L
localai.computer
ModelsGPUsSystemsAI SetupsBuildsOpenClawMethodology

Resources

  • Methodology
  • Submit Benchmark
  • About

Browse

  • AI Models
  • GPUs
  • PC Builds

Guides

  • OpenClaw Guide
  • How-To Guides

Legal

  • Privacy
  • Terms
  • Contact

© 2025 localai.computer. Hardware recommendations for running AI models locally.

ℹ️We earn from qualifying purchases through affiliate links at no extra cost to you. This supports our free content and research.

Can Apple M2 Max run context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16?

Runs Q496GB VRAM availableRequires 2GB+

Apple M2 Max meets the minimum VRAM requirement for Q4 inference of context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.

What this means for you

Apple M2 Max can run context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 with Q4 quantization. At approximately 69 tokens/second, you can expect Good speed - acceptable for interactive use.

You have 94GB headroom, which is sufficient for system overhead and smooth operation.

Quantization breakdown

QuantizationVRAM neededVRAM availableEstimated speedVerdict
Q42GB96GB69.11 tok/s✅ Fits comfortably
Q83GB96GB43.52 tok/s✅ Fits comfortably
FP166GB96GB23.57 tok/s✅ Fits comfortably

Suitable alternatives

AMD Instinct MI300X
192GB
923.44 tok/s
Price: —
NVIDIA H200 SXM 141GB
141GB
745.55 tok/s
Price: —
AMD Instinct MI300X
192GB
638.78 tok/s
Price: —
NVIDIA H100 SXM5 80GB
80GB
573.48 tok/s
Price: —
NVIDIA H200 SXM 141GB
141GB
540.74 tok/s
Price: —

More questions

Apple M2 Max specs & pricingFull guide for context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 speed on Apple M2 Maxcontext-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 Q4 requirements