Can I Run Meta Llama 3.1 8B Instruct on Apple Mac Studio M2 Ultra (192GB Unified Memory)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.1 8B Instruct (8B) on Apple Mac Studio M2 Ultra (192GB Unified Memory) (144 GB).
✓ Fits Comfortably in VRAM
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama3.1:8bLocal Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M2 Ultra (192GB Unified Memory)
Silent 144GB usable unified VRAM running 70B FP16 & 405B Q3
Cloud Instance Options for Meta Llama 3.1 8B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 16 GB | 2.15 GB | 19.46 GB (14%) | ~31 t/s | FITS |
| Q8_08.5 bpw | 8.48 GB | 2.15 GB | 11.68 GB (8%) | ~58 t/s | FITS |
| Q6_K6.56 bpw | 6.56 GB | 2.15 GB | 9.69 GB (7%) | ~73 t/s | FITS |
| Q5_K_M5.68 bpw | 5.68 GB | 2.15 GB | 8.78 GB (6%) | ~84 t/s | FITS |
| Q4_K_M4.8 bpw | 4.8 GB | 2.15 GB | 7.87 GB (5%) | ~97 t/s | FITS |
| FP44.25 bpw | 4.24 GB | 2.15 GB | 7.29 GB (5%) | ~109 t/s | FITS |
| IQ3_XS3.3 bpw | 3.28 GB | 2.15 GB | 6.29 GB (4%) | ~136 t/s | FITS |
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Frequently Asked Questions: Meta Llama 3.1 8B Instruct on Apple Mac Studio M2 Ultra (192GB Unified Memory)
How much VRAM does Meta Llama 3.1 8B Instruct need on Apple Mac Studio M2 Ultra (192GB Unified Memory)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 3.1 8B Instruct requires 7.87 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Meta Llama 3.1 8B Instruct achieve on Apple Mac Studio M2 Ultra (192GB Unified Memory)?
Based on Apple Mac Studio M2 Ultra (192GB Unified Memory)'s 800 GB/s memory bandwidth, expected decode speed is approximately 97 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.