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Short answer: choose hardware from the specific Llama model, not from the Llama name. A 1B–3B model can run on a modern laptop or desktop, often without a discrete GPU. An 8B–11B model needs substantially more memory, while a 70B model is normally workstation or server hardware at FP16. Llama 3.1 405B is a multi-GPU, multi-node deployment project.
Meta’s published figures are model-memory estimates, not complete system requirements. You must add headroom for the operating system, inference runtime, KV cache, context window, and any concurrent users. Quantization can lower the memory threshold, but may alter output quality and speed.
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Start with the model tier
“Running Llama locally” can mean anything from a small assistant on a laptop to a multi-node server deployment. Llama releases span several orders of magnitude in size, so there is no single correct GPU, RAM capacity, or CPU specification.
Meta describes its 1B and 3B models as lightweight and suitable for on-device and edge deployment. Llama 3.1 is available in 8B, 70B, and 405B variants; Llama 3.2 includes a 3B text model and an 11B vision model; and Llama 3.3 is a 70B text-only model. Vision models also need memory for image processing in addition to the language model.
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The practical decision is to identify the exact checkpoint, decide whether you will use FP16 or a quantized format, estimate the context length and number of simultaneous requests, then select a memory architecture that can hold the resulting workload.
Meta’s official FP16 planning figures
The clearest starting point is Meta’s current self-hosting guidance. The values below describe the model in FP16 and should not be read as the total amount of system RAM or VRAM a finished computer can get by with.
| Model | Published FP16 model memory | Accelerator example from Meta | What the figure means |
|---|---|---|---|
| Llama 3.2 3B | 6 GB | 1× A10 24 GB or T4 | Room remains for the runtime and a normal context window on the listed accelerator class. |
| Llama 3.2 11B | 22 GB | 1× A100 40 GB or L40S | The 22 GB is the model allocation; vision processing and context add overhead. |
| Llama 3.3 70B | 140 GB | 2× A100 80 GB or an H100-class setup | At FP16 this is a multi-accelerator workload, not a typical gaming-PC target. |
These figures come from Meta’s deployment guidance accessed in 2026. They are planning values, not a promise of consumer performance or a universal minimum. A card with exactly the published model-memory amount can still fail when the operating system, runtime metadata, KV cache, tokenizer, graphics stack, or a longer prompt needs additional space.
Hardware by use case
Small local assistants: 1B to 3B
A modern laptop or desktop with adequate system RAM is a sensible entry point for a 1B or 3B model. Meta explicitly positions these sizes for on-device and edge use. A CPU-capable runtime can execute them without a dedicated GPU; a modest accelerator can improve response time but is optional.
For a practical setup, prioritize 16 GB or more of system memory so the operating system and runtime are not fighting the model for space. That is a usability recommendation rather than a published Meta minimum: no universal CPU core count or RAM minimum was established in the available official material. If you plan very long prompts, image inputs, or other applications at the same time, choose more memory.
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Integrated graphics and Apple silicon unified memory can work because the model can use system memory. The trade-off is throughput: CPU or shared-memory execution generally produces fewer tokens per second than a discrete GPU with sufficient VRAM.
Mid-size text and vision models: 8B to 11B
An 8B or 11B model is where memory planning becomes important. Meta’s FP16 example for Llama 3.2 11B is 22 GB and pairs it with an A100 40 GB or L40S. A consumer machine may run a quantized version in less memory, but it should not be sized to the compressed file alone.
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Plan for the quantized model, the runtime, and the KV cache that grows with the context window. A vision model also needs working memory for image encoding and the visual tokens inserted into the prompt. If your workload is occasional chat with moderate context, one accelerator with enough headroom may be adequate. For larger contexts, multiple users, or image-heavy requests, system RAM or a second accelerator can become necessary.
Large models: 70B
At FP16, 70B is workstation or server territory. Meta lists 140 GB of model memory for Llama 3.3 70B and specifies two A100 80 GB accelerators or an H100-class configuration. A single 24 GB or 48 GB card cannot hold that FP16 model by itself.
Quantization changes the buying threshold. Formats using fewer bits per weight can put a 70B model within reach of a machine that combines GPU memory with system RAM. The result depends on the exact quantized file, runtime build, context length, and how many layers are offloaded to the GPU. CPU-plus-GPU hybrid execution can work, but tokens that must be calculated on the CPU or transferred across the bus usually reduce responsiveness compared with a fully resident multi-GPU model.
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For a shared service, throughput matters as much as whether one request fits. Concurrent requests each need KV-cache capacity, so a configuration that handles one short conversation may run out of memory under several users or long documents.
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Treat Llama 3.1 405B as a data-center project. Meta’s model-download documentation states: “This model requires significant storage and computational resources, occupying approximately 750GB of disk storage space and necessitating two nodes on MP16 for inference.” The statement establishes both the storage scale and the reference deployment shape; it is not a recommendation for a particular consumer workstation.
A 405B deployment requires multiple high-memory accelerators, fast interconnects, substantial host memory, and storage that can hold the model plus downloaded variants and temporary files. Even if a heavily quantized build reduces the weight footprint, loading, serving, and maintaining useful context remain distributed-systems problems. A desktop with one GPU should not be treated as a realistic 405B target.
How precision and quantization change the requirement
FP16 stores each parameter in 16 bits, or about two bytes. That is why Meta’s published model-memory examples are close to parameter count multiplied by two: 3B is about 6 GB, 11B about 22 GB, and 70B about 140 GB. The calculation describes weights only.
Quantization stores weights with fewer bits. The llama.cpp project supports integer formats from 1.5-bit through 8-bit. Lower-bit files generally use less memory and can make local inference possible on smaller GPUs or on CPU-plus-GPU systems. They can also change output quality, arithmetic speed, and compatibility with particular kernels. A nominal “4-bit” file is not exactly one quarter of the FP16 footprint because scales, metadata, alignment, and runtime buffers add overhead.
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Use the quantized file’s stated size as a floor, then reserve room for the runtime and context. Two files for the same model and nominal bit depth can have different memory needs. If quality is critical, compare the model’s behavior at the chosen quantization instead of assuming that the smallest file is equivalent to FP16.
Memory architecture: VRAM, unified memory, RAM, and hybrid offload
Discrete GPU VRAM
VRAM is the fastest place to keep weights and intermediate tensors for GPU inference. A model that fits completely in VRAM usually avoids the transfers that make hybrid execution slower. For FP16, compare the total model-memory figure with the combined capacity of the GPUs, then leave headroom rather than filling every gigabyte.
Apple unified memory
Apple silicon systems use a shared memory pool for the CPU and GPU. That makes them viable for local inference when the total unified-memory capacity is large enough for the model, runtime, and context. The memory is not dedicated solely to Llama: macOS and other applications consume part of the pool. A 16 GB system may be comfortable for small models but is not automatically suitable for an 11B or larger model.
System RAM
CPU inference loads model weights into system RAM. It can run without a dedicated GPU, but capacity and memory bandwidth both matter. RAM equal to the compressed file size is not sufficient once the operating system, runtime, context cache, and other programs are included. No source used for these figures publishes a universal consumer RAM minimum, so treat any exact number as workload-specific rather than an official requirement.
CPU-plus-GPU hybrid execution
llama.cpp supports hybrid inference when the model exceeds available VRAM. Some layers remain in system memory while others run on the accelerator. This is useful for models that narrowly exceed a card’s capacity, but performance depends on PCIe or other interconnect bandwidth, CPU speed, layer placement, and context size. Hybrid mode is a capacity solution first; it is not guaranteed to match the speed of a fully GPU-resident model.
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Runtime compatibility and model files
llama.cpp is designed for local inference with minimal dependencies. Its documented targets include Apple silicon, x86 processors with AVX-family instructions, CUDA, AMD HIP, Vulkan, and SYCL. It uses GGUF files for its standard workflow and can split work between CPU and GPU.
Before buying hardware, verify that the runtime you intend to use supports the accelerator backend and the model file format. A powerful GPU is not useful if the installed build lacks its backend, if the model is in an unsupported format, or if the driver cannot allocate the required memory. For a first deployment, use a runtime build that matches your operating system and accelerator stack, download a GGUF model at the intended quantization, and check memory usage while increasing context length gradually.
Storage, loading time, and context headroom
Storage is separate from inference memory. You need space for the model file, possible alternate quantizations, tokenizer data, caches, logs, and the operating system. The 405B documentation’s approximately 750 GB figure is a direct warning about this distinction: disk capacity can become a constraint before you have solved accelerator capacity.
Use an SSD with enough free space for the largest model you plan to keep plus temporary download space. Loading a multi-hundred-gigabyte model also takes time and stresses storage throughput. Keep an additional margin rather than filling a drive to its nominal capacity.
The context window affects working memory after the weights are loaded. Longer prompts, document retrieval, conversation history, and image tokens expand the KV cache. If a short prompt fits but a long one fails, reduce the context setting, lower concurrency, or move to hardware with more memory. Do not infer that a model’s weight file size is its complete runtime requirement.
A step-by-step sizing method
- Identify the exact checkpoint. Write down the family, parameter tier, text or vision variant, and release. “Llama 3” is not specific enough to size hardware.
- Choose precision. Use Meta’s FP16 figure for an uncompressed baseline, or record the exact quantized file size and bit format you intend to run.
- Add runtime and context headroom. Reserve memory for the operating system, inference process, KV cache, tokenizer, image processing, and any other applications.
- Choose the memory location. Decide whether weights will live in discrete VRAM, Apple unified memory, system RAM, or a CPU-plus-GPU split.
- Check workload shape. One short chat, long-document analysis, image prompts, and multiple simultaneous users have different cache requirements.
- Verify backend support. Confirm that the runtime build, drivers, model format, and accelerator backend work together before committing to hardware.
- Test below the limit. Start with a moderate context and one request, observe actual memory use, then increase context or concurrency. If allocation fails, reduce those settings or move part of the model to system RAM.
Common buying mistakes and fixes
- Buying by model name alone: Llama 3.2 3B and Llama 3.3 70B have radically different requirements. Size the parameter tier first.
- Matching VRAM to the weight file exactly: Leave room for runtime buffers and KV cache; otherwise loading may succeed but generation will fail at longer contexts.
- Assuming quantization is lossless: Lower-bit formats reduce memory, but quality and speed can change. Select a quantization appropriate to your accuracy needs.
- Ignoring vision overhead: An 11B vision model is not just an 11B text model when images are in the prompt. Reserve additional working memory.
- Confusing capacity with speed: Hybrid CPU/GPU execution can make a model fit while remaining slower than a fully GPU-resident deployment.
- Planning a 70B or 405B desktop deployment as if it were a 3B assistant: 70B FP16 is a multi-accelerator workload, and Meta describes 405B as requiring approximately 750 GB of storage and two MP16 nodes.
- Using unsupported software backends: Confirm CUDA, HIP, Vulkan, SYCL, or Apple-silicon support in the runtime build you will actually install.
What the published figures do not establish
The available official guidance does not provide one universal minimum CPU model, core count, system-RAM number, tokens-per-second result, or consumer GPU recommendation. Performance varies with quantization, context, batch size, backend, drivers, memory bandwidth, and concurrency. Use Meta’s figures to set the scale of the deployment, then validate the exact model and workload on the intended machine.
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