The EB-2 National Interest Waiver (NIW) is an employment-based, second-preference immigrant visa petition. Its most significant advantage is that it allows an individual to bypass the lengthy and complex PERM labor certification process. This means you do not need a specific job offer or an employer to sponsor you.

Essentially, you are petitioning the U.S. government on your own behalf, arguing that your work is so important to the United States that it's in the national interest to waive the standard requirement of testing the U.S. labor market for a qualified worker.

To qualify for EB-2, you must first meet one of two baseline criteria:

  1. Advanced Degree Holder: You have a master's degree, a doctoral degree, or a bachelor's degree plus five years of progressive, post-baccalaureate work experience in your field.

  2. Exceptional Ability: You can demonstrate a degree of expertise significantly above that ordinarily encountered in the sciences, arts, or business.

Once you meet the EB-2 baseline, you must then prove you qualify for the National Interest Waiver itself by satisfying the legal framework established in the 2016 USCIS case, Matter of Dhanasar.



The Core of the NIW: The Three-Prong Dhanasar Framework

Your entire petition must be structured to prove you meet three specific criteria, known as the "three prongs" of the Dhanasar test. Every piece of evidence you submit should support one or more of these points.

Prong 1: Your Proposed Endeavor has Substantial Merit and National Importance

This prong focuses on the quality and importance of your work, not your personal qualifications.

  • Substantial Merit: Your field of work must be valuable and well-established. This can be demonstrated in areas like STEM, healthcare, technology, defense, economics, education, arts, and culture.

  • National Importance: You must show that the work's impact is not just local but has broader implications for the U.S. as a whole. For example, your research could advance medical science, your engineering work could strengthen national security, or your business could create jobs nationwide.

Prong 2: You are Well-Positioned to Advance the Proposed Endeavor

This prong shifts the focus to you. You must prove you have the skills, knowledge, and track record to make the proposed work happen. This is where you demonstrate your personal expertise and achievements. Evidence can include:

  • Your education, skills, and licenses.

  • A strong record of success and accomplishments in your field.

  • A detailed plan for your future work in the U.S.

  • Progress made toward achieving your goals.

  • Interest from potential users, customers, investors, or other relevant entities in the U.S.

Prong 3: On Balance, it Would be Beneficial to the U.S. to Waive the Job Offer and Labor Certification Requirements

This is the "waiver" prong. You must argue why the U.S. gains more by letting you work immediately in your field than by forcing you through the standard PERM process. Key arguments include:

  • Urgency: Your work addresses an urgent national need (e.g., public health, cybersecurity, supply chain security).

  • Critical Skills: Your unique expertise is critical and not easily articulated in a standard labor certification. The U.S. needs your specific skills, not just any qualified worker.

  • Government Interest: The petitioner’s contributions are of a nature that the U.S. government has a clear interest in (e.g., work for a government agency or on a government contract, as is common in defense).

  • Entrepreneurship: If you are an entrepreneur, a labor certification is not practical, as you are the one creating the job.



Building Your Petition Packet: A Step-by-Step Guide

Your NIW petition is a comprehensive package of forms, legal arguments, and supporting evidence. It should be meticulously organized with a table of contents and exhibits.

Group 1: Core USCIS Forms and Fees

  • Form I-140, Immigrant Petition for Alien Worker: This is the main application form.

    • Filing Fee: $715 (as of late 2025). Always verify the current fee on the official USCIS I-140 page before filing.

  • Form G-1145, E-Notification of Application/Petition Acceptance (Optional but Recommended): This free form allows you to receive a text message or email confirming that USCIS has accepted your packet.

Group 2: The Petition Letter (Your Central Argument)

This is the most critical document, acting as the roadmap for the entire petition. It should be 15-25 pages long and tie all your evidence directly to the three Dhanasar prongs.

  • Introduction: Briefly state your name, your field (e.g., "Senior Project Manager specializing in defense supply chain logistics"), and the purpose of the petition—to seek a National Interest Waiver.

  • Eligibility for EB-2: Clearly state whether you qualify based on an advanced degree or exceptional ability and point to the supporting evidence (e.g., diplomas, transcripts).

  • Argument for the National Interest Waiver: Dedicate a separate section to each of the three Dhanasar prongs.

    • For Prong 1, explain what your endeavor is and why it has substantial merit and national importance.

    • For Prong 2, showcase your accomplishments, skills, and plans to prove you are well-positioned to succeed.

    • For Prong 3, argue why it benefits the U.S. to waive the PERM process for you.

  • Conclusion: Summarize your case and respectfully request that the petition be approved.

Group 3: Essential Personal & Eligibility Documents

  • Identification: A copy of your passport's biographical page.

  • Proof of Status (if in the U.S.): Your most recent I-94 travel record, visa stamps, and any approval notices (e.g., I-797 for H-1B).

  • Proof of EB-2 Qualification:

    • Copies of your degree certificates (bachelor's, master's, Ph.D.).

    • Official academic transcripts for all degrees.

    • If qualifying with a bachelor's + 5 years of experience, include letters from previous employers detailing your progressive work history.

Group 4: Evidence of Your Impact (Supporting the Dhanasar Prongs)

This is the bulk of your packet. Organize it logically into exhibits.

Subgroup 4.1: Letters of Recommendation (Expert Testimony)

These letters are crucial for validating your claims. Aim for 4-7 strong letters.

  • Who to Ask:

    • Independent Experts: These carry the most weight. They are experts in your field who know you through your reputation or work but have not directly supervised or collaborated with you (e.g., a professor who cited your paper, a senior industry leader).

    • Dependent Experts: These are people you have worked with (e.g., current or former supervisors, senior colleagues, high-level clients).

    • Examples for a Defense Professional: Senior executives at your company, engineering leads, program managers on military contracts, officials from Tier 1 or Tier 2 suppliers, retired military officials, or defense analysts.

  • Content of the Letters: Each letter should be unique and provide specific, concrete examples. They must address your contributions in the context of the Dhanasar prongs:

    • Explain your specific role and key achievements.

    • Detail the national importance of your work (e.g., "Their management of the supply chain for Project X was critical to ensuring our naval fleet's readiness...").

    • Confirm you are a leading figure and well-positioned to continue this work.

    • Emphasize that your skills are unique and critical, implying that a labor certification would be inadequate.

Subgroup 4.2: Objective Documentary Evidence

This is where you provide tangible proof of your accomplishments.

  • Professional History: Detailed resume or CV, employment verification letters, and detailed job descriptions that highlight your critical responsibilities.

  • Tangible Output:

    • Published research, articles, and book chapters.

    • Evidence of citations to your work (e.g., Google Scholar profile).

    • Patents filed or granted.

    • Conference presentations or invited talks.

  • Recognition and Impact:

    • Awards and honors received in your field.

    • Proof of membership in professional organizations that require significant achievements for entry.

    • Media coverage or press releases about you or your projects.

    • Evidence of funding, grants, or contracts you have managed or secured.

    • Quantitative Metrics: This is vital. Provide data showing your impact—cost savings, efficiency improvements, revenue generated, project delivery timelines, risk mitigation, etc. For example: "Led a team that reduced production time for a critical defense component by 20%, saving an estimated $1.2M annually."



Special Strategy: Handling Cases with Sensitive or Non-Public Information

For professionals in fields like national defense, proprietary corporate R&D, or intelligence, traditional evidence like publications is often unavailable. This is not a barrier if addressed correctly.

The Strategy

  1. Directly Address the Absence of Publications: In your petition letter, explicitly state that due to the sensitive, classified, or proprietary nature of your work, public dissemination is not possible and is contrary to industry norms and national security interests.

  2. Substitute with Powerful Alternative Evidence: Compensate for the lack of public evidence by strengthening other areas.

    • Redacted Contracts & Project Documents: Provide contracts or internal project summaries (with sensitive details redacted) that show your company supplies critical components to U.S. government agencies or military branches. Highlight your name and managerial role.

    • Internal Memos & Performance Reviews: Include official company documents that praise your contributions, reliability, and the importance of your role in mission-critical projects.

    • Letters as Expert Testimony: Your recommendation letters become even more critical. They must act as a substitute for peer review, with experts attesting to the innovation, complexity, and national importance of your confidential work without revealing classified details.

  3. Focus on Your Indispensable Role: Frame your petition around your unique and critical skills. Argue that your expertise in managing complex, sensitive projects is not easily replaceable and is essential for maintaining U.S. strategic advantages.



Strategic, Financial, and Timeline Considerations

DIY vs. Hiring an Immigration Attorney

  • Do-It-Yourself (DIY):

    • Pros: Cost-effective. You save thousands in legal fees.

    • Cons: Extremely time-consuming. High risk of making critical mistakes in legal arguments or evidence presentation. It's difficult to be objective about your own accomplishments.

    • Cost: ~$715 (I-140 filing fee).

  • Hiring an Attorney:

    • Pros: Expertise in framing your profile to meet the Dhanasar criteria. Higher probability of success. Saves you immense time and stress. They know exactly what USCIS adjudicators look for.

    • Cons: Significant cost.

    • Cost: Legal fees typically range from $5,000 to $12,000.

  • ⚠️ A Note on "Consultants": Be wary of services offered by non-attorney "consultants." They cannot provide legal advice, and their services may constitute the unauthorized practice of law. This is an area where unethical practices can occur. Always verify that you are working with a licensed attorney in good standing.

Costs and Timelines (as of late 2025)

ItemCost (USD)Processing TimeNotes
I-140 Filing Fee$715Regular: 6 - 18+ monthsHighly variable. Check USCIS processing times.
I-907 Premium Processing$2,80545 calendar daysOptional add-on for a guaranteed decision (approval, RFE, or denial).
Attorney Fees$5,000 - $12,000N/AVaries by firm and case complexity.
Petition Prep TimeN/A2 - 6 monthsTime for you (or your lawyer) to gather documents and draft letters.
I-485 Adjustment of Status~$1,4406 - 24+ monthsFiled after I-140 approval if your priority date is current.

Practical Probabilities: The success rate for EB-2 NIW is generally high, especially for well-prepared cases from STEM and other critical fields. However, approval is never guaranteed. A strong, well-documented petition prepared with legal guidance has a significantly higher probability of success than a hastily assembled DIY case.

Executive summary (one paragraph)

  • If your goal is a single big local LLM (70B+) with low latency and simple management → pick a single high-memory Apple Ultra (M2 Ultra / M3 Ultra / equivalent Mac Studio) because unified RAM + on-chip bandwidth dominate performance and avoid network sharding pain. (Apple)

  • If your goal is many concurrent smaller model sessions (13B/30B) or low-cost horizontal scaling → a cluster of M4/M3 Pro Mac minis (each running its own instance) is often better: cheaper per seat, lower power, and operationally simpler for multi-tenant serving. (Apple)

  • Distributed single-model sharding across many minis is possible (EXO, llama.cpp RPC, etc.) but is experimental: you’ll be network-bound and face engineering complexity. (GitHub)


1) Hardware facts that matter (accurate, current, and why they affect LLM inference)

1.1 Core hardware numbers (most load-bearing facts)

  • M2 Ultra — configurable up to 192 GB unified memory and 800 GB/s memory bandwidth (one of the main reasons it’s best for very large models). (Apple)

  • M4 family — Apple advertises the M4 Neural Engine as capable of ~38 trillion operations/sec; M4 Pro / M4 Max offer significantly higher memory bandwidth depending on SKU (e.g., M4 Max 410–546 GB/s on laptop/Studio SKUs). Use Core ML / MLC/Metal to leverage these accelerators. (Apple)

  • Mac mini (M4 Pro) — configurable to 48 GB or 64 GB unified memory, and the Mac mini M4 Pro model includes 10 Gb Ethernet on higher SKUs. That 10GbE link is important for cluster network planning. (Apple)

Why those numbers matter:

  • Unified memory size sets the maximum model footprint you can hold in RAM (weights + activations + working memory). If the model + runtime + context > RAM, you’ll swap or fail.

  • Memory bandwidth (hundreds of GB/s) controls how fast the SoC can stream weights/activations — on-chip bandwidth is orders of magnitude higher than network bandwidth (see network section).

  • Neural Engine / Core ML / Metal: if you can convert a model or use a runtime that targets Apple’s Neural Engine or ML accelerators, you get major latency/throughput wins per watt — but that depends on tooling and model compatibility. (Apple)


2) Models: Gemini availability & local equivalents (legal + practical)

  • Gemini 2.5 Flash (Google) is a hosted model available via Google Vertex AI / Vertex/AI Studio and Google Cloud — Google does not publish Gemini 2.5 Flash downloadable weights for local hosting, so you cannot legally run it natively unless Google releases the weights. Use Google’s API for Gemini. (Google Cloud)

  • Open / local equivalents that practitioners commonly use (good candidates for on-device, RAG or host locally):

    • Mixtral / Mistral family (e.g., Mixtral-8x7B): strong efficiency and reasoning-per-compute. (Hugging Face)

    • DeepSeek-R1 family: open reasoning-focused models (now on Hugging Face) with strong chain-of-thought capabilities. (Hugging Face)

    • Qwen family & other open releases — good candidates depending on license.

  • Practical rule: if you need Gemini-level quality but local hosting, choose the top open model that fits your hardware, use quantization (GGUF q4 etc.), and combine with RAG to reduce hallucinations.


3) EXO / EXO Labs and distributed inference

  • What it is: EXO (exo-explore/exo) is an open-source project/toolkit to assemble heterogeneous consumer devices into a single inference cluster (phones, Macs, Raspberry Pi, GPUs). Use it to experiment with horizontal scaling on commodity hardware. It’s active OSS and community driven. (GitHub)

  • What it enables: horizontal pooling of compute — good for running many independent model instances or experimental multi-device sharding.

  • What it doesn’t magically solve: network bandwidth and synchronization overhead — sharding a single model across many devices will generally be network-bound unless you have extremely low-latency, very high-bandwidth links and the software to avoid frequent cross-node transfers. (GitHub)


4) Single machine vs cluster: a real tradeoff table

Goal Best hardware pattern Why / pros Cons / caveats
Run one big model (70B+) for low latency Single M2 Ultra / Ultra-class Mac Studio with 128–192GB unified RAM Model fits in memory; massive on-chip bandwidth; simpler to manage. (Apple) High upfront cost; heavy power draw at max load.
Many concurrent small/mid sessions (13B/30B) Cluster of M4/M3 Pro minis running independent instances Linear scale by node; cheaper per seat; easy redundancy; low idle power. (Apple) Not great for a single monolithic model; network orchestration for distributed work.
Run proprietary/cloud-only models (Gemini 2.5 Flash) locally Not possible — call provider API (Vertex AI) Leverage provider SLAs, updated models Ongoing cloud cost; latency to cloud; data residency concerns. (Google Cloud)
Shard single model over many consumer devices (EXO) EXO / custom RPC Innovative, low-cost experiment Experimental; network bottleneck; engineering overhead. (GitHub)

5) Network math — why sharding stalls

  • 10 Gb Ethernet (common on Mac mini M4 Pro higher SKU) = 10 Gbit/s = 1.25 GB/s raw (before protocol overhead). That’s ~1–2 GB/s in practice. Compare this with on-chip memory bandwidth for Ultra/M4 (hundreds of GB/s) — network is orders of magnitude slower for the kind of weight/activation movement required during inference. Sharded single-model inference therefore easily becomes network-bound. (Use local on-node inference when possible.) (Apple)


6) Software stack realities (what gives the performance)

  • llama.cpp (ggml/gguf): de-facto for CPU + Metal offload hobbyist inference; supports many quant formats (q4/q8/...); easiest for raw local CPU inference. Good for macOS. (Used in our earlier FastAPI stack.)

  • llama-cpp-python: Python bindings — convenient.

  • mlc-llm / MLX / Core ML: compilers and runtimes that target Metal and the Neural Engine; can give big throughput wins for converted models. (Use when you can convert model to Core ML / MLC format.)

  • Ollama / Ollama-like local servers: easy onboarding and local model management for macOS (pull & serve).

  • Distributed / RPC: llama.cpp has some experimental RPC/distributed features; EXO is another route. All of these are still experimental for single-model sharding — expect friction. (Apple)


7) Practical deployment guidance for your website + local LLM (concrete)

  1. Start simple (recommended path)

    • Use a single node (Mac mini M4 Pro or a Mac Studio M2 Ultra depending on model size) to host a local LLM server (llama.cpp / mlc-llm / Ollama).

    • Build a RAG pipeline: web scraper → chunking → embeddings (sentence-transformers or OpenAI embeddings) → vector DB (pgvector) → local LLM to synthesize+cite. (This is what I already gave you code for.)

    • Pros: simple, reliable, low engineering cost; works for most product use cases.

  2. If you need multi-tenant scale

    • Run multiple minis as independent containers (each runs its own 13B/30B instance) behind a load-balancer / router. That scales well and avoids sharding. Estimate linear throughput increase per node. (Apple)

  3. If you insist on sharding a single huge model across nodes

    • Use EXO or a custom RPC + llama.cpp sharding + very fast LAN (25GbE or better) — but expect performance loss vs single Ultra due to network and orchestration overhead. This is experimental; only attempt if you have engineering time and monitoring. (GitHub)


8) Costs, ETAs, and probabilities (realistic estimates you can use for budgeting and planning)

Important: hardware/software prices and availability change. I cite current prices and official specs — re-check before purchase.

8.1 Hardware cost examples (US retail / Apple list ranges)

  • Mac mini M4 Pro (48–64GB) — typical configured price ~$1,799–$2,199 for 48–64GB configurations (retail varies). (Apple / reseller pages show M4 Pro mini configs and typical store prices). (AppleInsider)

  • Mac Studio with M2 Ultra (64GB start, configurable to 192GB)Mac Studio with M2 Ultra starts at about $3,999 for base Ultra configurations; fully configured 192GB units are much more (varies by storage/RAM). Expect $4k–$10k+ depending on RAM/SSD. (TechRadar)

8.2 Power & hosting cost (practical)

  • Mac mini (M4 Pro) idle power is very low (single-digit watts in many reports) and full-load typically under ~60–70W in real reviews — best for home/colocated clusters. (Jeff Geerling)

  • Mac Studio (Ultra) peak/system max power can be much higher (Apple pages list maximum continuous power ratings; measured max draws reported <300–480W depending on config) — budget for higher electricity if you run 24/7. (Apple)

8.3 ETA / project schedule estimates (realistic)

  • Get a basic RAG + local LLM prototype running (single Mac mini + FastAPI + llama.cpp + pgvector): 1–3 days for an experienced dev (includes model download + ingest a few pages). (You already have the code stack I gave earlier.)

  • Productionize (monitoring, autoscaling, SSL, hardening, more data): 2–6 weeks (depends on QA, rate limiting, privacy/legal reviews).

  • Set up a small mini cluster for multi-tenant 13B instances (N=3–8): 1–3 weeks including orchestration (docker/k8s), LB, and testing.

  • Attempt single-model sharding across devices (EXO / custom): several weeks → months of engineering (experimental; high variance). Probability of acceptable performance vs single Ultra: low unless you have very low latency network and sophisticated sharding. (GitHub)

8.4 Probability / risk estimates (high-level)

  • Chance a 70B quant model will run comfortably on a single M4 Pro mini: very low (<5%) — RAM is the limiting agent (mini max 48–64GB). Use Ultra or cluster. (Apple)

  • Chance cluster of M4 Pro minis outperforms a single Ultra for many independent 13B sessions: high (70–90%) — horizontal scale is linear for independent instances (cost/performance tradeoff favors minis for many seats). (Apple)

  • Chance you can shard a single 70B model across many minis and get lower latency than single Ultra: low (<20%) — network bottleneck and orchestration overhead usually prevents this. (GitHub)


9) Practical implementation checklist (step-by-step, actionable)

  1. Choose initial model & node

    • If you want max simplicity -> start with Mixtral-8x7B (7B family) or a quantized Llama-2/3 13B (GGUF q4) if you have 32GB+. Use this for RAG. (Hugging Face)

  2. Choose runtime

    • llama.cpp / llama-cpp-python for quick CPU/Metal usage.

    • mlc-llm if you plan to target Metal/Neural Engine for latency.

    • Ollama for easiest on-macone click start.

  3. Build RAG (you already have code):

    • Scraper → chunker → embedder (all-MiniLM or larger) → pgvector index → local LLM server → FastAPI endpoint → frontend.

  4. Test scale

    • Test with synthetic concurrent users. If you need linear scale, add N minis running independent instances. If you need one big model, buy an Ultra Mac Studio or use a cloud GPU.

  5. If you need more capacity later

    • Options (in order of complexity and reliability): buy Ultra Mac Studiorent cloud GPU instancesbuild a mini cluster with EXO + fast interconnect (experimental).


10) Useful links & where to read more (contextual)

  • Apple M2 Ultra announcement / specs (memory, bandwidth). (Apple)

  • Apple M4 introduction (Neural Engine 38 trillion ops/sec). (Apple)

  • Mac mini (M4 / M4 Pro) official tech specs (memory configs, 10GbE). (Apple)

  • Google Vertex AI / Gemini 2.5 Flash (hosted API; no local weights). (Google Cloud)

  • EXO GitHub (run your own AI cluster / EXO Labs). (GitHub)

  • Mixtral-8x7B model info (Mistral). (Hugging Face)

  • DeepSeek-R1 model on Hugging Face (open reasoning family). (Hugging Face)

(Click the citation links next to the statement for the authoritative page.)


11) Final practical recommendations (pick one based on goals)

  1. If you want to ship a stable web product fast (my top recommendation for most):

    • Start 7B–13B quantized model on a Mac mini M4 Pro (48GB), implement the RAG stack (pgvector+sentence-transformers + llama.cpp), use the FastAPI stack you requested earlier. Scale horizontally with more minis if traffic grows.

  2. If you want a single, best local single-model experience (no network sharding complexity):

    • Invest in a Mac Studio with Ultra-class chip and large RAM (M2 Ultra / M3 Ultra / M4 Ultra when available with 128–192GB) and run a quantized 70B+ model locally.

  3. If you want to experiment with home clusters / bleeding edge:

    • Explore EXO + a handful of minis / devices, but treat this as R&D — expect to spend significant engineering time optimizing sharding and networking.

Bigger models (13B, 30B, 70B+) are not just “more context” — they usually reason better, know more, follow instructions more reliably, and produce higher-quality, less-noisy outputs. A well-tuned 7B can be excellent for many tasks (fast, cheap, low memory), and when paired with a good RAG pipeline it can be surprisingly effective — but it will often lose on hard reasoning, long multi-step chains, and subtle instruction-following compared with larger models.

Why size matters (what larger models get you)

  • Better reasoning & chain-of-thought — larger parameter counts give the model more internal capacity to represent complex patterns and multi-step reasoning. That shows up in fewer factual mistakes and better multi-step answers.

  • Stronger instruction following / safety behavior — bigger models usually generalize better to new prompts and follow instructions more robustly without brittle prompt engineering.

  • Richer world knowledge — larger models (trained on more data / capacity) tend to remember more facts and subtler patterns.

  • Better zero-/few-shot performance — tasks where you give few examples often improve with scale.

  • Higher ceiling for fine-tuning / LoRA / instruction-tuning — bigger base models obtain larger gains from additional tuning.

What size doesn't directly change

  • Context window is orthogonal to parameter count — a model’s maximum context (4k, 32k, 64k tokens…) depends on its architecture/training, not simply whether it’s 7B or 70B. You can have 7B models with huge context windows and large models with short windows.

  • Perfect factuality — larger models reduce but do not eliminate hallucination. Retrieval (RAG) + citation is still important.

Quantization: how it helps and its costs

  • Benefit: quantization (q4/q8 etc.) reduces model memory and CV/cache requirements, letting you run larger models on constrained hardware. You can often run a 13B/30B quantized model where a non-quantized model wouldn’t fit.

  • Cost: small drop in numeric precision can slightly reduce answer quality, especially on fine distinctions — but modern quant schemes (GGUF q4_k/q4_0 etc.) often have negligible quality loss for many tasks.

  • Net: quantized large model (e.g., 13B q4) frequently outperforms a non-quantized 7B on reasoning tasks.

When 7B is the right choice

  • Low-latency, cost-sensitive apps (chat UI, interactive agents).

  • Tasks that are narrow and well-covered by retrieval (FAQ bots, short summarization) — combined with RAG, 7B often suffices.

  • Limited hardware (e.g., 16–32 GB RAM) where larger models cannot fit even quantized.

When you should pick larger (13B / 30B / 70B)

  • Complex multi-step reasoning, coding, math, or tasks requiring deeper world knowledge.

  • When you want fewer prompt hacks and better generalization out-of-the-box.

  • If you can host larger quantized models (≥64GB unified mem for comfortable 70B use; 13B/30B comfortable on 32–64GB with quantization).

Practical hybrid strategies (get the best of both worlds)

  • RAG (retrieval + small model): use 7B + a good retriever/vector DB. This often gives excellent factual answers with citations, and it’s very resource efficient. Great for web-query → answer pipelines.

  • Model cascades: do cheap/fast first pass with 7B; if confidence is low or complexity high, escalate to 13B/30B/70B.

  • Distill or LoRA: distill larger model behavior into a smaller model or apply LoRA to improve a 7B on your domain.

  • Quantized large models: run 13B/30B quantized models if your hardware supports them — you’ll usually get a noticeable quality jump over 7B for moderate extra resources.

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