Can I Run Mistral Large 2 (123B Instruct) on Apple MacBook Pro/Air (16GB Unified Memory)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Mistral Large 2 (123B Instruct) (123B) on Apple MacBook Pro/Air (16GB Unified Memory) (14 GB).
✕ Out of Memory — Exceeds VRAM Capacity
Local Hardware Configurations & Pricing
Out of VRAM: High-Capacity Hardware Upgrades2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
Apple MacBook Pro/Air (16GB Unified Memory)
Daily driver running 7B–14B models with 32K context comfortably
Apple Mac Mini M4 Pro (64GB Unified Memory)
Silent 65W desktop running 32B–70B models with large KV context
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Mistral Large 2 123B & Llama 3.3 70B Q8_0 locally with 128K context
Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Mistral Large 2 (123B Instruct) @ 83.04 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 246 GB | 5.91 GB | 261.27 GB (999%) | 0 t/s | OOM |
| Q8_08.5 bpw | 130.38 GB | 5.91 GB | 141.6 GB (999%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 100.86 GB | 5.91 GB | 111.05 GB (793%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 87.33 GB | 5.91 GB | 97.05 GB (693%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 73.8 GB | 5.91 GB | 83.04 GB (593%) | 0 t/s | OOM |
| FP44.25 bpw | 65.19 GB | 5.91 GB | 74.13 GB (530%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 50.43 GB | 5.91 GB | 58.86 GB (420%) | 0 t/s | OOM |
Export Full Architecture & Infrastructure Spec
Download an instant Markdown architecture spec with exact memory math, hardware benchmarks, and runtime CLI flags.
Frequently Asked Questions: Mistral Large 2 (123B Instruct) on Apple MacBook Pro/Air (16GB Unified Memory)
How much VRAM does Mistral Large 2 (123B Instruct) need on Apple MacBook Pro/Air (16GB Unified Memory)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Mistral Large 2 (123B Instruct) requires 83.04 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Mistral Large 2 (123B Instruct) achieve on Apple MacBook Pro/Air (16GB Unified Memory)?
Based on Apple MacBook Pro/Air (16GB Unified Memory)'s 100 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.