“Can I run AI models locally?” is the first question everyone asks—and the answer is always “it depends on your memory.” Local AI is booming, but confusion between RAM, VRAM, and unified memory leads to expensive mistakes.
In this guide, you’ll get exact memory needs for popular models, how quantization changes the math, and a look-up table so you can match hardware to the models you want. If you’re still choosing hardware, our best mini PCs for AI buyer’s guide ranks options by tier; for mini PCs for virtualization (same form factor, different use case), we have a separate pillar.
Quick reference: 7B Q4 ≈ 4–5 GB; 14B Q4 ≈ 8 GB; 32B Q4 ≈ 18–20 GB; 70B Q4 ≈ 35–40 GB. Add 2–4 GB for overhead. Use the table below for exact numbers by model and quantization.
RAM vs VRAM — What’s the Difference for AI?
System RAM (DDR5 / LPDDR5X)
System RAM is general-purpose memory used by the CPU. Most current mini PCs ship with either DDR5 SO-DIMMs (socketed, upgradeable in some models) or soldered LPDDR5X (smaller, more power-efficient, but fixed at purchase). The practical difference matters: standard DDR5 delivers around 70–100 GB/s of bandwidth, while LPDDR5X—found in AMD Strix Halo machines and Apple Silicon Macs—pushes 200+ GB/s. That bandwidth gap translates directly into tokens per second during inference.
For AI workloads, system RAM is used whenever you run CPU-only inference (no discrete GPU), when the model is too large to fit entirely in VRAM and layers spill over from GPU to CPU, or when the runtime needs space for the KV cache during long conversations. Capacity sets your model-size ceiling, and bandwidth determines how fast you generate tokens once the model is loaded.
GPU VRAM (GDDR6X, etc.)
VRAM is dedicated video memory on your graphics card, and when the entire model fits inside it, inference is fastest—there’s no PCIe bus transfer bottleneck. Common VRAM tiers on desktop GPUs are 8 GB (GTX 1070/RTX 4060), 12 GB (RTX 4070), 16 GB (RTX 4080), 24 GB (RTX 4090), and 48 GB (A6000 / professional cards). GDDR6X delivers 500–1,000 GB/s of bandwidth depending on the card, which is 5–10× faster than system DDR5. That’s why a 12 GB GPU running a 7B model at Q4 produces 40–80 tokens per second, while the same model on system RAM manages 12–18 tok/s. Towards AI’s analysis of VRAM needs for local vs cloud LLMs breaks down the cost-performance trade-off in detail.
Unified Memory (Apple Silicon)
On Apple Silicon Macs (M1 through M4 series), CPU and GPU share a single unified memory pool. There’s no separate VRAM and no PCIe copy penalty—the GPU can access the same memory the CPU uses, which eliminates the biggest bottleneck of discrete GPU setups. A 64 GB Mac mini M4 Pro effectively has “64 GB for the model,” though not all of it is available: macOS and background services reserve 2–4 GB, so plan for roughly 60 GB of usable model space. The M4 Pro’s unified memory runs at 273 GB/s, which sits between DDR5 and GDDR6X—fast enough to deliver 18–22 tok/s on a 7B model and solid performance on 32B models. See our Mac mini for AI guide for how that plays out in practice.
How Model Size Translates to Memory
The Rule of Thumb
Roughly ~2 GB per 1 billion parameters at FP16 (full precision), ~1 GB at Q8, and ~0.5 GB at Q4_K_M. So a 7B model at Q4 needs about 4 GB of memory, and a 70B model at Q4 needs about 35–40 GB. Always add 2–4 GB on top for the OS, runtime, and overhead. ApX ML’s hardware RAM requirements guide walks through these calculations model-by-model.
Here’s a worked example: take Llama 3.1 70B. At FP16 that’s 70 × 2 = ~140 GB. At Q8_0 it’s 70 × 1 = ~70 GB. At Q4_K_M it’s 70 × 0.5 = ~35 GB. Add 3–4 GB for overhead, and you land at ~38–39 GB total. A 64 GB machine handles that comfortably with room left for the OS, KV cache, and a browser running in the background.
Context Length and KV Cache
Longer context windows consume additional memory at runtime through the KV (key-value) cache—this is memory used on top of the model weights themselves. A 4K context on a 7B model adds roughly 0.5 GB. Scale that up, and a 32K context on a 30B model can add 2–4 GB. Some newer models support 128K context, where KV cache alone can consume 8–16 GB for a large model. This is runtime memory that sits alongside the model weights, so if your 32B Q4 model occupies 18 GB and you’re running a 32K context, plan for 20–22 GB total before you even count OS and runtime overhead.
Overhead
Don’t assume “model size = all I need.” Your operating system takes 2–3 GB of RAM at idle. The Ollama runtime itself uses around 500 MB. If you’re running Open WebUI as a front-end, add another ~1 GB. A browser with a handful of tabs easily adds 1–2 GB more. On a 32 GB machine running an 18 GB Q4 model, that leaves roughly 10 GB for KV cache, the OS, and everything else—workable for short contexts, but tight if you’re having long conversations or running multiple tools alongside your model.
Quantization Explained — Trade Memory for (Minimal) Quality
What Quantization Does
Quantization reduces the numerical precision of model weights—from 16-bit floating point (FP16) down to 8-, 6-, 5-, or 4-bit integers. Think of it like compressing a high-resolution photo to JPEG: you lose some precision in the process, but for most practical uses, the difference is hard to spot. A 16-bit weight can represent 65,536 distinct values; a 4-bit weight represents just 16. Yet because large language models have billions of redundant parameters, this compression shrinks memory by 2–4× with usually small quality loss for everyday tasks like chat, coding, and summarization.
Common Levels
The GGUF format (used by Ollama and llama.cpp) offers several quantization levels. Here’s how they compare per 1 billion parameters:
| Level | Bits | Memory per 1B params | Quality impact |
|---|---|---|---|
| FP16 | 16 | ~2 GB | Baseline (full precision) |
| Q8_0 | 8 | ~1 GB | Negligible loss |
| Q6_K | 6 | ~0.75 GB | Very minor loss |
| Q5_K_M | 5 | ~0.63 GB | Minor loss |
| Q4_K_M | 4 | ~0.5 GB | Slightly noticeable for complex reasoning |
Lower precision means less RAM or VRAM required and slightly lower quality. For most users, Q4_K_M represents the best trade-off between size and capability.
When Q4_K_M Is Good Enough
For coding assistants, casual chat, creative writing, summarization, and RAG pipelines, Q4_K_M is the community consensus sweet spot. Benchmarks consistently show that the perplexity difference between Q4_K_M and FP16 is small enough that humans can’t reliably distinguish the outputs in blind tests. Reserve Q5_K_M or higher for specialized domains where precision genuinely matters—medical text analysis, legal document review, or academic research where subtle reasoning differences could compound. For everything else, Q4_K_M lets you run a larger model in the same memory, and a bigger model at Q4 almost always outperforms a smaller model at Q8.
The Look-Up Table — Memory Needed by Model and Quantization
The values below are rounded from real-world measurements. LocalLLM.in’s Ollama VRAM requirements guide has additional per-model benchmarks; Micro Center’s 8 GB / 16 GB / 32 GB memory guide groups models by available RAM.
| Model | Params | FP16 (GB) | Q8_0 (GB) | Q4_K_M (GB) | Min hardware tier |
|---|---|---|---|---|---|
| Phi-4 | 3.8B | ~8 | ~4 | ~2 | 8 GB |
| Llama 3.2 | 8B | ~16 | ~8 | ~4–5 | 16 GB |
| DeepSeek R1 | 14B | ~28 | ~14 | ~8 | 24–32 GB |
| Qwen 2.5 | 32B | ~64 | ~32 | ~18–20 | 32–64 GB |
| Llama 3.1 | 70B | ~140 | ~70 | ~35–40 | 64–128 GB |
| Qwen 3 | 235B | — | — | ~120+ | 128 GB+ |
Memory Bandwidth — The Hidden Performance Factor
Two machines can have the same amount of RAM and run the same model, yet produce tokens at wildly different speeds. The difference is memory bandwidth—how fast data moves from memory to the processor. During the token generation (decoding) phase, the entire model must be read from memory for every single token produced, making inference heavily bandwidth-bound. Here’s how common memory types compare:
| Memory type | Typical bandwidth | ~tok/s on 7B Q4 |
|---|---|---|
| DDR4 (older NUCs) | 50–70 GB/s | 5–10 tok/s |
| DDR5 (most new mini PCs) | 100–140 GB/s | 12–18 tok/s |
| LPDDR5X (AMD Strix Halo) | 200+ GB/s | 20–30 tok/s |
| Apple M4 Pro unified | 273 GB/s | 18–22 tok/s |
| GDDR6X (discrete GPU) | 500–1,000 GB/s | 40–80 tok/s |
One nuance worth understanding: bandwidth matters most during the generation (decoding) phase, where the model produces one token at a time and must read through all the weights each step. The initial prompt processing (prefill) phase is more compute-bound—it processes all input tokens in parallel and benefits more from raw FLOPS than from memory throughput. This is why a discrete GPU with high GDDR6X bandwidth dominates token generation speed, while the prompt evaluation gap between GPU and high-bandwidth system memory is often smaller than you’d expect.
When comparing mini PCs, don’t look at capacity alone. A 64 GB DDR5 machine at 100 GB/s will generate tokens noticeably slower than a 64 GB LPDDR5X machine at 200+ GB/s, even though both can load the same models.
Compare GPU and Memory Options
NVidia GPUs
- AI Performance: 623 AI TOPS
- OC mode: 2565 MHz (OC mode)/ 2535 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- SFF-Ready Enthusiast GeForce Card
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- NVIDIA Ampere Streaming Multiprocessors: The all-new Ampere SM brings 2X the FP32 throughput and improved power efficiency.
- 2nd Generation RT Cores: Experience 2X the throughput of 1st gen RT Cores, plus concurrent RT and shading for a whole new level of ray-tracing performance.
- 3rd Generation Tensor Cores: Get up to 2X the throughput with structural sparsity and advanced AI algorithms such as DLSS. These cores deliver a massive boost in game performance and all-new AI capabilities.
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure.
- A 2-slot Design maximizes compatibility and cooling efficiency for superior performance in small chassis.
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.125-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- SFF-Ready enthusiast GeForce card compatible with small-form-factor builds
- Axial-tech fans feature a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- Phase-change GPU thermal pad helps ensure optimal heat transfer, lowering GPU temperatures for enhanced performance and reliability
- 2.5-slot design allows for greater build compatibility while maintaining cooling performance
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- SFF-Ready enthusiast GeForce card compatible with small-form-factor builds
- Axial-tech fans feature a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- Phase-change GPU thermal pad helps ensure optimal heat transfer, lowering GPU temperatures for enhanced performance and reliability
- 2.5-slot design allows for greater build compatibility while maintaining cooling performance
AMD GPUs
- Axial-tech fans now feature a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- Phase-change GPU thermal pad helps ensure optimal heat transfer, lowering GPU temperatures for enhanced performance and reliability
- 2.5-slot design allows for greater build compatibility while maintaining cooling performance
- Dual-ball fan bearings last up to twice as long as standard conventional sleeve bearings designs
- 0dB technology lets you enjoy light gaming in relative silence
- High-Performance GPU: AMD Radeon RX 7700 XT with AMD RDNA 3 architecture, featuring 54 Compute Units and RT+AI Accelerators for exceptional gaming and content creation performance
- Blazing Fast Clock Speeds: Boost Clock up to 2584 MHz and Game Clock of 2226 MHz, ensuring smooth and responsive gameplay in demanding titles
- Ample Memory Configuration: 12GB GDDR6 memory on a 192-bit bus, paired with 48MB AMD Infinity Cache for reduced latency and enhanced performance at high resolutions
- Advanced Cooling Solution: Dual Fan Design with Striped Ring Fans and Ultra-fit Heatpipe technology, ensuring efficient thermal management and sustained performance during extended gaming sessions
- 0dB Silent Cooling: Fans remain off at low temperatures, providing silent operation during light workloads or idle states
- Chipset: AMD RX 7900 XT
- Memory: 20GB GDDR6
- AMD Triple Fan Cooling Solution
- Boost Clock: Up to 2400 MHz
- System Compatibility Note: 2.9-slot design with dimensions 298 x 131 x 58 mm; requires two 8-pin PCIe power connectors and a recommended 800W power supply. Please verify available space and PSU compatibility before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Flagship RDNA 4 Performance: Powered by the AMD Radeon RX 9070 XT GPU with 64 Compute Units featuring 3rd Gen Ray Tracing and 2nd Gen AI Accelerators, delivering uncompromising 4K gaming and AI-enhanced workloads.
- Generous 16GB GDDR6 VRAM: 16GB of high-speed GDDR6 memory on a 256-bit memory bus running at 20 Gbps, providing exceptional bandwidth for 4K textures, ray tracing, and professional content creation.
- Factory Overclocked: Boost clock up to 2970 MHz and Game Clock at 2400 MHz, delivering peak performance out of the box for the most demanding games and applications.
- Chipset: AMD RX 7600
- Memory: 8GB GDDR6
- XFX SWFT Dual Fan Cooling Solution
- Boost Clock: Up to 2655 MHz
Intel GPUs
- System Compatibility Note: 2‑slot ITX card, 169.9x123.5x39.2mm, single 8‑pin power, recommended 500W PSU. Verify chassis clearance before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Intel Arc A380 GPU: Powered by Intel Xe architecture with 6GB GDDR6 on 96‑bit bus – ideal for compact gaming, HTPC, and media builds.
- 2250MHz GPU Clock: Factory overclocked core delivers solid performance for esports titles and everyday creative tasks.
- Small Form Factor ITX Design: Compact 2‑slot card fits easily into mini‑ITX and small form factor cases without sacrificing performance.
- System Compatibility Note: This 2-slot card measures 249 x 132 x 41 mm and requires a recommended 650W power supply. Please verify available chassis space and PSU compatibility before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Next-Gen Xe2-HPG Architecture: Powered by the Intel Arc B580 GPU with 160 Xe Matrix Engines (XMX) and 20 compute units, delivering enhanced performance for 1440p gaming and AI-accelerated workloads.
- 12GB High-Bandwidth GDDR6 Memory: Features 12GB of fast GDDR6 memory on a 192-bit interface running at 19 Gbps, providing ample capacity for modern game textures and creative applications.
- Ultra-Fast Engine Clock: Delivers an engine clock of 2740 MHz out of the box, ensuring smooth, high-framerate gameplay and responsive performance in demanding titles.
- Intel Arc A750 8GB PCI Express 4.0 Graphics Card
- Item Package Dimension: 17.519L x 11.179W x 6.33H inches
- Item Package Weight - 4.64 Pounds
- Item Package Quantity - 1
- Product Type - VIDEO CARD
- OC Edition Boost Clock: 2660MHz
- TORN Cooling 2.0
- Metal Backplate
- Blue Breathing Light
- Next-Gen Intel Arc Graphics: Powered by Intel Arc A580 GPU with Intel Xe HPG microarchitecture, featuring 384 XMX engines for enhanced AI acceleration and content creation.
- High-Performance Memory: 8GB GDDR6 on a 256-bit interface running at 16 Gbps, delivering excellent bandwidth for 1440p gaming and creative workloads.
- Factory Overclocked: Engine clock set at 2000 MHz out of the box, providing optimized performance for smooth gameplay and multimedia tasks.
- Advanced Dual-Fan Cooling: Features a dual-fan design with striped axial fans and an ultra-fit heatpipe for efficient thermal management. 0dB Silent Cooling stops fans completely at low temperatures for silent operation.
- Durable Construction: Includes a stylish metal backplate for enhanced PCB rigidity and a premium aesthetic, backed by ASRock's Super Alloy components for long-term reliability.
What Happens When a Model Doesn’t Fit
Partial GPU Offloading
If your model is too large for VRAM but you have a discrete GPU, you can split the workload: some layers run on the GPU and the rest run on the CPU using system RAM. In Ollama and llama.cpp, you control this with the num_gpu parameter, which sets how many layers get offloaded to the GPU. For example, if you have 12 GB of VRAM and a 35 GB Q4 model, roughly 30–35% of the layers can fit on the GPU while the rest run on the CPU. The result is 3–5× slower than full GPU inference, but still significantly faster than pure CPU—and often fast enough for interactive use at 8–15 tok/s depending on the model and your system RAM bandwidth.
Swap Hell
If the model doesn’t fit in RAM at all, your OS starts paging data to the SSD swap file—and performance collapses. Even a fast NVMe SSD delivers roughly 5–7 GB/s of sequential read bandwidth, which is 15–25× slower than DDR5 and 50–100× slower than GDDR6X. In practice, swap-heavy inference drops to fractions of a token per second with constant disk thrashing. If you see your system grinding on swap (disk activity pegged at 100%, fans spinning, interface stuttering), stop the model immediately and switch to a smaller model or a more aggressive quantization level. There’s no tuning your way out of swap-bound inference.
How to Check Before You Start
Before loading a model, check whether it will fit. Run ollama show llama3.1:70b to see the model’s on-disk size (the in-memory footprint is similar). Then check your available memory: on Linux, run free -h; on macOS, open Activity Monitor and click the Memory tab; on Windows, open Task Manager → Performance → Memory. If the model size exceeds your free memory (not total—free), you’ll hit partial offload or swap. For GPU users, nvidia-smi shows current VRAM usage and remaining free VRAM.
Practical Recommendations by Use Case
| Use case | Recommended model size | Min RAM | Best config |
|---|---|---|---|
| Casual chatbot | 7B–8B | 16 GB | Any mini PC or Mac mini 16 GB |
| Coding assistant | 14B–32B | 32–64 GB | Mac mini 64 GB or AMD 64 GB mini PC |
| Creative writing/summarization | 14B–32B | 32–64 GB | 64 GB preferred for long context |
| RAG pipeline | 14B–32B | 32–64 GB | 64 GB; need room for embeddings model too |
| Multi-model server | Mixed | 64–128 GB | 128 GB AMD or 64 GB Mac + discipline |
A note on multi-model setups: if you want a chat model, a coding model, and an embeddings model loaded concurrently (a common setup for development workflows), plan for the sum of all model sizes plus overhead. A 14B chat model at Q4 (~8 GB) plus a 7B code model at Q4 (~4 GB) plus a small embeddings model (~1 GB) totals around 13 GB of model weight alone—add OS overhead and KV caches, and you’re looking at 18–20 GB minimum. With 32 GB of RAM, that’s workable but tight; with 64 GB, you have comfortable headroom.
When in doubt, 64 GB of fast memory (LPDDR5X or Apple unified) is the safest bet for serious local AI use. It handles 32B models comfortably at Q4, leaves room for long context windows and concurrent processes, and won’t feel limiting for at least the next generation of popular open-weight models. If you’re ready to buy, see our best mini PCs for AI buyer’s guide to pick a machine that matches your budget and use case.
FAQ
Is 16 GB enough for AI?
Yes, for 7B–8B models at Q4 quantization—think Llama 3.2 8B or Phi-4 3.8B. You’ll have enough headroom for short-to-medium context conversations and basic tasks. Anything larger than 8B will either require aggressive quantization or won’t fit alongside the OS and runtime overhead, so 16 GB is best treated as a starting point for experimentation rather than a long-term setup.
Do I need 64 GB RAM for AI?
Only if you want to run 30B+ parameter models, maintain long context windows (32K+), or keep several models loaded concurrently. For a single 7B–14B model, 32 GB is plenty. But if you’re planning to run a 32B coding model alongside an embeddings model for RAG, 64 GB gives you the breathing room to do it without constantly swapping models in and out of memory.
Does more RAM help even if the model fits in VRAM?
Marginally but meaningfully. Even when the model runs entirely on the GPU, the KV cache can spill into system RAM at very long context lengths. Extra system RAM also means the OS, runtime, and other applications aren’t competing for the same pool, which prevents unexpected slowdowns during inference. It won’t change your peak tok/s, but it improves stability during sustained use.
Can I add RAM later?
It depends on the machine. Many mini PCs—especially those with LPDDR5X—have soldered RAM that’s fixed at the factory. Some DDR5 SO-DIMM models (like certain Beelink or ASUS NUC configurations) do allow upgrades. Always check the spec sheet before buying; if local AI is your goal, buying the RAM you need upfront is almost always cheaper and safer than hoping for upgradeability.
Is Q4 quantization noticeably worse?
For most tasks—chat, coding, creative writing, summarization—barely. Blind comparison tests consistently show that users can’t reliably distinguish Q4_K_M output from FP16 on these workloads. Where Q4 falls short is in complex, multi-step mathematical reasoning and highly technical domain-specific tasks. If you’re doing medical or legal text analysis, consider Q5_K_M or Q6_K; for everything else, Q4_K_M lets you run a bigger model, which usually matters more than higher precision on a smaller one.
How much VRAM does a 70B model need?
At Q4_K_M: ~35–40 GB; at Q8_0: ~70 GB; at FP16: ~140 GB. No single consumer GPU has enough VRAM for a 70B model at Q4 (the RTX 4090 tops out at 24 GB), so you’ll need either a professional card (A6000 at 48 GB still falls short), multi-GPU, or a high-RAM system running CPU inference. A 64 GB Mac mini M4 Pro or a 128 GB AMD Strix Halo mini PC are the most practical compact options.
Unified memory vs discrete VRAM — which is better for AI?
Unified memory (Apple Silicon) is simpler to set up and very efficient for large models because there’s no VRAM size wall—the model can use nearly all installed memory without partial offloading. Discrete VRAM (NVIDIA GDDR6X) is faster per token when the entire model fits, thanks to 500–1,000 GB/s bandwidth. The practical answer: if your target model fits in a discrete GPU’s VRAM, the GPU wins on speed. If it doesn’t, unified memory with high capacity is a smoother experience.
What’s the cheapest way to run a 32B model?
A Mac mini M4 Pro with 64 GB unified memory (~$1,999) or an AMD-based 64 GB mini PC (Beelink, GMKtec, or similar) around ~$1,500. Both handle Qwen 2.5 32B at Q4 (~18–20 GB) with plenty of room for the OS, runtime, and moderate context lengths. The Mac offers higher memory bandwidth; the AMD options sometimes offer more flexibility with discrete GPU pairing if you expand later.
How does context length affect memory?
Context length directly increases the KV cache size, which sits in memory alongside the model weights. A 4K context on a 7B model adds about 0.5 GB; a 32K context on a 30B model adds 2–4 GB; and at 128K context (supported by some newer models), the KV cache alone can consume 8–16 GB for large models. If you’re running RAG pipelines or processing long documents, this runtime memory cost is often the difference between “fits comfortably” and “doesn’t fit at all.”
Should I buy more RAM or a better GPU?
For mini PCs, more RAM almost always wins. Mini PCs rarely support full-size discrete GPUs, so VRAM upgrades aren’t an option—your GPU is either integrated or limited to mobile-class parts. More system RAM lets you run larger models and longer contexts on CPU or unified memory inference, which is the primary bottleneck in compact form factors. If you’re building a desktop tower, a GPU upgrade makes sense; for the mini PC use case, put your budget into maximum RAM and the fastest memory type (LPDDR5X or unified) you can afford. See our Ollama setup guide for mini PCs once you’ve chosen hardware.
Conclusion
RAM (or unified memory) is the #1 spec for local AI; quantization is your best friend for fitting more in less. Use the look-up table to match the model you want to your hardware—and when you’re ready to buy, use our best mini PC for AI guide (linked in the intro) to pick a machine. For a broader overview of what hardware specs matter beyond memory, Arsturn’s Ollama hardware guide is a solid companion read.
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