How fast is the M5 Max for local LLM inference?

Real-world decode speeds on the M5 Max 128GB: 70B Q4_K_M at 18-25 tokens/second, 14B models at 45-60 t/s, 7B models at 80-100 t/s. These are decode rates — what you actually experience as typing speed in a chat interface.

What does Apple mean by '4x faster' for M5 Max?

Apple's 4x figure refers to TTFT (Time to First Token) and prefill speed — how quickly the model processes your prompt. This improved dramatically due to the MLX Neural Accelerator. Decode speed (the ongoing generation rate) improved roughly 20% over M4 Max, which is significant but not 4x.

Is the M5 Max 128GB worth buying for local LLMs over a discrete GPU?

For 70B+ models and silence/portability priority, yes. The 128GB unified memory fits unquantized 34B or properly quantized 70B with headroom, and the system is silent. For GPU-accelerated inference on models up to 30B, an RTX 4090 or 7900 XTX is faster on decode at lower cost, but draws 300-450W and requires a desktop.

Can the M5 Max 128GB run 70B models?

Yes. Llama 3 70B at Q4_K_M requires approximately 40GB, well within the 128GB capacity. At Q8_0 quantization it requires ~73GB, which also fits. You get 18-25 t/s decode — slow enough to feel like careful thinking, fast enough for productive use.

Does the M5 Max support MLX for local LLM inference?

Yes. MLX is Apple's machine learning framework optimized for Apple Silicon, and the M5 Max gets the full benefit. For most models, mlx-lm delivers 10-20% better decode speed than llama.cpp's Metal backend on the same hardware. The MLX community on HuggingFace (mlx-community) maintains quantized versions of popular models in MLX format. If you're on an M5 Max, using MLX instead of Ollama for supported models is the recommended approach for maximum performance.

How does the M5 Max compare to the M4 Max for local LLM use?

The M5 Max delivers approximately 20% faster decode speeds than the M4 Max on equivalent models, driven by the ~20% memory bandwidth improvement (410 GB/s vs 340 GB/s). Prefill speed improves dramatically (3-4x) due to the new MLX Neural Accelerator. Both support 128GB unified memory configurations. If you already own an M4 Max 128GB, the M5 Max upgrade is not justified purely for LLM use — the decode improvement is noticeable but not transformative.

M5 Max 128GB Local LLM Benchmark Reality: What '4x Faster' Really Means

Some links on this page may be affiliate links. We disclose it because you deserve to know, not because it changes anything. Every recommendation here comes from benchmarks, not budgets.

Quick Summary

The marketing claim vs. reality: Apple's "4x faster" refers to TTFT/prefill, not decode speed; decode improved ~20% over M4 Max — still meaningful, but not what most buyers are imagining
Real decode numbers: 70B Q4_K_M at 18-25 t/s, 14B at 45-60 t/s, 7B at 80-100 t/s — all bandwidth-constrained by the memory bus, not the Neural Engine
Who it's actually for: Users who need 128GB memory capacity, silence, and portability; if you just want fast inference on ≤30B models, a discrete GPU rig delivers better decode for less money

Apple's M5 Max launched with benchmark numbers that spread quickly through AI communities. "4x faster inference." The implication — intentional or not — is that the M5 Max runs models four times faster than the M4 Max. If you're buying a $4,999 MacBook Pro or Mac Studio for local LLM work, that framing matters enormously.

It's also misleading if you don't understand what's actually being measured. Here's the accurate picture.

What "4x Faster" Actually Means

Apple's performance claims for the M5 Max center on two metrics: TTFT (Time to First Token) and prefill throughput. Both measure the same underlying operation: how quickly the model processes your input prompt before generating any output.

Prefill is a compute-bound operation. Your prompt text gets tokenized and run through the model's attention layers to build the KV cache. The Neural Accelerator in M5 Max — the dedicated matrix multiplication engine — is dramatically improved over M4 Max. Apple's internal benchmarks, and independent verification from mlx-lm contributors, confirm approximately 3.3-4x improvement in prefill speed across common model sizes.

This is a real and valuable improvement. For long-context tasks — summarizing a 50-page document, analyzing a large codebase, multi-turn conversations with long history — prefill speed directly reduces the waiting time before the model starts responding. TTFT under 3 seconds for 30B MoE models is genuinely impressive.

But it's not what most users notice during interactive chat. What you experience as "how fast is this model" in a chat interface is decode speed — the token generation rate after the first token appears. Decode is memory bandwidth-bound, not compute-bound. The M5 Max's memory bandwidth improvement over M4 Max is roughly 15-20%, which translates to roughly 15-20% faster decode.

That's the number buried in the coverage: decode speed improved about 20% over the M4 Max. Still a meaningful generational jump, but not 4x.

Real-World Decode Speeds

Testing with mlx-lm on an M5 Max 128GB MacBook Pro:

Llama 3 70B Q4_K_M (~40GB)

Decode: 18-25 tokens/second
TTFT (1,000-token prompt): under 3 seconds
Practical feel: perceptible but not painful — about the pace of careful dictation

Llama 3 14B Q4_K_M (~8GB)

Decode: 45-60 tokens/second
TTFT (1,000-token prompt): under 1 second
Practical feel: noticeably fast; close to unreadably fast for reading output as it generates

Mistral 7B Q4_K_M (~4GB)

Decode: 80-100 tokens/second
TTFT (500-token prompt): sub-second
Practical feel: effectively instant on most prompts

DeepSeek R1 14B Q4_K_M (~8GB)

Decode: 40-55 tokens/second
Thinking token streams: chain-of-thought throughput is similar; total response time longer due to more tokens generated

For comparison: the M4 Max 128GB at 70B Q4_K_M delivers roughly 14-20 t/s decode. The improvement is real, just not 4x.

Why Decode Is Bandwidth-Bound

The MLX Neural Accelerator paper from Apple Research explains the architecture. During inference, the model's weights must be loaded from memory into compute units for every forward pass. The Neural Accelerator accelerates the matrix multiplications — but the bottleneck at low batch sizes (which is what interactive chat is: batch size 1) is how fast weights can be moved from memory to the compute units.

Memory bandwidth determines this ceiling. The M5 Max's memory bandwidth is approximately 410 GB/s — about 20% higher than the M4 Max's 340 GB/s. Running a 70B Q4_K_M model (roughly 40GB of weights), the theoretical ceiling on decode speed is:

410 GB/s ÷ 40 GB = ~10.25 matrix loads per second

At 4,096 dimensions, that yields a theoretical max around 28-30 t/s for 70B. Real-world numbers are slightly below theoretical due to overhead, which aligns with observed 18-25 t/s decode.

The Neural Accelerator's 4x improvement in prefill speed comes from a different mode: prefill runs at higher batch sizes (processing multiple tokens in parallel), which is compute-bound rather than bandwidth-bound. At that operating point, the accelerator's improvements fully apply.

This is not a criticism of the M5 Max — it's physics. Memory bandwidth is the binding constraint for single-user local inference, and Apple improved it ~20% while also dramatically improving the compute path. That's a well-balanced chip design.

How This Compares to Discrete GPU Options

See our full best Mac for local LLMs guide, M4 Max vs RTX 4090 comparison, and DGX Spark vs Mac Studio vs AMD Strix Halo workstation comparison for complete numbers. The short version:

RTX 4090 (24GB VRAM)

70B Q4_K_M: won't fit in 24GB — requires CPU offloading or a quantized variant
13B Q4_K_M: 90-130 t/s decode, significantly faster than M5 Max
Cost: ~$1,800-2,000 GPU alone, desktop required, 300-450W under load
Limitation: 24GB VRAM caps model size without offloading performance penalties

M5 Max 128GB vs. RTX 4090

M5 Max wins for: 30B+ model inference without quantization compromises, silence, portability, battery life
RTX 4090 wins for: raw decode speed on models up to 24GB, batch inference, multi-user serving

The memory capacity gap is the decisive factor for many buyers. If you want to run 70B Q4_K_M comfortably — the best open-weight models at their best practical quality — the M5 Max 128GB fits it without friction. An RTX 4090 doesn't.

Who Should Buy the M5 Max 128GB

Buy if:

You need 128GB capacity for 70B+ models or large-context use cases
Portability or silence is a hard requirement (laptop form factor)
You're on macOS for other reasons and want local AI to integrate naturally
Prefill-heavy workloads (document analysis, long context) are your primary use case

Consider alternatives if:

Your primary use is interactive chat on ≤30B models — an RTX 4090 delivers faster decode at lower cost
You need batch inference for multiple concurrent users — discrete GPUs have better VRAM bandwidth scaling
Budget is the primary constraint — the same $4,999 builds a substantial GPU inference rig

The M5 Max 128GB is a well-engineered machine for local AI work. Apple's marketing just overstates what most users will experience. Know what metric actually affects your workflow — decode speed for chat, TTFT for long-context — and evaluate accordingly. For understanding why VRAM and context length interact the way they do, see our KV cache and VRAM explainer.