Best Mini PCs for Running Coding Agents Locally (2026): Mac mini vs Strix Halo vs DGX Spark

Running a coding agent locally comes down to three things: how much memory the box has (which determines which models fit), how fast that memory moves data (which determines token throughput), and whether the box can sustain that throughput for hours without throttling. Agents run long sessions, not quick bursts, so steady output matters more than a peak you cannot hold.

This article compares three hardware paths for local LLM workloads in 2026: the Apple Mac mini (M5 Pro), Strix Halo mini PCs built on the AMD Ryzen AI Max+ 395, and the Nvidia DGX Spark 64 GB. It also covers how Directionally Local's Autopilot and Hotcell stack runs on each.

#Quick comparison

Prices and availability change. Verify before purchasing.

#Mac mini (M5 Pro)

Apple's current Mac mini lineup includes three M6 models and one M5 Pro. For coding agent workloads, the M5 Pro is the relevant option.

Per Apple's specs page, the M5 Pro ships with 24 GB of unified memory, configurable to 48 GB or 64 GB. Memory bandwidth is 307 GB/s. The chip has a 15-core CPU and 16-core GPU, upgradeable to 18-core CPU and 20-core GPU. Peak continuous power draw is 155 W. The unit measures 5.0 x 5.0 x 2.0 inches.

Unified memory means the CPU and GPU share the same pool: no separate VRAM and no offloading layers. A model that fits in 48 GB or 64 GB runs fully on-chip.

The M6 base models run at 153 GB/s bandwidth with a 32 GB ceiling, or 170 GB/s with a 32 GB ceiling on the higher M6 configuration. Those figures make them workable for smaller models but limiting for anything above 7B at full precision or 13B at aggressive quantisation.

Trade-offs. The M5 Pro is a fixed-capacity purchase: you choose 24, 48 or 64 GB when you order, with no upgrade path later. macOS is the only OS. Apple does not publish prices on the specs page, so verify pricing directly before budgeting.

#Strix Halo mini PCs

"Strix Halo" is AMD's name for the Ryzen AI Max+ 395 chip, not a single product. GMKtec, Framework, Beelink, and Minisforum all build mini PCs around it. Specs and prices vary by vendor.

Per AMD's product page, the Ryzen AI Max+ 395 has 16 Zen 5 cores, 32 threads, a boost clock up to 5.1 GHz, and a default TDP of 55 W, configurable between 45 W and 120 W. The integrated GPU is a Radeon 8060S with 40 RDNA 3.5 compute units. The XDNA 2 NPU is rated at 50 TOPS.

The GMKtec EVO-X2 is a concrete example. It uses soldered LPDDR5X-8000 memory in 64 GB or 128 GB configurations, runs at 120 W sustained with a 140 W peak, and was listed at USD 2,199.99 (regular USD 2,599.99) when checked in October 2026. Check the current listing before purchasing, prices vary by configuration and retailer.

Strix Halo uses a 256-bit LPDDR5X-8000 memory bus, which works out to about 256 GB/s of bandwidth: a little below the Mac mini M5 Pro and the DGX Spark. During the 2026 memory shortage, 128 GB configurations have often sold for around USD 3,500, so check the price of the exact configuration you want.

A Tom's Hardware review published 4 February 2026 noted that fan noise on the EVO-X2 can be noticeable under sustained load and flagged the price as high for what the hardware delivers. Worth weighing if the box will sit on a desk rather than in a rack.

The 128 GB configuration is the main reason to look at Strix Halo. That headroom supports larger models or parallel agent workloads without hitting a memory ceiling. The memory is soldered, so the capacity you buy is the capacity you keep.

Upcoming: ServeTheHome reported in October 2026 that AMD "Gorgon Halo" systems with up to 192 GB were arriving around that time. Those were not confirmed specs at time of writing, but worth watching if you are not in a hurry.

Trade-offs. Strix Halo boxes run Linux or Windows, an advantage for the Linux-native parts of the Directionally Local stack. Fan noise under sustained load is a documented concern. Memory is non-upgradeable. The 64 GB option exists, but the 128 GB configuration is where the platform's advantage over the Mac mini becomes clear.

#DGX Spark 64 GB

The Nvidia DGX Spark 64 GB became available on 23 October 2026, sold through Acer, ASUS, Dell, Gigabyte, HP, and MSI. It is not sold as a direct Nvidia-branded box. Starting price is USD 4,999, per reporting from ServeTheHome and Futurum Group.

Per Nvidia's DGX Spark spec page, the 64 GB model has 273 GB/s memory bandwidth and draws 240 W. It includes ConnectX-7 200 GbE networking, matching the 128 GB model. Storage is either 1 TB or 4 TB; ServeTheHome could not confirm at launch which tier the USD 4,999 price covers, verify with the OEM before ordering.

The 128 GB DGX Spark launched roughly a year earlier at USD 3,999. ServeTheHome reports that model is now selling for around USD 6,950, with OEM prices varying. Nvidia says two 64 GB units can be clustered to reach 128 GB of total memory for around USD 8,000, though ServeTheHome generally recommends one 128 GB node over two 64 GB nodes for that use case.

The 64 GB box is only days old as of this writing. Street prices may move as OEM stock settles.

ServeTheHome noted that USD 6,950 is close to three years of a USD 200/month cloud subscription, but with a cloud subscription, your data goes to a third party. That is their framing, not ours, but it is a useful lens for the buy-versus-subscribe calculation.

Trade-offs. At 240 W, the DGX Spark draws significantly more power than the other two options. Its 64 GB memory ceiling matches the Mac mini M5 Pro at maximum configuration, so the 128 GB model is the more natural comparison for teams that need headroom. The 64 GB model's case over the Mac mini rests on discrete GPU architecture and ConnectX-7 networking, relevant if you plan to cluster units or run inference workloads that benefit from GPU-specific compute paths. The price premium over Strix Halo boxes is real.

#What fits in the memory

Memory capacity sets a hard ceiling. A model that does not fit cannot run at all; one that barely fits may leave no room for the agent's context window or parallel workloads.

As a rough guide: 64 GB fits a 70B-parameter model at 4-bit quantisation, or a smaller model at higher precision. 128 GB opens up larger models or lets you run multiple smaller models simultaneously. These are general capacity observations, not benchmarks from testing on these specific boxes.

Quantisation is the main tool for fitting larger models into a given memory budget, but lower bit-width quantisation reduces quality on hard tasks. Local open-weight models are already weaker than frontier models on complex reasoning, aggressive quantisation narrows that gap further. A fixed-capacity box means you make that trade-off once, at purchase time.

#Running Autopilot and Hotcell on each box

Autopilot and Hotcell are our products.

Hotcell is the sandbox runtime. On macOS (Mac mini), it uses Apple Virtualization for per-sandbox isolation. On Linux (Strix Halo and DGX Spark), it uses Firecracker microVMs, each sandbox gets its own kernel with low overhead. Docker is supported on all three platforms.

Autopilot is the coding agent. It supports 25+ model providers, including Ollama and LM Studio for local models. Point it at whichever model is running on the box.

Default-deny egress is kernel-enforced on Linux and inside Firecracker microVMs. On macOS, isolation runs through Apple Virtualization. In both cases, code and API keys never leave the machine. The blast radius of a misbehaving agent is contained to its sandbox.

Strix Halo and DGX Spark boxes run Linux natively, which means Firecracker-based isolation and the full Linux toolchain without a compatibility layer. The Mac mini runs macOS, which uses a different isolation path but is equally supported.

Directionally Local installs both as one package:

npm install -g directionallylocal
dl

dl asks where agents should run. A Linux box with Ubuntu 24.04 or 22.04, or Debian 12, can be your own server over SSH, so tasks keep going while your laptop is closed. On a Mac mini, run dl on the Mac itself and pick this computer; tasks pause while it sleeps. dl works with OpenRouter, DeepSeek, OpenAI, Ollama and other providers. You pick one during setup. More in the install docs, and on Autopilot and Hotcell.

#How to choose

• Mac mini M5 Pro: compact, quiet, macOS, 64 GB ceiling. The right call if that memory budget fits your models and you prefer Apple's unified memory architecture.
• Strix Halo box: 128 GB, Linux-native, louder under load. The GMKtec EVO-X2 is one option; Framework, Beelink, and Minisforum also make Strix Halo boxes. Check each vendor's current specs and pricing.
• DGX Spark 64 GB: Nvidia GPU architecture, ConnectX-7 networking, 240 W draw, USD 4,999 entry. Worth considering if the GPU compute path matters and 64 GB is enough. Wait for street prices to stabilise before ordering.
• DGX Spark 128 GB: if you need Nvidia's architecture and memory headroom, this is the more natural choice. ServeTheHome recommends one 128 GB node over two clustered 64 GB units.
• Gorgon Halo: if 192 GB in a mini PC form factor is relevant to your workload, ServeTheHome reported those systems were arriving in October 2026. Worth watching before committing.

We're also building our own edge hardware for coding agents. More on that soon; see the plan.

#FAQs

What is the best mini PC for local LLM workloads in 2026? It depends on your memory requirement. For 64 GB, the Mac mini M5 Pro and DGX Spark 64 GB are both options with different architectures and price points. For 128 GB, Strix Halo boxes like the GMKtec EVO-X2 or the DGX Spark 128 GB are the main choices.

Can I run a coding agent like Autopilot on a Mac mini? Yes. Autopilot runs on macOS. Hotcell uses Apple Virtualization for sandbox isolation. Point Autopilot at a local model running via Ollama or LM Studio, and code and keys stay on the machine.

How much memory do I need to run a 70B model locally? As a general capacity guide, a 70B-parameter model at 4-bit quantisation fits in roughly 40–48 GB. 64 GB gives you room for the model plus agent context. 128 GB gives more flexibility on quantisation level or lets you run multiple models simultaneously.

Is the DGX Spark 64 GB worth buying over the 128 GB model? The 64 GB model starts at USD 4,999 and became available in late October 2026. The 128 GB model is now selling for around USD 6,950 per OEM pricing reported by ServeTheHome. If 64 GB fits your models, the lower entry price is real. If you need more headroom, ServeTheHome recommends one 128 GB node over two clustered 64 GB units.

Does Hotcell run on Linux mini PCs like Strix Halo boxes? Yes. On Linux, Hotcell uses Firecracker microVMs for per-sandbox isolation with kernel-level separation. Default-deny egress is kernel-enforced. Strix Halo boxes and the DGX Spark both run Linux, so you get the full Firecracker-based isolation path.

What is the difference between the Mac mini M5 Pro and M6 models for local AI? The M5 Pro tops out at 64 GB unified memory with 307 GB/s bandwidth. The M6 models max out at 32 GB with 153 or 170 GB/s bandwidth depending on configuration. For larger models, the M5 Pro is the relevant option. The M6 base models work for smaller models or lighter agent workloads.

Are Strix Halo mini PC specs the same across all vendors? No. The AMD Ryzen AI Max+ 395 chip is consistent, but memory configuration, power limits, cooling, and price vary by vendor. GMKtec, Framework, Beelink, and Minisforum all make Strix Halo boxes with different trade-offs. Check each vendor's current specs and pricing before deciding.