← Can It Run? — GPU compatibility matrix · Local LLMs guide
DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The Mac Studio M4 Max 128GB has 128 GB.
🔍 DeepSeek Coder V2 33B (DeepSeek) is a 33B-parameter model for code generation and analysis. Top-tier code model, competitive with GPT-4 for programming. The question is whether the Mac Studio M4 Max 128GB’s 128 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | Mac Studio M4 Max 128GB |
|---|---|---|
| Q4_K_M (recommended) | 19.5 GB | ✅ |
| Q8_0 (near-lossless) | 34 GB | ✅ |
Expected performance
Running DeepSeek Coder V2 33B at Q4 on the Mac Studio M4 Max 128GB, expect roughly ~16 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 19.5 GB used of 128 GB leaves about 108.5 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the Mac Studio M4 Max 128GB
Architecture: Apple Silicon · VRAM: 128 GB · Price: ~$3,500.
Good: Unified 128GB runs 70B+ models silently at low power
Watch out: Slower raw throughput than NVIDIA; costly; no upgrades
Check current Mac Studio M4 Max 128GB price on Amazon →
Related checks
Other models on the Mac Studio M4 Max 128GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the Mac Studio M4 Max 128GB run DeepSeek Coder V2 33B?
Yes — runs at Q4 and full Q8 quality. DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4_K_M; the Mac Studio M4 Max 128GB has 128 GB.
How much VRAM does DeepSeek Coder V2 33B need?
DeepSeek Coder V2 33B needs about 19.5 GB at Q4_K_M (recommended) and 34 GB at Q8_0.
How many tokens per second will the Mac Studio M4 Max 128GB get on DeepSeek Coder V2 33B?
Roughly ~16 tokens/sec at Q4 in Ollama or llama.cpp.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.