The Tool Desk
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Choose the model and document workflow first, then size the computer around them. For trying small models or retrieval, an existing computer may be enough; larger models, longer contexts, or multiple simultaneous users can call for a dedicated GPU system. The practical limits are not just model size: account for GPU memory or unified memory, system RAM, context, concurrency, bandwidth, software support, and the cost and complexity of the whole setup.
Start with the work, not the workstation
Write down what the system must do before comparing computers. A single-user assistant for short questions has different needs from batch analysis of case files or a shared service handling several requests at once.
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- Choose the model and its quantization. Check the actual download size and the precision or quantization you intend to run. Parameter count alone does not tell you the full runtime memory requirement.
- Describe the document workflow. Will the model receive a whole long document, or will retrieval select relevant passages? The cited hardware guidance establishes no universal context-window minimum for legal work.
- Specify context and concurrency. Estimate the length of prompts and documents, and the number of simultaneous users or jobs. Both context and parallel requests add memory pressure.
- Set a speed expectation. Decide whether slow interactive responses are acceptable or whether the task needs batch throughput. Performance comparisons only mean much when model, quantization, context, backend, and hardware are all specified.
Then test the selected model against representative, approved materials on the actual workflow. This is more useful than buying to a parameter-count rule or a generic “lawyer PC” specification.
How much hardware do the available tiers imply?
The CCBE’s Technical guide on the use of AI tools and models by lawyers, Edition 2026, gives examples across several spending levels. These are illustrative feasibility examples, not independent benchmarks, guaranteed performance, or recommendations for legal quality.
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| Use case or example | Hardware or cost stated | How to interpret it |
|---|---|---|
| Small conversational models or embedding/retrieval experiments | The CCBE guide says an existing Windows computer with as little as 8GB of RAM can support examples of this kind. | A starting point for experimentation, not proof that every model or document workflow will fit or be useful. |
| DeepSeek-R1:14B example | The CCBE guide gives 16GB RAM and about 2.5 tokens per second on its referenced machine. | A guide-specific example; it is not a general speed benchmark or a promise for another computer. |
| Dedicated inference workstation | About €2,000 excluding VAT, using September 2025 component prices, for an example including a 128GB-RAM motherboard and a GPU with 24GB VRAM. | The guide associates this configuration with 20–40B parameter text-only models at a “comfortable speed.” Treat the price as a dated reference, not a current quote. |
| Higher-end GPU example | About €8,000 for an RTX Pro 6000 with 96GB VRAM, as stated in the CCBE guide. | A time- and market-sensitive example, not a default purchase for an individual experimenting with local AI. |
| Higher-end workstation tier | About €20,000 in the CCBE guide’s illustrative estimate. | Presented in the context of larger open-weight models or concurrent use; not an entry-level requirement. |
For a separate set of model-fit examples, NVIDIA’s local LLM guide gives RTX GPU starting tiers of 6–8GB for Qwen 3.5 4B, 12–16GB for Qwen 3.5 9B or Gemma 4 12B, and 24GB or more for Qwen 3.6 27B. These are vendor examples for its RTX audience, not independently tested recommendations for legal tasks. NVIDIA also notes that quantization can reduce memory use, while aggressive quantization can reduce response quality.
These figures are useful for framing options, but they do not replace checking the exact model, context, runtime, and workload. A system that can load a model may still be too slow or too constrained for the work you expect.
Size memory for the model, context, and users
GPU memory and unified memory
For a GPU-based setup, VRAM often sets the practical model tier: more VRAM can allow a larger model or context to fit. But “fits” is only the first test. Leave room for runtime overhead and the context/KV cache, and check what memory pool and inference backend the chosen software actually uses. Apple Silicon systems use unified memory; its usefulness depends on the runtime and workload, so do not assume it is interchangeable with dedicated GPU VRAM in every setup.
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System RAM
System RAM serves the operating system, runtime, and other applications, and may also be part of the memory available to a given setup. Do not use a software’s baseline requirement as a guarantee that a particular model and long-document workload will fit. LM Studio, for example, recommends 16GB or more for Apple Silicon Macs; it says 8GB Macs may work with smaller models and modest context. For Windows, it recommends at least 16GB RAM and at least 4GB of dedicated VRAM.
Context length and concurrency
Longer prompts and contexts consume additional memory. So do parallel requests. Ollama documents that RAM needs scale with OLLAMA_NUM_PARALLEL multiplied by OLLAMA_CONTEXT_LENGTH; concurrent GPU inference also depends on available VRAM. A machine sized for one short chat may queue requests or fail to fit when several jobs run at once.
For a document-heavy workflow, benchmark the real process: representative files, the intended context, the retrieval setup if any, and the expected number of simultaneous requests. The cited sources do not establish one context target that suits all legal practice.
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Check software and hardware compatibility before buying
Requirements vary by operating system and runtime, and the documentation changes. Confirm support for the exact computer, OS version, GPU generation, driver, and inference backend before committing to a build.
- LM Studio: Its requirements page lists Apple Silicon M1, M2, M3, and M4 with macOS 14 or newer, and says Intel Macs are currently unsupported. For Windows x64, it requires AVX2, in addition to its stated memory recommendations.
- Ollama: It documents Apple GPU acceleration through Metal and separate support paths for other GPU vendors and platforms. Verify the exact combination rather than assuming a non-NVIDIA card will behave like a supported NVIDIA setup.
- Multiple GPUs: The CCBE guide warns that many consumer motherboards cannot practically provide full bandwidth to several GPUs; it says most consumer motherboards can only house one full-speed GPU. Check the specific board’s lane allocation, power delivery, cooling, and software support rather than treating multi-GPU operation as a simple upgrade.
Living documentation can change. Check the current LM Studio system requirements, Ollama’s Apple GPU acceleration documentation, and NVIDIA’s local LLM guide when selecting components.
Compare complete systems, not just VRAM
Once a model and workflow are defined, compare candidate systems against the same checklist. A high VRAM figure cannot compensate for an unsupported backend, inadequate system memory, or a poor fit for the expected workload.
- Model fit: Does the selected model and quantization fit in the memory pool the runtime will use, with space for context and overhead?
- Context and concurrency: Can it handle the intended document lengths and number of simultaneous users without swapping, failing, or unacceptable queuing?
- Speed: Compare measurements only for the same model, quantization, context, backend, and task. A speed figure from another configuration may not predict yours.
- Memory architecture and bandwidth: Consider dedicated VRAM, system RAM, or unified memory as appropriate, plus bandwidth once the model fits.
- Compatibility: Verify OS, drivers, CPU instruction requirements, GPU generation, and the selected runtime’s backend.
- Physical and operating constraints: Account for power draw, cooling, noise, available space, and any multi-GPU motherboard limits.
- Total cost: Include the machine, GPU, RAM, storage, power, setup effort, and a realistic upgrade path. The CCBE’s workstation prices use September 2025 component prices, so they should not be treated as current retail quotes.
Local execution is not the same as a secured legal workflow
Local inference can keep prompts and files on the machine, but privacy depends on the actual configuration and the rest of the workflow. NVIDIA describes local LLM workflows as keeping prompts, files, and local context on-device. Ollama says locally processed prompts and data are not visible to it, and documents a local-only mode that disables cloud features. Those statements apply to the configurations described by each vendor, not every installation or deployment.
Ollama binds to loopback by default, but its documentation explains that changing the host setting can expose the service on a network. Review local-only settings, network exposure, logs, backups, cloud fallbacks, and firm policy before using confidential material.
Hardware capacity is not evidence that a model’s answers are accurate, complete, privileged, or safe to file. The cited sources do not provide a standardized independent benchmark establishing legal reliability for any hardware tier. Evaluate the chosen model using representative, approved materials, require appropriate human review, and apply existing confidentiality and professional-responsibility controls.
Quick Recap
Sources and scope
- CCBE, Technical guide on the use of AI tools and models by lawyers, Edition 2026. Its cost examples use September 2025 prices.
- LM Studio system requirements. Software requirements and recommendations, not a guarantee that every model or context will fit.
- Ollama FAQ. Documentation on memory scaling and runtime behavior.
- Ollama GPU support. Check the current platform and vendor support path for the intended machine.
- Ollama network exposure documentation. Explains the host setting relevant to network access.
- NVIDIA local LLM guide. Vendor model-fit examples for RTX hardware.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
