Can I Run DeepSeek Coder V2 236B MoE on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running DeepSeek Coder V2 236B MoE (236B) on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) (110 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run deepseek-coder-v2:236bLocal Hardware Configurations & Pricing
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Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context
AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)
Cheapest 128GB unified memory box for loading 70B–109B models
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Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek Coder V2 236B MoE @ 151.34 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 472 GB | 4.03 GB | 493.3 GB (448%) | 0 t/s | OOM |
| Q8_08.5 bpw | 250.16 GB | 4.03 GB | 263.7 GB (240%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 193.52 GB | 4.03 GB | 205.07 GB (186%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 167.56 GB | 4.03 GB | 178.2 GB (162%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 141.6 GB | 4.03 GB | 151.34 GB (138%) | 0 t/s | OOM |
| FP44.25 bpw | 125.08 GB | 4.03 GB | 134.24 GB (122%) | ~3 t/s | PARTIAL |
| IQ3_XS3.3 bpw | 96.76 GB | 4.03 GB | 104.93 GB (95%) | ~19 t/s | FITS |
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Frequently Asked Questions: DeepSeek Coder V2 236B MoE on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)
How much VRAM does DeepSeek Coder V2 236B MoE need on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)?
At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek Coder V2 236B MoE requires 151.34 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will DeepSeek Coder V2 236B MoE achieve on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)?
Based on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)'s 256 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.