Here's the schema — a single universal rubric you can apply to score any local-AI candidate (a GPU, a model, a serving framework, a deployment) against the same structural skeleton, so every comparison in this whole conversation collapses into one repeatable due-diligence instrument rather than nine separate narratives.
Each row follows the pattern Feature :: Function :: Spec/Metric :: Value(Range/Band/Qty) :: Similar/Related/Other. "Feature" is the atomic attribute being measured (it should not overlap with any other row — that's the MECE discipline). "Function" is what that attribute actually does for you operationally. "Spec/Metric" is the measurable unit. "Value" is the realistic 2026 band, not a single number, since real hardware/software spans a range. "Similar/Related/Other" cross-links the attribute to adjacent decisions so you see what it trades off against. This mirrors the discipline used in vendor-neutral edge-AI scoring: define criteria once, weight them per your use case, score every candidate against the identical rubric, and never trust a vendor's own reference numbers over your own measured workload. hyperion-consulting
The nine domains below are mutually exclusive (no attribute appears twice) and collectively exhaustive (nothing relevant to local-AI due diligence falls outside them).
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Raw compute | Determines theoretical max math throughput | TOPS/TFLOPS (INT8/FP16) | 20-85 TOPS (NPU edge) / 200-2000+ TFLOPS (datacenter GPU) | Related to memory bandwidth (Domain B); Other: thermal envelope | TOPS alone is marketing bait — it's necessary but not sufficient; always pair with bandwidth localaimaster |
| Memory bandwidth | Governs actual tokens/sec once model is resident | GB/s | 120 GB/s (Intel NPU) to 819 GB/s (Mac Studio M5 Ultra) to 3,000+ GB/s (H200) | Similar to disk I/O bottleneck in training (Domain C) | This is the real throughput governor, not TOPS — score this first localaimaster |
| Compute class | Defines what tier of workload is feasible | NPU / GPU / TPU / CPU-only | Edge NPU, consumer GPU, datacenter GPU, CPU fallback | Related to power envelope and form factor | NPU wins on battery efficiency; GPU wins on raw model-size ceiling; CPU is the universal fallback floor watercrawl |
| Multi-chip scaling efficiency | Measures real gain per added GPU | Tokens/sec per doubling of GPU count | ~1.7-1.8x per doubling (sub-linear due to NVLink sync) | Other: interconnect bandwidth (NVLink/PCIe) | Never assume linear scaling when budgeting multi-GPU clusters hosn |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Model-resident memory | Holds weights for inference | GB required per model size | FP16: ~2GB/B params; INT8: ~1GB/B; INT4: ~0.5GB/B | Related to quantization format (Domain C) | Use this as your first filter — if it doesn't fit, nothing else matters navyaai |
| KV cache memory | Holds per-session context state | GB, scales with context length × concurrent users | Often the actual ceiling before compute is exhausted | Similar to RAM headroom; Other: prefix-cache dedup gains | Budget 20-30% extra headroom here — this is what silently caps your concurrent-user count hosn |
| Unified vs discrete memory | Determines flexibility of allocation | Unified (Apple/AMD APU) vs VRAM (NVIDIA discrete) | 8GB (edge) to 512GB (Mac Studio Ultra) | Related to training feasibility (Domain D) | Unified memory wins for max model size on one box; discrete VRAM wins for raw training speed presenc |
| Storage throughput | Affects checkpoint I/O and dataset load | NVMe sequential read/write, capacity | 4TB+ recommended for serious fine-tuning workflows | Other: checkpoint frequency strategy | Slow NVMe adds 20-40% wall-clock time to training runs — don't skimp here presenc |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Parameter count | Sets baseline capability ceiling | Billions of parameters | 1B (edge) to 120B+ (frontier local) | Related to memory (Domain B) | Bigger isn't always better — match to actual task complexity, not prestige |
| Quantization format | Compresses model to fit memory | GGUF / GPTQ / AWQ / NF4 | 4-bit to 8-bit common; Q4_K_M is community default | Similar to lossy compression tradeoffs | GGUF for flexible CPU/GPU split; GPTQ/AWQ for GPU-only max-throughput serving vettedconsumer |
| Accuracy retention | Measures quality loss from compression | Perplexity delta vs FP16 | ~1% loss at Q4_K_M; degrades faster on reasoning/coding tasks | Related to task type (chat vs agentic vs coding) | Step up to Q5/Q6 specifically for agentic, coding, or long-context workloads vettedconsumer |
| Fine-tuning method | Determines trainable scope and memory cost | Full FT / LoRA / QLoRA | Full FT: ~6x model size; LoRA: ~1.2x; QLoRA: ~0.4x | Other: training time per epoch | QLoRA is the only realistic path to 70B+ fine-tuning on a single workstation presenc |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Batching architecture | Determines concurrent-request efficiency | Continuous batching / static batching / none | vLLM/TGI: continuous; Ollama: limited | Related to concurrency band (Domain F) | This single attribute explains most of the throughput gap between frameworks morgannriu |
| Hardware compatibility | Defines which silicon the framework supports | GPU-only / GPU+CPU / CPU-only | vLLM: GPU; llama.cpp: CPU+GPU; both cover edge to datacenter | Similar to driver/runtime maturity (CUDA vs ROCm vs MLX) | Pick the framework that matches your hardware ceiling, not the most popular one |
| Throughput multiplier | Quantifies real-world speed vs baseline | Tokens/sec at concurrency | vLLM ~793 tok/s vs Ollama ~41 tok/s at high concurrency; gap <10% at single-user | Other: ease-of-setup tradeoff | If you're solo, the framework choice barely matters; if you're serving a team, it matters enormously morgannriu |
| Operational complexity | Measures setup/maintenance burden | Low/Medium/High | Ollama: low; vLLM/TGI: medium-high; Triton/Ray Serve: high | Related to team size and DevOps maturity | Match complexity tolerance to whether you have dedicated infra staff watercrawl |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Topology type | Defines where compute physically lives | Single-device / single-rig / multi-rig on-prem / distributed mesh | Solo laptop to departmental cluster to federated mesh | Related to all other domains simultaneously | This is your master filter — pick topology before pricing anything |
| Interconnect | Governs multi-GPU/multi-node scaling | NVLink / PCIe / Ethernet / WAN | NVLink: highest; WAN mesh: lowest and least reliable | Other: latency sensitivity of workload | Federated/mesh setups (Petals, Exo, Kalavai) lack production-grade health monitoring as of 2026 — treat as beta, not infrastructure sharedllm |
| Air-gap capability | Determines data sovereignty compliance | Fully air-gapped / hybrid / cloud-dependent | On-prem rigs can be fully air-gapped; mesh networks cannot | Related to compliance domain (Domain H) | If sovereignty/compliance is non-negotiable, distributed mesh options are currently disqualified |
| Network resilience | Affects mesh/distributed reliability | Worker churn tolerance, resume capability | Largely unsolved across all current distributed frameworks | Other: coordinator durability | Don't build production dependency on Petals/Exo/Kalavai/SharedLLM yet — verified as alpha/beta only sharedllm |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Concurrent users per GPU | Sets real-world seat capacity | Users at target latency | 7B model: 5-50 users/GPU depending on class; 70B: 60-500 across multi-GPU | Related to KV cache (Domain B) | Size for peak concurrency (10-15% of total users), never for total headcount linkedin |
| Active-to-total user ratio | Converts headcount into sizing requirement | Ratio | Typical 1:10 to 1:20 | Other: usage pattern variance by org | Multiply your total user count by this ratio before buying hardware iternal |
| Latency target | Defines acceptable user experience | First-token latency (ms) | 50-280ms depending on GPU class and load | Similar to throughput tokens/sec | Agentic/long-context workloads can multiply compute demand 5-10x — budget a separate SLA tier linkedin |
| Workload multiplier | Accounts for non-chat task overhead | Multiplier vs simple chat | 1x (chat) to 5-10x (agentic/long-context) | Related to quantization accuracy loss (Domain C) | The single biggest source of capacity-planning surprises in production linkedin |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Upfront hardware cost | Capital outlay | USD | $499 (Jetson Orin Nano) to $300,000+ (8x H100 DGX) | Related to depreciation schedule | Treat as CapEx amortized over 3 years minimum |
| Per-million-token cost | Operating cost comparator vs cloud | USD/million tokens | On-prem ~$0.47-1.24/M tokens (optimized) vs cloud ~$0.60/M tokens | Other: break-even volume threshold | On-prem only wins economically above a sustained volume threshold — model your actual token volume before committing navyaai |
| Break-even point | Determines cloud vs on-prem crossover | Tokens/month or months-to-breakeven | Varies widely by deployment size; right-sizing GPUs can flip the economics | Related to quantization (lowers GPU count needed) | Quantization discipline can be the difference between on-prem winning or losing the TCO argument navyaai |
| Power/cooling overhead | Recurring operational cost | kWh and facility cost | Scales with GPU class; datacenter-tier GPUs need real cooling planning | Other: facility/rack constraints | Often underestimated in SMB on-prem budgeting — get a real power audit, not an estimate |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Data sovereignty | Controls where data physically resides | Air-gapped / on-prem / hybrid / cloud | Full control on-prem; partial on hybrid; none on cloud API | Related to topology (Domain E) | Non-negotiable for regulated industries — disqualifies cloud and unverified mesh options |
| Maturity status | Indicates production-readiness | Alpha / Beta / Production | vLLM/TGI: production; Petals/Exo/Kalavai/SharedLLM: alpha-beta | Other: community support size | Never put alpha/beta-status software on a critical path without a fallback plan sharedllm |
| Auditability | Enables compliance verification | Logging, lineage tracking | Varies widely by framework — most OSS frameworks require add-on tooling | Related to license type | Check this explicitly if you're in healthcare, finance, or government contexts |
| License type | Determines legal usage constraints | MIT / Apache-2.0 / AGPL-3.0 / GPL-3.0 | Petals: MIT; Kalavai: Apache; SharedLLM: AGPL; Exo: GPL | Other: commercial-use restrictions | AGPL/GPL can impose obligations on derivative commercial products — verify with legal before production use sharedllm |
| Feature | Function | Spec/Metric | Value (Range/Band) | Similar/Related/Other | Usage Guidance |
|---|---|---|---|---|---|
| Driver/runtime maturity | Affects long-term stability | CUDA / ROCm / MLX ecosystem age | CUDA: most mature; ROCm: improving; MLX: newest, Apple-only | Related to hardware compute class (Domain A) | Ecosystem maturity often outweighs raw hardware specs in real-world reliability |
| Upgrade path | Determines future scalability | Single-GPU to multi-GPU to cluster | Some topologies (Mac Studio) cap out; others (H100 rigs) scale linearly via additional cards | Other: vendor lock-in risk | Buy into the topology that matches your 2-3 year growth trajectory, not just today's load |
| Community/support depth | Affects troubleshooting speed | GitHub activity, documentation depth | vLLM/llama.cpp: large active communities; distributed mesh projects: small, niche | Related to maturity status (Domain H) | Weight this heavily for solo operators without dedicated infra teams |
Score every candidate (a specific GPU, model, framework, or full deployment) against the same 36 atomic rows above, using a simple 1-5 weighted scale per row based on what matters for your specific category (SMB on-prem, solo R&D, or on-device) — this is the exact discipline vendor-neutral evaluation frameworks recommend: define criteria once, weight per use case, score every candidate identically, and always validate against your own measured workload rather than vendor reference numbers. For rubric-based evaluation specifically of model outputs (not just hardware), the same atomic, per-criterion scoring discipline — binary/ordinal/nominal criteria with configurable weights, avoiding criterion conflation — is the current best-practice structure in formal LLM evaluation research. The MECE property of this schema means a "good" or "bad" score in one domain never silently substitutes for a deficiency in another — a frontier GPU (Domain A) cannot compensate for an alpha-stage distributed framework (Domain H), and high TOPS (Domain A) cannot compensate for low memory bandwidth (Domain A's own internal sub-metric) — so a true qualification decision requires a passing or acceptable score across all nine domains relevant to your category, not just the one or two that feel most exciting. scale.stanford
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A bit about David Youngblood...
David is a Partner, Father, Student, and Teacher, embodying the essence of a true polyoptic polymath and problem solver. As a Generative AI Prompt Engineer, Language Programmer, Context-Architect, and Artist, David seamlessly integrates technology, creativity, and strategic thinking to co-create systems of enablement and allowance that enhance experiences for everyone.
As a serial autodidact, David thrives on continuous learning and intellectual growth, constantly expanding his knowledge across diverse fields. His multifaceted career spans technology, sales, and the creative arts, showcasing his adaptability and relentless pursuit of excellence. At LouminAI Labs, David leads research initiatives that bridge the gap between advanced AI technologies and practical, impactful applications.
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David believes in the power of collaboration and principled responsibility in leveraging AI for the greater good. He challenges the status quo, inspired by the spirit of the "crazy ones" who push humanity forward. His commitment to meritocracy, excellence, and intelligence drives his approach to both personal and professional endeavors.
"Here’s to the crazy ones, the misfits, the rebels, the troublemakers, the round pegs in the square holes… the ones who see things differently; they’re not fond of rules, and they have no respect for the status quo… They push the human race forward, and while some may see them as the crazy ones, we see genius, because the people who are crazy enough to think that they can change the world, are the ones who do." — Apple, 1997
Why I Exist? To experience life in every way, at every moment. To "BE".
What I Love to Do While Existing? Co-creating here, in our collective, combined, and interoperably shared experience.
How Do I Choose to Experience My Existence? I choose to do what I love. I love to co-create systems of enablement and allowance that help enhance anyone's experience.
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When & Where Does All of This Take Place? Everywhere, in every moment, of every day. It's a very fulfilling place to be... I'm learning to be better about observing it as it occurs.
I've learned a few overarching principles that now govern most of my day-to-day decision-making when it comes to how I choose to invest my time and who I choose to share it with:
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- Response Over Reaction: Take pause and choose how to respond from the center, rather than simply react from habit, instinct, or emotion.
- Progress Over Perfection: One of the greatest inhibitors of growth.
- Inspired by "7 Habits of Highly Effective People": Integrating Covey’s principles into daily life.
David is dedicated to fostering meaningful connections and intentional living, leveraging his diverse skill set to make a positive impact in the world. Whether through his technical expertise, creative artistry, or philosophical insights, he strives to empower others to live their best lives by focusing on what truly matters.
— David Youngblood