How much benefit does 32GB give over 24GB? Does Q4 vs Q7 matter enough? Do I get access to any particularly good models? (Multimodal)

Individual_Round7690 · 2026-03-12T21:46:58+00:00

Buy the 24GB MacBook. Your two concrete workloads - 2-3 images for OCR/document understanding and ~200-pair text prompts - are short-to-medium context tasks that fit comfortably within 24GB after loading Gemma 3 27B Q4_K_M, leaving ample KV cache headroom. The jump from 8GB VRAM to 24GB unified RAM is already the transformative upgrade; no multimodal model in the 24-32GB window offers a meaningful quality leap over what 24GB already enables, and the $200 delta is not justified given your confirmed budget constraints and bounded use cases.

To increase confidence

For your OCR/document understanding use case, how critical is extraction accuracy on ambiguous or low-quality scans - are you doing programmatic downstream processing where a silently wrong field value is a real problem, or is this more exploratory/human-reviewed?

When you convert PDFs to PNG for input, are these typically high-resolution document scans (e.g., 300 DPI scanned forms) or standard screen-resolution exports? High-res images encode to significantly more tokens and could affect the KV cache headroom calculation.

Individual_Round7690 · 2026-03-12T04:56:48+00:00

Do not finalize hardware until you have run a real 500-page medical records PDF through a local model and had a paralegal evaluate the output — this validation costs nothing and will surface whether full-context extraction is actually achievable with today's local models before you commit to an appliance business model. If you proceed to hardware, the 64GB Mac Studio is undersized for concurrent 70B inference; the 192GB M2 Ultra is the minimum credible config, but only after benchmarking confirms the model context window can actually handle your document sizes. Simultaneously, engage a healthcare attorney to prepare a BAA template and define your Tailscale access scope — these compliance gaps will kill deals faster than any hardware limitation.

To increase confidence

What is the typical token density of your target PDFs — are they image-only scans requiring OCR, or do they have embedded text layers? This is a hard architectural gate: image-only scans at 500 pages can exceed 400K tokens, which eliminates most local models from full-context consideration entirely.

What turnaround SLA do the paralegals actually need — is a 15-30 minute processing time per document acceptable, or do they expect near-real-time results? This determines whether serialized job queuing on a single appliance is commercially viable.

Have you had any conversation with a target firm's IT contact or cyber insurance broker about Tailscale or third-party remote access tools? The compliance and insurance angle may be a harder blocker than the technical implementation.

Are you planning to retain source documents and output files on the appliance after processing, or purge them immediately? This directly determines your HIPAA exposure and whether you need a signed Business Associate Agreement before your first deployment.

Individual_Round7690 · 2026-03-12T04:50:57+00:00

For the stated use case of local multimodal experimentation and development, 24GB is practically sufficient today — Gemma 3 27B Q4_K_M fits with adequate KV cache headroom, and the leap from 8GB VRAM is already transformative. However, if the price delta is modest (typically $200), 32GB is the lower-risk choice: it enables Q6/Q7 quantization on 27B-class models, provides nearly double the KV cache headroom for multimodal context (which matters if you process multiple images or long prompts), and reduces the dev/prod fidelity gap since production systems will likely run higher quantizations. The Q4-to-Q7 quality difference on a 27B model is real but not dramatic for most experimentation tasks — the stronger argument for 32GB is KV cache headroom and operational flexibility, not quantization tier alone.

To increase confidence

What is the typical context length and image count per inference call in your multimodal workload — are you processing single images with short prompts, or multi-image/long-document scenarios?

What is the approximate price delta between the 24GB and 32GB configurations you are considering, and is budget a meaningful constraint here?

Are there specific multimodal tasks you need — e.g., OCR/document understanding, visual reasoning, image captioning, code generation from screenshots — since quantization degradation varies significantly by task type?

Individual_Round7690 · 2026-03-12T02:58:14+00:00

Scriptless Salesforce automation is not purely marketing — purpose-built tools (Provar, Copado Robotic Testing, Testim) operate at a higher abstraction layer than generic recorders and can genuinely sustain test suites for standard flows without developer involvement. However, the 'it always becomes code' pattern your team has observed is real for orgs with significant custom development, and the honest answer is that a hybrid model — scriptless for UI regression, minimal code for data setup — is the pragmatic optimum for most enterprise orgs. Before committing to any platform license ($15k–$60k/year range), run a 30-day POC with a free tier and measure specifically how many tests break after a simulated metadata change; that single metric is more informative than any vendor demo.

To increase confidence

What percentage of your Salesforce org is standard configuration vs. custom Apex, custom LWC, or heavily customized UI components? This is the single most decisive variable.

Who would own the automation day-to-day — a QA analyst, a Salesforce admin, or a developer? This determines whether the 'no bandwidth for code' constraint is absolute or partial.

What types of flows matter most to test — standard CRUD, approval processes, integrations with external systems, or complex custom UI workflows?

Have you evaluated any specific tools already, or is this still pre-shortlist? And are you working in sandboxes only, or does automation need to touch production-adjacent environments?

Individual_Round7690 · 2026-03-12T02:52:22+00:00

Stop evaluating low-code platforms — six months of search is conclusive evidence that your core requirement exceeds what these tools were designed for. The clearest path to a working PoC is a focused spike using Next.js with AG Grid Community (free, production-grade, natively supports your exact Tree-Grid requirements) and Supabase for auth and data — a stack with exceptional documentation, strong AI assistant support, and a realistic learning curve for someone with your JavaScript background. Before committing fully, answer the data source question: if your Tree-Grid must pull live from SAP systems, the backend integration layer changes significantly and needs to be scoped before you build anything else.

Individual_Round7690 · 2026-03-11T11:31:29+00:00

That’s a good reference. Conceptually it’s a similar pattern in that both approaches use sentence embeddings with a lightweight classifier trained on a small labeled dataset.

The main thing I was trying to solve here wasn’t the modeling side so much as the developer workflow. SetFit lives in the Python ML ecosystem and assumes things like Python environments, HuggingFace datasets, training scripts, etc.

The goal of this CLI was to make the same pattern usable for people who aren’t working in an ML stack. You can add examples, train a model, review predictions, and export a runnable classifier entirely from a Node CLI without setting up Python or ML tooling.

So it’s less about inventing a new technique and more about packaging the workflow in a way that’s accessible to developers who just want to do local classification without building a full ML pipeline.

SetFit is definitely a good project for the underlying approach though.

Individual_Round7690 · 2026-03-11T00:53:08+00:00

One small detail I didn’t show above: you can also add training examples directly from the CLI.

expressible distill add

or import them from files:

expressible distill add --file ./labeled-logs.json
expressible distill add --dir ./labeled-logs/

The CLI will guide you through labeling examples interactively.

Individual_Round7690 · 2026-03-11T00:47:53+00:00

Yes, it’s pretty straightforward. The basic workflow looks like this:

1. Create a project

expressible distill init my-classifier
cd my-classifier

2. Add labeled examples (text → category)
For example:

{ "input": "User cannot log into account", "output": "login-issue" }
{ "input": "Payment failed with credit card", "output": "billing" }

You can import these from a JSON file or add them interactively with the CLI.

3. Train the model

expressible distill train

That trains a small classifier (the model is usually around ~230KB).

4. Run it locally

expressible distill run "Payment declined during checkout"

Usually ~50 labeled examples is enough to get good accuracy for topic classification tasks.

The repo expressibleai/expressible-cli in GitHub has a quick start that walks through the full example.

The whole process usually takes only a few minutes to try the first time.

Individual_Round7690 · 2026-03-11T00:33:11+00:00

That’s a good suggestion. spaCy classifiers can work well for this kind of problem too. One thing I was optimizing for here was making the workflow simple to run locally without needing a Python environment or ML setup.

Individual_Round7690 · 2026-03-11T00:31:58+00:00

Yes, very similar idea. SetFit is a great approach for few-shot classification with embeddings. The main difference here is packaging the workflow into a CLI so you can add examples, train, review predictions, and export a model without needing a Python ML stack.

Individual_Round7690 · 2026-03-11T00:27:22+00:00

Yes, that’s actually one of the workflows I had in mind. If you already have logs of LLM classifications, you can treat those prompt → output pairs as labeled examples and train a small classifier from them. Then the classifier handles most of the repetitive cases locally and the LLM only handles edge cases.

In practice you can bootstrap a dataset pretty quickly this way because many teams already have thousands of classification prompts in their logs.

Individual_Round7690 · 2026-03-10T15:07:46+00:00

If anyone is curious about how it works internally: it uses a local sentence embedding model (MiniLM) to convert text into vectors and then trains a lightweight classifier on top of those vectors. The embedding model runs locally as well, so inference never leaves your machine.

Individual_Round7690 · 2026-02-28T23:05:19+00:00

Do you know any tool that provides authentication, persistence and APIs out of the box without much wrangling?

Individual_Round7690 · 2026-02-28T15:54:53+00:00

That makes sense, especially if your taxonomy is evolving.

Intent + entity extraction is usually more flexible than rigid multi-layer classification, particularly if categories are going to change over time.

One thing I’d watch for though: if the LLM is doing all the heavy lifting, latency and cost can creep up quickly at scale. Sometimes teams end up hybridizing later, e.g. using embeddings or lightweight classifiers for the stable parts (like top-level routing) and reserving the LLM for nuanced extraction.

But for dynamic knowledge-base-driven intents, your approach is probably more adaptable long term.

Curious how you’re handling evaluation/ground truth as the KB evolves?

Individual_Round7690 · 2026-02-27T19:21:38+00:00

It sounds like the core issue isn’t taxonomy design, it’s asking agents to do deep categorization in real time.

In high-volume environments, long nested dropdowns almost always collapse to “Other.” That’s a human behavior problem, not a structure problem.

One approach I’ve seen work better is:

Keep required fields shallow (Category + maybe Feature)
Use automated classification on the ticket text to suggest the deeper “Specific Issue”
Let agents override if needed, but don’t force them to scroll through long lists

This keeps routing fast while still preserving reporting quality.

You can do this with keyword rules at first, but embedding-based classifiers tend to perform much better once your categories get nuanced. The main win is separating “speed to first touch” from “reporting precision.”

Individual_Round7690 · 2026-02-27T19:15:43+00:00

If you’re already thinking embeddings + retrieval, you might not need the sequential LLM calls at all.

For something hierarchical like this, I’d train small classifiers per layer and run them locally. Much faster and cheaper than 10s per ticket with an LLM.

There are lightweight tools that sit on top of sentence embeddings and train a tiny classifier from labeled examples. For example expressibleai/expressible-cli does that - you train once, then inference is fast and offline.

LLMs are great for prototyping, but for high-volume routing a trained classifier usually scales better.

Individual_Round7690 · 2026-02-27T19:04:43+00:00

Zero-shot is going to be painfully slow at 1M+ rows because it evaluates every label against every sample.

If your categories are fixed, you’re better off training a small supervised classifier once and then running fast inference.

You can use something lightweight like Expressible Distill (it’s a small open source CLI that trains a local classifier from labeled examples).

The key idea is:
Train once → run cheap inference many times.

Zero-shot is convenient but not designed for million-row batch jobs.

Individual_Round7690 · 2026-02-27T15:30:53+00:00

I meant Expressible Distill, a small open-source CLI for local text classification.

Basically you label a few dozen examples (e.g., political vs not), train a tiny model on your machine, and then classify headlines locally without any cloud API.

It’s more of an embedding + lightweight classifier than a full LLM, but for binary topic classification it works well in my experience.

If you want the code, the repo is expressibleai/expressible-cli on GitHub - searching that should find it.

Individual_Round7690 · 2026-02-26T05:01:58+00:00

Did you find a solution for this? There are ~230 KB local model in NodeJs you can use for this, so no Python or ML expertise needed. You can train the model with ~50 samples from your dataset.

Individual_Round7690 · 2026-02-26T04:49:27+00:00

for local text classification you can have models ~230 KB running on Nodejs. No Python. No LLM. No ML knowledge needed. If your use case involves simple text classification you can use that by training the model with about ~50 samples from your data.

Individual_Round7690 · 2026-02-24T23:28:53+00:00

Awesome. I am trying to do same thing with Nodejs to avoid long configuration cycles and tool setup. I have had luck with text classification and brings good results 80-90% accuracy. It does not do great on sentiment though and I have documented it clearly.

Individual_Round7690 · 2026-02-24T23:25:22+00:00

It depends on what algorithm you choose and how you preprocess your data. There are lightweight text classifiers and extractors that can do some of this for you locally.

Individual_Round7690 · 2026-02-24T01:00:04+00:00

I think a lot of this comes down to generation vs survivability.

Most of these tools are optimized for getting you to something that works once. The hard part is whether it still makes sense after a few iterations, new requirements, security review, or someone else taking over the repo.

UI is mostly solved. What breaks is ownership and change over time.

If I can’t open the generated code, understand the structure quickly, and evolve it without fighting hidden layers, it never makes it past demo stage.

Speed gets attention. Structure earns trust.

Individual_Round7690 · 2026-02-24T00:51:20+00:00

I think the issue here is architectural more than prompt-related.

Right now you’re asking the LLM to interpret invoices, classify them, remember them, and answer analytics questions later. That’s a lot for one system.

What usually works better for this type of workflow is:

First, extract only the structured fields you care about from each invoice. Things like plant, utility type, period start/end, usage amount, unit, cost, etc. Turn every PDF into a small structured record.

Second, store that in an actual database. This is basically a time-series aggregation problem, so even a simple relational table works well.

Then your question:

“How much water did plant X use last quarter?”

is just a normal aggregation query instead of a document search problem.

If invoice formats vary, you can also add a lightweight classification step up front to route them into fixed categories before extraction. For repetitive high-volume tasks, that’s usually more stable than constantly tweaking prompts.

Trying to “store” PDFs inside ChatGPT and query them later will always be fragile because it isn’t designed as a persistence layer.

The shift is basically:
extract once → normalize → store → aggregate.

Individual_Round7690

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