For most of the last few years, "local LLM" meant either a Mac with too little memory or a workstation that cost as much as a small car. The mid-2026 hardware cycle has finally broken that logjam. Four boxes now sit between $2,000 and $10,000, each capable of running serious open-weight models on your own desk. The catch is that they require different software stacks and serve different workflows. The box that wins on paper often loses on your desk.
Who should care
| Buyer | Best choice | Why |
|---|---|---|
| Indie hacker, tight budget | Strix Halo ($2-3k) or used Mac Studio | Cheapest path to 128GB—but read the warnings first |
| AI researcher needing CUDA | DGX Spark ($4k) | Same stack as cloud; 128GB desktop box |
| Founder wanting one machine | M5 Max 128GB ($5k) | Daily driver + LLM box + travel computer |
| Corporate or model trainer | RTX 6000 Pro ($10k) | Only box here that trains 13B–70B models without complaint |
Our analysis
The most concrete data comes from independent practitioners running identical benchmarks on both the DGX Spark and Strix Halo. One tester, running both units side by side with the same models and inference suites, found that Strix Halo's Vulkan backend outperformed ROCm by roughly 17 percent in token generation—meaning buyers targeting serious workloads should install Vulkan via llama.cpp rather than the AMD-branded ROCm path.
That finding cuts both ways. Vulkan is faster, but the real-world throughput ceiling on Strix Halo lands around 180 GB/s—measurably below AMD's marketing figures. For token generation, that gap matters. One practitioner put it bluntly: the usable bandwidth is closer to a high-end mobile processor than a dedicated AI accelerator.
The trade-offs at each price point:
- DGX Spark ($4k):** Runs the same CUDA stack as every cloud provider. Any inference engine—vLLM, llama.cpp, TensorRT-LLM, SGLang—just works. This is the box the cloud crowd understands immediately.
- Strix Halo ($2-3k):** The cheapest 128GB all-in-one you can buy. The hardware price is aggressive. The software maturity is not. Real-world throughput is the constraint, and the CUDA door is closed.
- M5 Max ($5k):** A laptop first. MLX and GGUF are mature enough for most solo workloads. You give up CUDA; you gain portability and a machine that handles your full workday, not just inference.
- RTX 6000 Pro ($10k):** 96GB GDDR7 ECC VRAM, full PCIe, true workstation cooling. If you are doing LoRA, QLoRA, or supervised fine-tuning on 13B–70B models for work, the sticker is rounding error against engineering hours saved.
What to watch:** Strix Halo's software story is a work in progress. If AMD's drivers and tooling mature in the next driver cycle, the hardware pricing becomes genuinely disruptive. Until then, most buyers in the comments sections are holding off—used Mac Studios at similar price points are looking attractive again.
Bottom line
Most buyers will end up at either DGX Spark or M5 Max. DGX Spark wins if CUDA compatibility and inference performance are the job. M5 Max wins if you want one machine that does everything. Strix Halo is tempting on price and still risky in practice. Buy it only if the price gap is the entire decision.
Frequently asked questions
What is the cheapest 128GB box for local LLMs in 2026?
The AMD Strix Halo, found in the Framework desktop, HP Z2 Mini and some OEM mini-PCs, at roughly $2,000–$3,000.
Do I need CUDA for local LLMs?
Not strictly, but most mature tooling assumes CUDA. MLX on Apple Silicon and GGUF cross-platform runtimes are the main alternatives.
Can the M5 Max 128GB actually train models?
For small fine-tunes and experiments, yes. For serious training of 13B-plus models, no—that is the RTX 6000 Pro's job.
Why is Strix Halo controversial among testers?
Real-world throughput sits well below AMD's marketing bandwidth figure, and the software stack is less mature than NVIDIA's CUDA ecosystem.
Is DGX Spark the same as a cloud GPU?
In software terms, yes—it runs the same CUDA stack. In performance terms, no—it is a desktop box, not a data-center GPU.