Local LLM Deployment: Intelligence, Hardware, Speed & Cost

Prompt

I need a technical comparison of current open-weight LLMs for a fully local, on-premise AI deployment. The goal is to understand the trade-off between: MODEL INTELLIGENCE × LOCAL INFERENCE SPEED × MEMORY REQUIREMENTS × CONCURRENCY × HARDWARE COST The target deployment is a private local AI platform intended primarily for interactive professional use. Typical usage: - 1 primary active user - Potentially up to 10 users with light or intermittent concurrency - Data must remain completely local - No external inference APIs should be required - Q4 or an equivalent high-quality 4-bit quantization should be considered the default deployment target where technically appropriate Analyze the following models: - Mistral Small 4 - Qwen3.8 27B - Qwen3.8-Flash-Next - GLM-5.3-Flash - DeepSeek V4.1 Flash First verify whether these are the correct and current model names. If a model name is outdated, deprecated, incorrect, or has been superseded, clearly state this and use the closest current open-weight equivalent. Do not silently substitute models. --- ## 1. MODEL COMPARISON Create a table containing: 1. Model 2. Artificial Analysis Intelligence Index, if available 3. Total parameter count 4. Active parameters per token, if applicable 5. Architecture: - Dense - Mixture-of-Experts - Hybrid - Other 6. Native numerical precision 7. Commonly available local quantizations 8. Approximate Q4 / 4-bit model size 9. Minimum practical VRAM or unified memory 10. Recommended memory capacity 11. Maximum or typical supported context length 12. Suitability for: - 1 interactive user - 2–5 concurrent users - up to 10 light concurrent users 13. Main strengths 14. Main limitations Clearly distinguish: - model weights size - runtime memory requirement - KV-cache memory - additional runtime overhead Do not assume that a model that technically fits into memory will necessarily provide a good interactive experience. --- # 2. LOCAL INFERENCE PERFORMANCE Estimate realistic local generation performance for each model. Focus primarily on: TOKENS PER SECOND DURING AUTOREGRESSIVE GENERATION / DECODE for one active user. Do not report hosted API throughput as if it represented local performance. Use evidence in this order: 1. Real benchmark using the exact model and exact hardware 2. Real benchmark using the exact model on comparable hardware 3. Benchmark using a closely related model/hardware configuration 4. Engineering estimate when no measured data is available For every performance figure, state: - Model - Hardware - Number of GPUs or systems - Quantization / precision - Runtime: - llama.cpp - vLLM - SGLang - TensorRT-LLM - MLX - or other - Context length if known - Batch size if known - Whether the value represents: - prefill - decode / generation - combined throughput - Whether the result is: - MEASURED - DERIVED - ESTIMATED Do not present an estimate as a measured benchmark. --- # 3. HARDWARE SCENARIO A — SINGLE NVIDIA DGX SPARK Evaluate each model on: NVIDIA DGX Spark - GB10 Grace Blackwell Superchip - 128 GB unified memory - Single system For every model determine: - Does the Q4 model fit? - Does it fit comfortably or only technically? - Approximate memory headroom - Expected single-user tokens/s - Likely time-to-first-token characteristics - Maximum realistic context before memory becomes problematic - Whether CPU/unified-memory bandwidth is likely to become a bottleneck - Suitability for interactive use - Suitability for several concurrent users Classify each model as: - Excellent fit - Good fit - Usable with compromises - Technically possible but impractical - Does not fit Explain the classification. --- # 4. HARDWARE SCENARIO B — MULTIPLE DGX SPARK SYSTEMS For models that do not perform well on one DGX Spark, evaluate: - 2× DGX Spark - 4× DGX Spark Consider: - model sharding - tensor parallelism - pipeline parallelism where applicable - inter-system communication overhead - memory capacity - memory bandwidth - scaling efficiency - inference framework compatibility Estimate: - total available memory - approximate usable model capacity - expected single-user generation speed - performance scaling compared with one Spark - suitability for concurrent users - hardware acquisition cost Do not assume that doubling the number of systems doubles inference speed. --- # 5. HARDWARE SCENARIO C — HIGH-PERFORMANCE GPU WORKSTATION Evaluate a professional workstation based around: NVIDIA RTX PRO 6000 Blackwell - 96 GB VRAM Consider configurations such as: - 1× RTX PRO 6000 - 2× RTX PRO 6000 if justified Include: - required system RAM - CPU requirements - PSU requirements - PCIe considerations - possible CPU/RAM offloading - expected generation speed - memory limitations - context limitations - concurrency capability If partial CPU offloading is needed, explicitly explain the likely impact on tokens/s. Compare this scenario directly against DGX Spark. --- # 6. ALTERNATIVE PRICE/PERFORMANCE HARDWARE If another local hardware configuration provides substantially better price/performance, include it. Possible examples may include: - RTX 5090 32 GB - dual consumer GPU systems - other NVIDIA professional GPUs - AMD accelerators where software support is realistic - Apple Silicon systems if genuinely competitive for the workload - dedicated inference appliances Only include alternatives that are technically credible for the analyzed models. For each alternative state: - hardware configuration - total accelerator memory - estimated system cost - models it can realistically run - expected single-user performance - major limitations --- # 7. CONCURRENCY The main benchmark should remain single-user interactive performance. However, also estimate how each recommended hardware configuration would behave with: - 1 active user - 2 simultaneous users - 5 simultaneous users - 10 simultaneous users Do not simply divide single-user tokens/s by the number of users. Consider continuous batching and inference-server behavior where applicable. Distinguish between: - aggregate throughput - per-user generation speed - latency - time to first token The objective is to determine when a system stops providing a comfortable interactive experience. --- # 8. HARDWARE COST Estimate current hardware purchase cost in EUR. Prefer Spain or EU pricing where possible. Use realistic price ranges rather than false precision. Separate: - accelerator / GPU cost - rest of system cost - complete estimated system price For DGX Spark provide: - cost per system - 1× configuration - 2× configuration - 4× configuration For workstation configurations provide: - GPU cost - CPU - RAM - motherboard - storage - PSU / cooling - approximate complete workstation cost Do not include cloud/API inference costs. --- # 9. SIMPLIFIED COMPARISON Create this table: | Model | Intelligence Index | Architecture | Q4 Size | Practical Memory Requirement | Recommended Hardware | Single-User Speed | 1–10 User Suitability | Approx. Hardware Cost | Keep this table concise. --- # 10. DEPLOYMENT SCENARIOS Based on the analysis, define three generic deployment scenarios. ## SCENARIO 1 — COMPACT LOCAL SYSTEM Objective: Lowest reasonable acquisition cost while still providing a capable modern local LLM. Prioritize: - price/performance - low power consumption - simple deployment - acceptable interactive speed State: - recommended model(s) - hardware - expected tokens/s - memory headroom - approximate cost - limitations --- ## SCENARIO 2 — BALANCED LOCAL SYSTEM Objective: Higher model capability while maintaining practical local inference performance. Prioritize: - intelligence - usable interactive speed - memory capacity - support for several users - reasonable hardware cost State: - recommended model(s) - hardware - expected tokens/s - expected concurrency - approximate cost - limitations --- ## SCENARIO 3 — HIGH-PERFORMANCE LOCAL SYSTEM Objective: Provide the best interactive experience for demanding professional use. Prioritize: - high generation speed - low latency - stronger models - larger context - ability to serve several users Acquisition cost is secondary to performance, but should still be justified. State: - recommended model(s) - hardware - expected tokens/s - expected concurrency - approximate cost - limitations --- # 11. INTELLIGENCE VS HARDWARE CURVE Explain the relationship between: MODEL INTELLIGENCE vs REQUIRED MEMORY vs LOCAL TOKENS/SECOND vs HARDWARE COST Identify cases where: - a larger model provides little additional capability for significantly more hardware - an MoE model provides unusually good intelligence per active parameter - a smaller dense model provides better interactive performance - hardware bandwidth becomes more important than compute - adding more hardware has diminishing returns Do not assume the highest Intelligence Index model is automatically the best deployment choice. --- # 12. FINAL SUMMARY Finish with a decision-oriented summary answering: 1. What level of model capability can realistically be achieved with one DGX Spark? 2. What models justify moving from one to two DGX Sparks? 3. Are there models where four DGX Sparks are technically possible but economically questionable? 4. When does an RTX PRO 6000 workstation provide a better experience than DGX Spark? 5. What configuration offers the strongest price/performance for one user? 6. What configuration makes the most sense for up to 10 light users? 7. Where is the practical point of diminishing returns between intelligence, speed and hardware cost? The final objective is to answer: “If an organization wants to run a modern AI model completely locally, what level of model intelligence can it obtain at each hardware tier, how fast will the system actually feel, how many users can it reasonably support, and how much hardware will it require?” --- # EVIDENCE RULES For every important factual claim, clearly identify the source or basis. Prefer: 1. Official model documentation 2. Artificial Analysis 3. Official NVIDIA specifications 4. Hugging Face model repositories 5. Reproducible GitHub benchmark projects 6. Published inference framework benchmarks Clearly label unsupported calculations as estimates. Never use hosted API tokens/s as a proxy for local inference speed without explicitly stating that they are different measurements. Do not invent benchmark results. If reliable information is unavailable, state: “No reliable measured benchmark found” and provide a clearly labelled engineering estimate only if enough technical information exists to make one.