Comparing 384GB and 512GB for Muse Spark Open? See the measured 512GB M3 Ultra path and the unverified 384GB boundary, including the published 304GB Q2_K and 344GB oQ3e tests and why they do not replace the official 700GB NVFP4 requirement.
For local AI, choose Mac Studio unified memory from the exact model artifact and workload—not from the largest number you can afford. Apple’s 2026 M5 lineup spans 36GB through 512GB, but that memory is shared by the CPU, GPU, macOS and other applications. The advertised total is not guaranteed model capacity.
Compare current configurations · Plan by memory tier · Use the buying checklist
Short answer: leave headroom after weights for macOS, the runtime, KV cache and your other applications. A model file slightly smaller than the advertised unified-memory total is not a safe fit conclusion.
Current M5 Mac Studio memory options
| Chip path | Published unified-memory options | Published bandwidth | Important configuration limit |
|---|---|---|---|
| M5 Max | 36GB; configurable to 48GB, 64GB or 128GB | 460GB/s base; 614GB/s on the 40-core GPU configuration | The larger memory options require the specified higher M5 Max configuration. |
| M5 Ultra | 96GB; configurable to 256GB or 512GB | 1.2TB/s | 512GB requires the 36-core CPU / 80-core GPU configuration. |
Apple’s launch announcement states up to 512GB of unified memory and 1.2TB/s bandwidth for M5 Ultra. Those are capacity and bandwidth specifications, not independent evidence that every local-AI runtime or model uses the hardware efficiently.
Unified memory is shared memory
Apple silicon gives the CPU and GPU access to one unified memory system, which avoids copying every buffer between separate CPU RAM and dedicated VRAM. But macOS, the runtime, model weights, KV cache, temporary workspace, the user interface and other applications all draw from the same total.
Do not compare “64GB unified memory” with “64GB dedicated VRAM” as if the full capacities were equally available to a model. The architectures, software paths and allocation constraints differ. Read VRAM vs unified memory before comparing a Mac with a discrete GPU.
How to think about each memory tier
36GB and 48GB: smaller and mid-size local models
These configurations can provide useful room for quantized 7B, 14B and 32B-class models, depending on the artifact, context and runtime. A raw 32B model at four bits is approximately 16GB before quantization metadata, cache and overhead, so the exact downloadable file and measured resident memory still matter.
Do not choose 48GB on the assumption that a roughly 44GB 70B artifact is comfortable. Shared-system use, unit conversion, KV cache and buffers can consume the remaining margin. A model starting with reduced context or swap is not proof that the intended workflow fits.
64GB: more 70B options, still with a budget
Sixty-four gigabytes creates more room for selected 70B quantizations, but the complete allocation remains the test. Qwen’s official Qwen2.5-72B GGUF artifacts illustrate why labels are not enough: a larger quantization, longer context or simultaneous requests can consume the added margin quickly. Use the 70B VRAM guide for sourced artifact sizes and cache arithmetic.
96GB and 128GB: headroom beyond common 70B quantizations
These tiers can leave substantially more shared-memory headroom after many quantized 70B-class weight files. That headroom may serve longer context, concurrency, retrieval components or other applications. It does not automatically improve generation speed, and 128GB is still below the raw 145.4GB required by 72.7 billion parameters at two bytes each before runtime overhead.
The choice between 128GB M5 Max and 96GB M5 Ultra is not a memory-only comparison. The Ultra has higher published bandwidth and a larger GPU configuration, while the Max option has more nominal capacity in this pair. Measure the exact runtime and task.
256GB and 512GB: very large models and multi-component workflows
These capacities can make model families and precision levels inaccessible to ordinary discrete GPUs technically possible on one system. Capacity alone does not establish usable speed, model-format support, kernel coverage or application stability. Before paying the configuration premium, confirm that the intended runtime supports the model and estimate prompt-processing and generation time from a comparable test.
A model-size formula is only the first line
Raw weight bytes are approximately:
parameters × bits per weight ÷ 8That arithmetic excludes quantization metadata, embeddings stored at different precision, KV cache, runtime buffers and the rest of the operating system. GGUF labels such as Q4_K_M are mixed formats rather than exact four-bit files. Read the GGUF quantization guide before converting a filename into a capacity claim.
Software compatibility comes before capacity
Mac local-AI applications commonly use Metal or MLX paths rather than CUDA. Check the exact application, model format, quantization, required extensions and Apple-silicon support before buying. A CUDA-only workflow does not become compatible because the Mac has enough unified memory.
Compare the software and system tradeoffs in Mac Studio vs RTX 5090. If a required library or custom kernel is NVIDIA-only, capacity does not resolve the mismatch.
Bandwidth is not tokens per second
Apple publishes 460GB/s or 614GB/s for M5 Max configurations and 1.2TB/s for M5 Ultra. Memory bandwidth can matter for inference, but tokens per second also depend on the model, quantization, prompt phase, kernels, runtime build, context, concurrency and thermal behavior. Do not apply the ratio between bandwidth specifications directly to a performance estimate.
Mac Studio local-AI buying checklist
- Name the exact model artifact and quantization.
- Set the context length and simultaneous-request count you need.
- Estimate weights, KV cache and runtime overhead, then reserve OS/application headroom.
- Confirm the exact Mac Studio chip, GPU-core configuration and available memory option.
- Verify the runtime, model format and required features on Apple silicon.
- Find or run a comparable prompt-processing and generation test.
- Check storage: large model libraries can exceed the base SSD quickly.
- Compare the full configured system price, not a base Mac with a standalone GPU card.
Use the LLM VRAM calculator as a planning range and the local-LLM benchmark method to decide whether a configuration passes. Browse the Mac Studio offer page only after the memory and software requirements are written down.
Reviewed October 24, 2026. Apple specifications and configure-to-order combinations can change. HighVRAM has not independently benchmarked every M5 Mac Studio configuration and does not claim that advertised unified memory is fully available to a model.
Considering the 128GB compact-system class? Compare Mac Studio with DGX Spark across shared-memory headroom, bandwidth and Metal/MLX versus CUDA on Arm Linux.
DGX Spark alternatives: Compare Mac Studio with the broader DGX Spark alternative set in the constraint-first comparison hub.
Planning Muse Glimmer 30B on a Mac? Use the 24GB, 32GB, 48GB and 64GB unified-memory guide to separate Meta’s general Q4 target from the 48GB+ SGLang MLX path and application-specific memory pressure.
Planning Qwen3.8-27B on Apple silicon? Use the 16GB, 24GB, 32GB, 48GB and 64GB unified-memory guide to separate community GGUF fit from Qwen’s official checkpoints and account for macOS, cache, Metal state and swap.