Which Local Machine for Which Usage
The two tables above answer "what fits where." This section answers a different, more common question: given what you actually want to do, which of the fourteen configurations is the right one to buy. Same underlying data, organized by use case instead of by price.
| Usage | Recommended configuration(s) | Model class this targets | Why |
|---|---|---|---|
| Trying local inference cheaply, side project, budget under €2,000 | Mac mini M6 (32 GB) or RTX 5060 Ti 16 GB workstation | gpt-oss-20b, Qwen3.8-27B at a heavier quant | Cheapest entry point that runs a real current-generation model, not a toy fine-tune |
| Daily coding assistant, one model up to ≈30B, real context window | Mac mini M5 Pro (64 GB) or MacBook Pro M5 Pro (48 GB) | Qwen3.8-27B comfortable; Llama-4-Scout too tight | 48-64 GB gives one model room plus enough context to be useful, not just a benchmark pass |
| Mid-size MoE (≈100B total, ≈17B active) at good throughput | Dual RTX 5090 workstation (64 GB aggregate, with a backend that supports tensor splitting) or a 96-128 GB unified config (DGX Spark, Ryzen AI Halo, MacBook Pro M5 Max) | Llama-4-Scout-17B-16E | 55.6 GB of weights does not fit one 32 GB RTX 5090; 64 GB leaves limited runtime and context headroom, while 96-128 GB is safer |
| Frontier MoE (GLM-5.2, DeepSeek-V4-Pro), reduced but usable throughput accepted | Any workstation on this page with 512 GB+ system RAM added, running llama.cpp --n-cpu-moe or FreeToken | GLM-5.2, DeepSeek-V4-Pro-0813 | Full expert set stays resident in system RAM; only the per-token active experts stream to GPU, so VRAM stops being the hard limit |
| Largest model this page supports at full-accelerator-memory residency, no offload tricks | Mac Studio M5 Ultra (256 GB) or dual RTX PRO 6000 Blackwell (192 GB combined) | DeepSeek-V4-Flash-0731 fits within the available third-party weight estimates on both; Llama-4-Maverick-17B-128E fits only on the 256 GB config | ≈100-170 GB (third-party quantized estimates, no official figure) for DeepSeek-V4-Flash-0731; the upper estimate leaves limited runtime headroom on 192 GB. Llama-4-Maverick needs 205.7 GB and does not fit 192 GB |
| Local fine-tuning or training, not inference-only | RTX PRO 6000 Blackwell workstation (single or dual) | Depends on target model | Training needs VRAM headroom beyond weight residency for optimizer states and gradients, a cost this page's inference-only figures don't model |
| Sustained heavy or 24/7 production serving | Compare purchase, dedicated rental, and elastic rental | Exact production model and service level | Utilization, power, maintenance, availability, and the required GPU class determine the result; the hourly price alone does not |
mermaid
flowchart TD
A([What's the local usage?]) --> B{Trying it out, under €2,000?}
B -->|Yes| C([Mac mini M6 32GB or RTX 5060 Ti 16GB])
B -->|No| D{Daily driver, one model up to 30B?}
D -->|Yes| E([Mac mini M5 Pro 64GB or MacBook Pro M5 Pro])
D -->|No| F{Mid-size MoE, about 100B total?}
F -->|Yes| G([RTX 5090 workstation or 128GB unified])
F -->|No| H{Frontier MoE, reduced tok/s OK?}
H -->|Yes| I([Any workstation, 512GB+ RAM, CPU offload])
H -->|No| J{Full-residency flagship, up to 400B?}
J -->|Yes| K([Mac Studio M5 Ultra or dual RTX PRO 6000])
J -->|No| L{Training, not just inference?}
L -->|Yes| M([RTX PRO 6000 workstation, extra headroom])
L -->|No, 24/7 serving| N([Compare purchase and rental TCO])
style A fill:#F5E6D3,color:#333
style B fill:#E87E2F,color:#fff
style D fill:#E87E2F,color:#fff
style F fill:#E87E2F,color:#fff
style H fill:#E87E2F,color:#fff
style J fill:#E87E2F,color:#fff
style L fill:#E87E2F,color:#fff
style C fill:#7BC47F,color:#333
style E fill:#7BC47F,color:#333
style G fill:#7BC47F,color:#333
style I fill:#7BC47F,color:#333
style K fill:#7BC47F,color:#333
style M fill:#7BC47F,color:#333
style N fill:#6DB3F2,color:#fff
click C href "#fourteen-comparable-hardware-configurations" "Entry tier"
click E href "#fourteen-comparable-hardware-configurations" "Daily driver tier"
click G href "#what-actually-fits-named-models" "Mid-size MoE tier"
click I href "#what-actually-fits-named-models" "CPU-offload MoE serving"
click K href "#what-actually-fits-named-models" "Full-residency flagship tier"
click M href "#fourteen-comparable-hardware-configurations" "Training headroom"
click N href "#one-year-cost-projections" "Compare total cost"What's the local usage?
└─ Trying it out, under €2,000?
├─ Yes → Mac mini M6 32GB or RTX 5060 Ti 16GB
└─ No → Daily driver, one model up to 30B?
├─ Yes → Mac mini M5 Pro 64GB or MacBook Pro M5 Pro
└─ No → Mid-size MoE, about 100B total?
├─ Yes → RTX 5090 workstation or 128GB unified
└─ No → Frontier MoE, reduced tok/s OK?
├─ Yes → Any workstation, 512GB+ RAM, CPU offload
└─ No → Full-residency flagship, up to 400B?
├─ Yes → Mac Studio M5 Ultra or dual RTX PRO 6000
└─ No → Training, not just inference?
├─ Yes → RTX PRO 6000 workstation, extra VRAM headroom
└─ No, 24/7 serving → Compare purchase and rental TCO