The Tool Desk
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Choose the model, quantization, context length, and runtime before you buy hardware. Memory capacity is the first gate: your system must hold model weights plus the context/KV cache, runtime buffers, and operating-system overhead. As a practical NVIDIA starting point, plan on 6–8 GB of VRAM for small 4B models, 12–16 GB for about 9B–12B models, and 24 GB or more for 27B-class models. Apple Silicon can run local models through unified memory, but the amount of memory determines what fits and memory bandwidth largely determines speed.
There is no universally best platform. A discrete NVIDIA GPU offers broad runtime support and upgradeability; an Apple Silicon Mac is quiet and simple but generally cannot be upgraded after purchase; a CPU-only workstation is inexpensive to configure but slower; and a multi-GPU system can fit very large models at the cost of power, cooling, and complexity. The right choice is the one that keeps your target model in local memory with enough headroom for the context and workload you actually use.
How much memory do open-weight AI models need?
Estimate memory from parameter count and numerical precision, then add room for context and software overhead. The weights are only the starting point: a model that exactly fills VRAM may load unreliably, lose context capacity, or fall back to slower system-memory offload.
Use a precision-based estimate
Hugging Face’s current Transformers guidance gives a useful rule of thumb: loading weights in bfloat16 or float16 takes about 2 GB of VRAM per billion parameters. Float32 takes roughly 4 GB per billion parameters. Thus, a 70-billion-parameter model needs approximately 140 GB just for bfloat16/float16 weights, before cache and runtime memory.
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| Weight format | Planning estimate | What it means |
|---|---|---|
| Float32 | About 4 GB per billion parameters | Highest memory use; uncommon for ordinary local inference |
| Bfloat16/float16 | About 2 GB per billion parameters | Higher quality and speed when memory allows |
| 4-bit quantized | About 0.55–0.6 GB per billion parameters on Apple Silicon, plus overhead | Much lower memory use, with some quality loss depending on the quantizer and task |
Quantization stores weights with fewer bits. It can make a model practical on consumer hardware, but the smallest file is not automatically the best choice. Aggressive quantization can reduce accuracy, coding reliability, or instruction following. Compare quantized variants using the same prompts and choose the lowest precision that still meets your quality needs.
Budget for context and runtime headroom
Long prompts and large context windows consume memory through the key-value (KV) cache. Runtime buffers, tokenizer state, temporary tensors, and the operating system also need space. Leave a safety margin rather than sizing a card to the weight file alone. NVIDIA’s guidance is to use the most powerful model that fits comfortably in GPU memory, not the largest model that barely loads.
What VRAM tier fits common local models?
These bands are practical starting points for a single NVIDIA GPU. They are not hard limits: the exact quantization, context length, runtime, and operating system change the result.
| GPU memory | Examples from NVIDIA’s local-LLM guidance | Typical use | Planning note |
|---|---|---|---|
| 6–8 GB | Qwen 3.5 4B | Small chat, summarization, lightweight coding | Keep context moderate and reserve room for runtime overhead |
| 12–16 GB | Qwen 3.5 9B or Gemma 4 12B | General assistants and coding tools | A sensible middle tier for one-user local inference |
| 24 GB or more | Qwen 3.6 27B | Larger reasoning and coding models, longer contexts | More room for quantization choices and cache growth |
Treat the table as a capacity guide, not a promise that every listed model will fit at every context length. Check the model’s actual quantized file size and the runtime’s memory report before deployment.
How do I calculate the hardware for a specific model?
Write down the model, precision, context target, and number of simultaneous users before comparing components. Then calculate a conservative memory budget.
- Choose the model family and parameter count. Record the exact release, because two models with similar names can have different architectures and memory behavior.
- Select a precision. Start with bfloat16/float16 when quality is paramount; evaluate 8-bit or 4-bit variants when the full-precision weights exceed available memory.
- Estimate weight memory. Multiply parameters in billions by about 2 GB for bfloat16/float16, or use the quantized file’s stated size. For Apple Silicon 4-bit planning, MLX suggests a little over 0.5 GB per billion parameters.
- Add context and cache room. A 4,000-token conversation and a 32,000-token document workload have very different cache requirements. Leave additional capacity for the runtime and operating system.
- Check concurrency. Each active request can require its own KV-cache allocation. A card that is comfortable for one user may be inadequate for several simultaneous sessions.
- Validate with the target runtime. Load the exact model and context setting, observe peak VRAM or unified-memory use, and test a long prompt before committing to a production configuration.
For example, a 12B model in bfloat16 has a rough 24 GB weight budget before cache, so it is not a realistic fit on a 16 GB card in that format. A 4-bit build may fit, but you still need to verify the file size, context limit, and runtime overhead.
Should I buy an NVIDIA GPU, an Apple Silicon Mac, or a CPU system?
Choose the platform based on memory capacity, software support, noise, power, and upgrade plans—not on peak theoretical performance alone.
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NVIDIA GPU PC
A discrete NVIDIA card is the safest general-purpose choice for local inference because major runtimes support CUDA and a wide range of model formats. A 12–16 GB card covers many 4B–12B workloads, while 24 GB or more makes larger quantized models and longer contexts practical. A desktop also lets you replace the GPU, add system RAM, and improve cooling later.
- Strengths: broad software compatibility, dedicated high-bandwidth memory, many card choices, and straightforward upgrades.
- Limitations: higher purchase price at large capacities, substantial power draw, fan noise, and the possibility that a fast card has too little VRAM for your target model.
- Best fit: coding assistants, experimentation with multiple runtimes, and users who want to upgrade components over time.
Apple Silicon Mac
Apple Silicon uses unified memory shared by the CPU and GPU. The MLX guide, updated 19 October 2026, states that any Apple Silicon Mac can run a local LLM; in practice, memory capacity determines which models fit and memory bandwidth determines how quickly they generate. Buying more unified memory later is generally not possible, so select capacity at purchase.
- Strengths: quiet integrated hardware, low configuration friction, and one memory pool for CPU and GPU work.
- Limitations: fixed memory capacity, bandwidth differences between chip and configuration, and less freedom to replace the graphics processor.
- Best fit: portable or quiet single-user inference, especially when a 24–64 GB or larger unified-memory configuration meets the model plan.
CPU-only workstation
A CPU system can run any model that fits in ordinary RAM, but generation and prompt processing are generally slower than on a suitable GPU. It can be a sensible entry point for small models, testing, scheduled batch jobs, or applications where latency is unimportant. Intel’s OpenVINO material notes that model files can range from about 2 GB to more than 300 GB and warns that insufficient RAM can cause heavy swapping or crashes.
Use fast, adequate system RAM and an SSD. CPU/GPU offload can let a model exceed dedicated VRAM, but the transfer path usually reduces performance compared with keeping the active layers in local GPU memory.
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Multi-GPU workstation
Multiple GPUs combine memory capacity for models that do not fit on one card. They also add cost, power, cooling, PCIe-lane and motherboard constraints, and topology-dependent software configuration. This approach is most appropriate when a large model is essential and you are comfortable measuring inter-GPU communication and tuning the runtime.
| Option | Strength | Limitation | Best fit |
|---|---|---|---|
| NVIDIA PC, 12–16 GB VRAM | Broad support and useful mid-size capacity | Larger models require more VRAM or offload | 4B–12B models and coding assistants |
| NVIDIA PC, 24 GB+ VRAM | Fits larger quantized models and longer contexts | Higher cost, power, and cooling needs | 20B–30B-class local models |
| Apple Silicon, 24–64 GB+ unified memory | Quiet integrated system | Memory is usually not upgradeable; bandwidth varies | Portable or quiet local inference |
| CPU-only workstation | Simple hardware using ordinary RAM | Slower generation and prompt processing | Small models, testing, and batch jobs |
| Multi-GPU workstation | Combines memory for very large models | Complexity, power, cooling, and topology issues | Large models and advanced users |
How much system RAM and storage should I plan?
System RAM must cover the operating system, runtime, model-loading process, and any layers placed outside the GPU. If you intend to keep several models available, size RAM for the largest model plus the other applications you run rather than adding only the active GPU allocation.
Storage needs grow quickly. Intel reports model files ranging from about 2 GB to over 300 GB. Use an NVMe SSD with enough free space for the model, a second copy during downloads or conversion, tokenizer files, caches, and future versions. Keep several tens of percent of the drive free to avoid a cramped system and to leave room for temporary files.
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- Check whether the runtime loads a complete file into RAM before transferring layers.
- Expect first-run downloads and conversion steps to require extra temporary storage.
- Use a reliable backup or re-download plan for models you cannot easily replace.
- Do not confuse storage capacity with memory capacity: a model can fit on disk and still fail to load.
Which runtimes and drivers must the hardware support?
Hardware selection is incomplete until the operating system, driver, and inference runtime are confirmed. Ollama documents NVIDIA support with required compute capabilities and drivers, Apple GPU acceleration through Metal, and Vulkan paths for additional Windows and Linux GPUs.
- Identify the exact GPU or Apple chip and its memory capacity.
- Check the runtime’s supported operating systems, driver branch, compute capability, and backend.
- Install the current supported driver rather than assuming a generic display driver will work.
- Run a small known model first, then test the target model with your intended context length.
- Confirm that the runtime is actually using the accelerator; a model can appear to run while silently falling back to the CPU.
Support can change as runtimes add backends or drop old drivers. Verify compatibility for the specific versions you will deploy instead of relying on a general statement that a GPU family is supported.
What performance factors matter beyond VRAM?
Capacity determines whether a model fits; bandwidth and the rest of the system determine how usable it feels. Compare the following measurements for your workload:
- Generation throughput: tokens per second while producing an answer.
- Prompt-processing speed: how quickly a long document is ingested before generation begins.
- First-token latency: delay before the response starts, important for interactive tools.
- Concurrency: how many active requests can run without exhausting cache or causing unacceptable latency.
- Memory bandwidth: often a major determinant of inference speed, especially on unified-memory systems.
- Thermal behavior: sustained performance after the system warms up, not just a short benchmark burst.
- Noise and power: relevant for an office, bedroom, or always-on server.
- Upgradeability and total cost: include the power supply, cooling, case space, RAM, SSD, and electricity.
A faster GPU with insufficient VRAM may be less useful than a slower card that keeps the target model entirely local. Offloading can increase capacity, but its speed depends on the transfer path and how much data moves between memory pools.
Can a consumer GPU run a 70B model?
Not in bfloat16 or float16 on a single ordinary consumer card: the weights alone are approximately 140 GB for a 70B model. A heavily quantized version can reduce the requirement substantially, but it still needs room for context, runtime buffers, and the operating system, so a 24 GB card will not hold a typical 70B model entirely in VRAM.
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How should I compare a shortlist before buying?
Use a written workload specification and reject any configuration that fails a hard requirement.
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- Define the workload. Classify it as occasional chat, coding, long-document analysis, tool-using agents, or a multi-user service.
- Set the model target. Record parameter count, quantization, context window, and expected simultaneous users.
- Set a memory floor. Include weights, cache, runtime headroom, and operating-system memory.
- Choose a platform. Decide whether software compatibility, portability, upgradeability, silence, or maximum capacity is the priority.
- Check the complete build. Verify power-supply capacity, connectors, case dimensions, cooling, RAM slots, storage, and motherboard lanes.
- Test before scaling. Measure load time, first-token latency, sustained generation, and peak memory with the real model and prompt lengths.
- Price the total system. Include RAM, SSD, cooling, electricity, and any additional GPU rather than comparing graphics-card prices alone.
What common hardware-selection mistakes should I avoid?
Buying by model name alone
Parameter count does not reveal precision, architecture, context cost, or runtime requirements. Always check the exact weight format and file size.
Filling VRAM to the last megabyte
Leave room for cache and temporary allocations. A model that loads only with a short context may fail when given a real document.
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Large downloads, conversion tools, CPU offload, and the operating system can exhaust RAM even when the GPU has capacity. Swapping can make an otherwise workable setup painfully slow.
Assuming all backends behave alike
CUDA, Metal, Vulkan, and CPU paths have different support levels and performance characteristics. Confirm the backend actually selected by your runtime.
Optimizing for peak benchmark speed
Short tests can hide thermal throttling, long-prompt latency, or multi-user contention. Test the duration and concurrency your application will sustain.
Forgetting physical and electrical limits
High-end GPUs may need specific power connectors, a larger case, and stronger cooling. Multi-GPU builds also need suitable PCIe spacing and airflow.
Which hardware profile fits common workloads?
Occasional chat and summarization
A 6–8 GB GPU or an Apple Silicon system with enough unified memory can handle small quantized models. Prioritize low noise, fast startup, and adequate SSD space over maximum throughput.
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A 12–16 GB NVIDIA GPU is a practical middle tier for roughly 9B–12B models. Favor a runtime with dependable GPU acceleration and enough context for repositories or multi-file prompts.
Long-document analysis
Plan extra memory for the KV cache and prompt processing. A 24 GB GPU or a larger unified-memory configuration gives more flexibility than a card sized only for the weight file.
Tool-using agents
Agents may keep longer histories, invoke several tools, and run concurrent sub-tasks. Account for multiple active caches and choose a platform with both memory headroom and stable driver support.
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Measure concurrent throughput rather than single-user tokens per second. More VRAM, multiple GPUs, or a larger unified-memory system may be justified, but test the runtime’s scheduling and interconnect behavior before deployment.
What is the practical buying rule?
Start with the largest model you genuinely need, select the least aggressive quantization that meets your quality target, and buy enough memory to keep that model plus its intended context comfortably local. Then verify runtime and driver support, measure sustained performance, and account for noise, power, upgradeability, and total system cost. This process is more reliable than choosing a graphics card first and trying to force a model into it later.
Frequently Asked Questions
Can I add more VRAM to a graphics card later?
No. A GPU’s VRAM is fixed, so a memory shortage generally requires replacing the card or distributing the model across additional GPUs. System RAM and storage can often be expanded, but those upgrades do not turn a small-VRAM card into a large one.
Does a larger context window always improve local-model results?
No. A larger window allows more text, but it consumes KV-cache memory and can increase prompt-processing time. Use the shortest context that reliably contains the information your application needs.
Is memory bandwidth important on an Apple Silicon Mac?
Yes. Unified memory is shared by the CPU and GPU, and the MLX guidance identifies bandwidth as a major speed factor after capacity. Two Macs with the same memory amount can therefore deliver different inference performance.
What should I test after installing a new local-AI system?
Load the exact quantized model, send a short prompt, then test your maximum document length and expected concurrent sessions. Record peak memory, first-token latency, sustained tokens per second, and whether the runtime uses the intended accelerator backend.
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