Apple Silicon LLM Inference Speed Benchmarks: A Complete Guide to Local Large Model Performance on Mac

Why Apple Silicon LLM Inference Performance Matters

As large language models (LLMs) increasingly move from the cloud to local deployment, individual developers and researchers are asking: just how fast can a Mac equipped with Apple Silicon (M-series) chips run local large models? A recent post on Hacker News titled "Measured LLM inference speeds on Apple Silicon" attracted attention, with the author releasing complete raw test data under a CC BY 4.0 license, providing the community with a rare cross-reference benchmark.

For many, Apple's Unified Memory Architecture (UMA) is a natural advantage for running large models locally — the CPU, GPU, and Neural Engine share the same pool of high-bandwidth memory, meaning model weights don't need to be repeatedly copied between devices, significantly reducing latency. This architecture, adopted by Apple starting with the M1 chip, integrates all processing units into a single System-on-Chip (SoC) sharing a physical memory pool. In traditional PC architectures, the CPU uses system memory (DDR) while the GPU uses dedicated video memory (GDDR or HBM), with data transfers between them going through the PCIe bus — a copying process that becomes a serious bottleneck in large model inference. UMA eliminates this data transfer overhead, allowing all processing units to access model weights with zero-copy. Apple's UMA uses LPDDR5/LPDDR5X memory chips packaged directly on the SoC substrate, achieving a balance of high bandwidth and low power consumption — particularly critical for large model inference with weights often reaching tens of gigabytes.

But what real-world inference speeds actually look like has long lacked systematic, transparent measured data. This post is an attempt to fill that gap.

The Value of Open Data: Reproducible LLM Performance Benchmarks

The most commendable aspect of this post is the author's choice to release raw data under the CC BY 4.0 Creative Commons license. CC BY 4.0 (Creative Commons Attribution 4.0 International) is an extremely permissive open license published by Creative Commons — anyone can freely copy, modify, and redistribute the data or work, including for commercial purposes — with the sole condition of attributing the original source. This license is widely used in academic research and open-source communities and is considered one of the best practices for promoting knowledge sharing.

In the AI field, performance comparisons are often filled with vendor marketing language and unreproducible "benchmarks." Many hardware manufacturers or model developers publish performance data without reproducible test condition descriptions, preventing the community from independently verifying claims. An open, transparent raw dataset is therefore especially valuable — it means other researchers can not only cite conclusions but also download the raw data for secondary analysis, which holds significant importance in building fair performance evaluation systems.

Why Raw Inference Data Matters

Many performance reviews only present conclusive charts while hiding critical parameters like test environments, model quantization methods, context lengths, and batch sizes. Yet these variables are precisely what determine inference speed (typically measured in tokens/second).

Tokens/second (tokens generated per second) is the most fundamental metric in LLM inference performance evaluation. Here, a token is the basic unit in the model's vocabulary — typically one English word corresponds to 1-2 tokens, while one Chinese character corresponds to 1-3 tokens. Generally, the average person's reading speed is about 4-5 tokens per second, so when inference speed reaches above 10 tokens/s, the user experience approaches "real-time fluid conversation"; below 3 tokens/s, users feel a noticeable delay. It's important to note that tokens/second typically needs to distinguish between prompt evaluation (prefill) speed and generation speed — the two have different bottlenecks, and actual user experience primarily depends on generation speed.

Open raw data allows researchers to:

• Independently verify conclusions, avoiding "black-box benchmarks"
• Re-analyze as needed, for example focusing only on performance at a specific quantization level
• Cross-compare with their own devices, determining whether their Mac configuration achieves expected inference levels

This approach aligns with the reproducibility principles long advocated by the open-source community, grounding local LLM deployment discussions in facts rather than impressions.

Key Factors Affecting Apple Silicon Inference Speed

While the original post focuses on data, combining it with general principles of local LLM inference, we can identify several core variables that determine model running speed on Apple Silicon.

Memory Bandwidth Is the Hard Constraint

For generative inference (particularly the decoding phase), performance is often limited by memory bandwidth rather than compute power. To understand this, you need to know the two phases of large language model inference: Prefill and Decode. The prefill phase processes all input tokens at once — this is compute-intensive, and GPU power is the primary bottleneck. The decode phase, however, involves autoregressive token-by-token generation, where each generated token requires reading the entire model's weight matrix from memory.

Take a 70B parameter model as an example: even after 4-bit quantization, its weights are still approximately 35GB, meaning each token generated requires reading about 35GB of data from memory. Therefore, decode-phase throughput is almost entirely determined by memory bandwidth.

Memory bandwidth varies dramatically across Apple's chip tiers: from approximately 100GB/s for entry-level M-series chips to 400GB/s or higher for Max and Ultra variants. Taking the base M4 as an example, with memory bandwidth of approximately 120GB/s, theoretically running a 4-bit quantized 70B model allows at most about 3.4 complete model weight reads per second (120÷35≈3.4), yielding approximately 3.4 tokens/s. The M4 Max, with bandwidth reaching 546GB/s, has a theoretical ceiling of approximately 15.6 tokens/s. This directly determines how many tokens per second a large model can throughput.

This is why within the same generation of chips, M4 Pro, M4 Max, and M4 Ultra versions show multifold speed differences when running large models — memory bandwidth differences are directly reflected in tokens/second output.

Quantization Precision Determines Feasibility

Model quantization (e.g., 4-bit GGUF, 8-bit) affects not only memory usage but also inference speed significantly. Model quantization refers to the technique of converting neural network weights from high-precision floating-point numbers (such as FP16 or FP32) to low-bit integers (such as INT8, INT4). GGUF is a quantized model file format defined by the llama.cpp project, supporting various quantization strategies like Q4_K_M, Q5_K_S, Q8_0, etc., with different strategies providing different trade-offs between compression ratio and precision retention.

Taking Q4_K_M as an example, it quantizes most weights to 4-bit integers while retaining higher precision for critical layers — currently one of the most popular quantization schemes in the community. Lower-precision quantization allows larger models to fit within limited unified memory while reducing memory reads per inference to boost throughput, but excessive quantization degrades model output quality, manifesting as weakened reasoning ability and reduced text coherence. In recent years, quantization methods like GPTQ, AWQ, and GGUF have achieved extremely high compression ratios while maintaining model capability as much as possible, making it a reality to run multi-billion parameter models on consumer hardware.

If raw data covers multiple quantization configurations, it will help users find the optimal balance between "speed, quality, and model scale."

Inference Framework Optimization Level

On the Apple platform, the maturity of frameworks like llama.cpp (with Metal backend) and MLX directly affects measured performance.

llama.cpp is an open-source project initiated by Georgi Gerganov, originally aimed at efficiently running Meta's LLaMA model on pure CPU. Through rapid community iteration, it has evolved into a universal inference engine supporting hundreds of model architectures across multiple platforms (x86, ARM, CUDA, Metal, Vulkan). On Apple platforms, llama.cpp uses the Metal backend to invoke GPU-accelerated matrix operations, making it currently the most mature and broadly compatible local inference solution.

MLX is a deep learning framework open-sourced by Apple's machine learning research team in late 2023, designed specifically for Apple Silicon's unified memory architecture. MLX's core advantage lies in its "lazy evaluation" mechanism and native support for unified memory — data doesn't need explicit transfer between CPU and GPU, and the framework automatically schedules computation to the most appropriate processing unit. In community benchmarks, MLX's inference speed has surpassed llama.cpp's Metal backend for certain models and configurations, particularly excelling in the prefill phase. However, llama.cpp still holds advantages in model format compatibility, quantization scheme diversity, and cross-platform deployment.

The two complement each other, and choosing between them depends on specific use cases and model requirements. Therefore, the same device may show significant speed differences across different inference frameworks, making software stack selection equally critical.

Practical Guidance for Local Large Model Deployment on Mac

For developers and enthusiasts looking to run local large models on Mac, this type of measured data has direct decision-making value.

Mac Configuration Purchase Reference

If you're debating how much memory or which chip tier to get for your Mac to run local models, an inference speed benchmark covering multiple chip models helps you avoid the awkward situation of "buying too small and unable to run, or buying too large with poor cost-effectiveness." Especially for users hoping to run 30B or even 70B parameter models, memory capacity (at least 32GB or 64GB unified memory) and bandwidth selection are crucial.

Taking the 4-bit quantized version of a 70B parameter model as an example, model weights alone require approximately 35GB of memory. Adding KV Cache (a cache for storing intermediate states in the attention mechanism) and system overhead, the actual requirement exceeds 48GB of available memory. If choosing 8-bit quantization, memory needs double to approximately 70GB. Therefore, 64GB of unified memory is essentially the minimum configuration for running 70B models, while 128GB or 192GB Ultra versions provide ample space for longer context windows and higher-precision quantization.

Dual Considerations of Privacy and Cost

The biggest appeal of local inference lies in data privacy and long-term cost control. All inference is completed on-device, eliminating the need to upload sensitive data to the cloud; after a one-time hardware investment, there's no longer a need to pay per token. Using OpenAI GPT-4o's API pricing as reference, the cost per million output tokens is several dollars — in high-frequency usage scenarios, monthly API costs could reach hundreds or even thousands of dollars. By comparison, the marginal cost of a one-time high-end Mac investment approaches zero over long-term use (only electricity), making the economic case very clear for developers and researchers requiring high-volume inference calls.

Clear performance data helps users evaluate whether a local solution can meet their response speed requirements for actual use cases.

Transparent Data Drives Healthy Development of the Local AI Ecosystem

Although this Hacker News post didn't generate much discussion heat (4 points, no comments at the time), it represents a practice direction worth encouraging — replacing vague marketing language with open, reproducible raw data. As Apple Silicon performance continues to climb and local inference frameworks like MLX continue to mature, Mac is becoming a viable platform for individuals to run large models.

In this context, transparent benchmark data contributed by the community becomes increasingly important. It not only helps individuals make wiser hardware and software choices but also lays a solid factual foundation for the healthy development of the entire local AI ecosystem. For anyone seriously considering local LLM deployment, measured resources like this released under the CC BY 4.0 license are worth bookmarking and following closely.