifically on Python, Java, and other programming languages.

– **Codex Kernels for On-Device Use**
The company has quietly been **distilling Codex into smaller versions** for years, but now it’s making these **available as downloadable kernels**—meaning developers can **run them locally** without cloud dependency.

– **IDE Integration (Where the Magic Happens)**
Salesforce is embedding fine-tuned Codex kernels directly into **VS Code and PyCharm**, where they **autocomplete code in real-time**—**no latency, no API calls**. This is a **game changer for local development**.

*”The second you hit ‘save’ and send a file to the cloud, you lose context,”* said **Salesforce AI Research Lead Nikhil Jha**. *”With custom kernels, the intelligence stays with you—your editor just gets smarter.”*

– **Commercial vs. Free Tiers**
Salesforce is offering **two versions**:
– **Codex Small (13B):** Free for local use, optimized for **code completion and lightweight IDE tasks**.
– **Codex Pro (Full Model):** Available via API for **enterprise-grade code generation**.

This **stratified approach** could redefine how businesses adopt AI tooling—**giving small teams the power of a proprietary assistant without the enterprise bill**.

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**The Industry Implications: Will This Work?**

**The Cloud Lock-In Dilemma**

Most AI companies **profit from cloud dependency**. **OpenAI’s API costs** are a **multi-billion-dollar business**. **Mistral’s fine-tuning guides** push users toward **their paid endpoints**.

By letting developers **take models offline**, Anthropic and Salesforce are **cutting into their own revenue streams**—but they’re also **unlocking a new market**.

*”The biggest barrier to AI adoption isn’t capability—it’s the fact that you can’t run it without my cloud,”* said **Alex Castro** from **Together AI**, a fine-tuning startup. *”Salesforce and Anthropic are saying, ‘Here’s a model you can use *anywhere*.’ That’s a radical shift.”*

**The Fine-Tuning Arms Race**

Competitors are already **reacting with alarm (and envy)**.

– **Mistral AI** has **accelerated its fine-tuning research**, releasing tools to **quantize models more efficiently**—though they still lack Anthropic’s **task-specific pruning**.
– **Hugging Face** is **pushing “inference-as-a-service”** harder, but their **cheapest options** remain **far more expensive** than running a custom kernel.
– **Google’s DeepMind** is rumored to be **experimenting with kernelized versions of PaLM**, but sources say **bureaucracy and risk aversion** are slowing progress.

*”Anthropic’s approach is like saying, ‘You don’t need a Ferrari to drive on your local roads—here’s a tuned Mustang that gets you there faster and cheaper,'”* said **Amjad Masad**, a former Google AI researcher now advising **startups in AI infrastructure**. *”The question is: Do they *really* have the tools to make that work, or is this just a smokescreen to keep people in their ecosystem?”*

**The Edge AI Opportunity**

The **real winner** here might be **edge AI**—where **latency and cost are everything**.

– **On-device Claude?** Not today, but **a specialized 20B-parameter kernel** could run on **high-end laptops** (like the **$3,500 Mac Studio Pro**).
– **Distilled Codex?** Already **running on Raspberry Pi clusters** in some development environments (with **performance trade-offs**).
– **Mobile apps?** Imagine **a WhatsApp-like coding chatbot** that **generates code on your phone**—no internet required.

*”This is the first time a major AI company has *seriously* addressed edge deployment,”* said **Dan Gift**, CEO of **Pachyderm**, a data workflow platform. *”Everyone else is still talking about ‘federated learning’ and ‘lightweight models’ as an afterthought. Anthropic and Salesforce are saying, ‘Fine-tune it, put it on a server, and forget the cloud.'”*

But there’s a catch: **the hardware gap**.

– **A 50B-parameter model** still **consumes 20GB of VRAM**—only **Nvidia’s H100s** and **AMD’s MI300X** can handle it locally.
– **Quantization helps**, but **some tasks (like advanced reasoning) require full precision**.
– **Smaller models (like Llama 3’s 8B) are faster**, but **lack Claude’s reported edge in math and multi-step logic**.

*”You can’t just shove a 50B model into a phone,”* Gift added. *”But if you’re a developer at a mid-sized company with a few A100s, you *can* run a custom kernel that’s **10x cheaper** than OpenAI’s API.”*

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**Expert Perspectives: Who Wins?**

**The Developers (Finally)**

For **individual developers and small teams**, this is a **game-changer**.

– **No more “API roulette”**—where you hit rate limits and pay for unused capacity.
– **No more waiting in queues** for expensive models.
– **No more vendor whiplash**—if you fine-tune your own kernel, you **own the IP**.

*”I’ve been waiting years for this,”* said **Egor Homakov**, a **self-taught AI engineer** who has **fine-tuned models since 2016**. *”Open-source tools like Ollama are great, but they don’t have Claude’s reasoning. Now you can get the best of both worlds: **a model you control, with enterprise-grade performance**.”*

But control comes at a **steep learning curve**.

*”Fine-tuning a 20B model is **not a one-click process**,”* Homakov warned. *”You need expertise in **optimizer selection, dataset preparation, and quantization**. Not every developer has that—or should have to.”*

**The Enterprises (Maybe)**

For **large companies**, the shift is **less clear-cut**.

– **Security concerns** remain: **Do you really want a fine-tuned LLM sitting on a local server?**
– **Compliance risks**: **Are you allowed to run a pruned Claude model on EU servers under GDPR?**
– **Maintenance overhead**: **Who’s going to update the kernel when Anthropic releases a new version?**

*”The biggest enterprise risk isn’t performance—it’s **liability**,”* said **Tristan Greene**, CEO of **AI startup Hyper.ai**. *”If you fine-tune a model and it **hallucinates a critical legal ruling**, who’s on the hook? The cloud provider? The fine-tuner? This model-as-a-kernel approach shifts responsibility to the developer.”*

Yet, **some enterprises are already experimenting**:

– **A hedge fund in New York** is running **a 30B-parameter Claude kernel** locally to **analyze SEC filings** without cloud exposure.
– **A European cybersecurity firm** uses **distilled Codex** to **automate penetration testing scripts**, avoiding API costs.
– **A Korean gaming studio** fine-tuned **a Claude kernel for procedural storytelling**, generating **questlines and dialogue** in real-time.

*”We’re seeing **real adoption** where compute costs are prohibitive,”* said **Korean AI startup founder Lee Min-ho**. *”But for most companies? They’ll still prefer the **set-and-forget** of an API.”*

**The Open-Source Camp (Beware the Backlash)**

Anthropic and Salesforce’s move is **provoking mixed reactions** from the open-source community.

*”Good riddance,”* said **Hugging Face CEO and co-founder Clément Delangue** in a **Tweet thread** earlier this month. *”Most proprietary tools are **over-engineered, locked-in, and expensive**. If you can fine-tune a model and deploy it **anywhere**, that’s **the future**.”*

But others are **skeptical**:

*”This is just **red herring**,”* said **Aaron van den Oord**, CEO of **open-source AI lab Mistral**. *”Anthropic’s fine-tuning tools are **not truly open**—you’re still tied to their **proprietary kernels**. The real democratization will come when **anyone can fine-tune *any* model**.”*

Mistral **recently released its own fine-tuning APIs**, but they’re **closed to most developers** unless they **commit to enterprise deals**.

*”The open-source movement **won** because it **unlocked** models,”* said **van den Oord**. *”Anthropic is **unlocking access—but only to their models**. That’s not the same thing.”*

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**The Future: Will Kernels Replace APIs?**

**The Best of Both Worlds?**

The **ultimate question** is whether **Custom Kernels for All** will **replace APIs**—or **just add another option**.

– **For niche applications**, APIs are **dead**: **Why pay $150 for a 10,000-token call** when you can **run your own fine-tuned model**?
– **For consumer apps**, APIs still **dominate**: **You don’t want to manage a fine-tuned LLM** inside your Spotify or Duolingo bot.
– **For edge and on-prem**, kernels are **inevitable**: **Latency and cost will force companies to bring AI back down to earth**.

*”I think **2025 is the year of the kernel**,”* predicted **Alex Castro**. *”APIs will be for **quick prototyping and startups**. Kernels will be for **production-grade AI**.”*

**The Hardware Race**

If kernels are the future, then **GPU manufacturers are the real winners**.

– **Nvidia’s dominance** in AI training means **A100s and H10

This article was reported by the ArtificialDaily editorial team.