The best mini PCs for local AI inference in 2026

The most useful thing to know about local-AI mini PCs in 2026 is that the interesting ones mostly run the same chip. AMD's Ryzen AI Max+ 395 ("Strix Halo") with 128GB of unified LPDDR5X memory is the platform inside the Beelink GTR9 Pro, the GMKtec EVO-X2, the Framework Desktop, and HP's Z2 Mini G1a — same silicon, the same roughly 256GB/s of memory bandwidth, and therefore the same real-world model ceiling. What separates those boxes is price, networking, cooling, repairability, and support, not how large a model they can hold. So most of this guide is about matching one of them to how you actually work.

Two machines break the pattern and belong on any 2026 shortlist. NVIDIA's DGX Spark is the only unified-memory mini PC in this class with a full CUDA stack, and Apple's Mac Studio has more than double the memory bandwidth of the AMD boxes — which matters more than raw capacity for dense models.

One caveat colors every price below: 2026 is a bad year to buy memory. Contract DRAM prices climbed sharply early in the year as HBM for AI accelerators ate into commodity supply. That pushed up the DGX Spark and inflated every 128GB machine here. Treat the numbers as approximate and expect them to move; the soldered-LPDDR5X Strix Halo boxes have held up better than anything with socketed RAM.

Short version: for most people the Beelink GTR9 Pro is the best all-round 128GB Strix Halo box, and the GMKtec EVO-X2 is the cheapest sane way into that same 128GB tier. Spend up only if you need something specific — CUDA (DGX Spark), ECC and first-party support (HP Z2 Mini G1a), repairability (Framework Desktop), or Apple's bandwidth and MLX tooling (Mac Studio).

Quick picks:

• Best overall — Beelink GTR9 Pro
• Best value into 128GB — GMKtec EVO-X2
• Best supported / repairable — Framework Desktop
• Best first-party workstation — HP Z2 Mini G1a
• Best for CUDA and the biggest models — NVIDIA DGX Spark
• Best Apple option — Apple Mac Studio (M4 Max / M3 Ultra)
• Best sub-$1,400 starter — Minisforum AI X1 Pro-470 or Geekom A9 Max
• Best Intel alternative — ASUS NUC 14 Pro AI

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Beelink GTR9 Pro — Best overall#

The GTR9 Pro runs the AMD Ryzen AI Max+ 395, the chip that made local AI viable in this form factor. Its unified memory architecture is the point: CPU, GPU, and NPU all draw from the same pool of up to 128GB LPDDR5X, so a large model sits entirely in fast memory instead of being shuttled between VRAM and system RAM. What the GTR9 Pro adds over its identical-silicon rivals is the stuff you want in a machine that runs 24/7 — dual 10GbE networking and heavy-duty cooling that holds clocks under sustained load.

Be realistic about speed, because the marketing around this chip is not. Memory bandwidth (~256GB/s) is the ceiling, and it bites hardest on dense models. A dense 70B model at Q4 runs at low single-digit tokens per second here — usable for batch work, slow for interactive chat. Where the chip shines is smaller and sparse models: small dense models (7B–13B) run at tens of tokens per second, and 30B-class mixture-of-experts (MoE) models are dramatically faster still, because only a fraction of their weights are active per token. Even a large MoE like gpt-oss 120B stays usable rather than crawling. The dense-vs-MoE distinction is the single most important thing to understand before you buy — it matters more than any number on the box.

As of mid-2026, roughly $1,800–$2,000 for a 128GB / 2TB configuration (list price sits around $2,000, and the memory shortage keeps that floor moving). It's not cheap, but for mainstream-retail availability, strong I/O, and cooling built for always-on use, it's the box most people should default to.

Who it's for: Developers, researchers, and serious hobbyists who want an always-on 128GB local-AI machine with real networking, bought from a normal storefront.

Real trade-offs:

• Same ~256GB/s bandwidth and model ceiling as every other Strix Halo box — you're paying for I/O and cooling, not more capability
• Beelink's firmware and software support is adequate, not exceptional; the community fills the gaps
• Overkill if your daily models are 7B–13B — you'd be buying headroom you won't use

When to pick something else: If you want the same chip for less, the GMKtec EVO-X2 is the value play. If you want repairability and the strongest support story, the Framework Desktop.

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GMKtec EVO-X2 — Best value into 128GB#

The EVO-X2 is the cheapest credible way into the 128GB Strix Halo tier. Same Ryzen AI Max+ 395, same unified-memory advantage, and as of mid-2026 it runs roughly $1,700–$2,000 for a 128GB configuration — a few hundred dollars under the Beelink for the identical core capability. It's one of the most-recommended boxes in this class for exactly that reason.

Beyond the low price, its appeal is simply that it's a straightforward, widely available 128GB box — 2.5GbE networking and dual USB4 for fast external storage and displays. What it does not have is an OCuLink port for external-GPU expansion; that's a feature of GMKtec's pricier EVO-X3 tower (below), not this machine. Treat the EVO-X2 as the pure value play into 128GB, not as an eGPU platform.

One thing to skip: GMKtec also launched the EVO-X3, a vertical-tower machine built around the same Ryzen AI Max+ 395, at roughly double the EVO-X2's price. It does add an OCuLink port for external-GPU expansion — but despite the higher model number, it offers no more model ceiling. For pure local-AI capability the EVO-X2 is the better buy; the X3 is not the upgrade to wait for unless you specifically want that eGPU port.

Who it's for: Anyone who wants 128GB of Strix Halo for the lowest sane price.

Real trade-offs:

• GMKtec is a newer brand at this tier; the firmware/support track record is shorter than Beelink's or a first-party OEM's
• Networking and cooling are competent but not the GTR9 Pro's dual-10GbE-plus-serious-cooling setup
• Same dense-model bandwidth ceiling as the rest of the Strix Halo field

When to pick something else: If 24/7 networking and thermals matter, pay up for the Beelink. If you want ECC memory and enterprise support, the HP Z2 Mini G1a.

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Framework Desktop — Best supported / repairable#

Framework built the Strix Halo box for people who hate throwaway hardware. It's the same Ryzen AI Max+ 395 with up to 128GB LPDDR5X, but with Framework's repairability ethos and documentation, and it reviewed well in the tech press. In practice it runs Llama 3.3 70B and gpt-oss 120B locally like the rest of this tier — with the same bandwidth-bound caveats on dense models.

Pricing starts around $1,999 barebones for the 128GB board and lands closer to $2,800+ fully configured with a 1TB SSD, as of mid-2026. You're paying a modest premium over the cheapest EVO-X2 for a company with a genuine support-and-parts story.

Who it's for: Buyers who want the 128GB Strix Halo platform from a vendor with real documentation, replaceable parts, and a track record of standing behind hardware.

Real trade-offs:

• Costs more than the EVO-X2 for the same core silicon and model ceiling
• The memory is soldered LPDDR5X (a physical requirement of this chip), so "repairable" applies to storage, fans, and I/O — not the RAM
• Configured-with-SSD pricing climbs into HP Z2 Mini G1a territory; compare them directly at that point

When to pick something else: If price is the only axis, the EVO-X2. If you specifically need ECC memory and first-party workstation support, the HP Z2 Mini G1a.

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HP Z2 Mini G1a — Best first-party workstation#

This is the real answer to "I want a name-brand workstation, not a white-box mini PC." The Z2 Mini G1a runs the Ryzen AI Max+ PRO 395 with up to 128GB of LPDDR5x ECC memory and Radeon 8060S graphics, and reviewers have run gpt-oss 120B on it without a discrete GPU. As of mid-2026 the 2TB configuration runs roughly $3,300–$3,400.

This replaces a machine an earlier version of this guide called the "AMD Ryzen AI Halo Developer Platform" at $3,999 — we could not verify any first-party AMD product by that name and price, and the Z2 Mini G1a is the genuine high-end Strix Halo workstation. It costs less than that phantom figure, not more.

Who it's for: Teams and professionals who need ECC memory, first-party warranty and support, and the accountability of an HP workstation SKU.

Real trade-offs:

• Roughly double the EVO-X2 for the same Ryzen AI Max+ core and the same ~256GB/s ceiling; you're paying for ECC, support, and brand
• The PRO chip and ECC matter for reliability, not for raw inference speed
• If provenance and support aren't requirements, the cheaper Strix Halo boxes do the same work

When to pick something else: If you don't need ECC or enterprise support, any of the cheaper Strix Halo boxes. If you need CUDA, the DGX Spark.

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NVIDIA DGX Spark — Best for CUDA and the biggest models#

If NVIDIA's software stack is non-negotiable, the DGX Spark is the machine — and it's the flagship of this entire category, an omission in earlier versions of this guide. It pairs the GB10 Grace Blackwell superchip with 128GB of unified LPDDR5x and NVIDIA's stated ~1 petaFLOP of FP4 compute, runs the full CUDA stack, and NVIDIA markets it for models up to about 200B parameters.

The catch is price. It launched at around $4,000, and the top-storage Founders Edition SKU has been reported higher still on the back of the memory shortage. Several OEMs — including Asus, Dell, and MSI — ship the same platform under their own names, sometimes at lower storage for slightly less.

Who it's for: Developers and researchers whose workflows are built on CUDA, and anyone who wants to run the largest models on a first-party NVIDIA box without a data-center GPU.

Real trade-offs:

• The most expensive machine here, and the shortage moved it the wrong way
• CUDA and NVIDIA's ~1 PFLOP FP4 figure are the draw; like every unified-memory box in this class, dense-model token rates are still gated by memory bandwidth
• Overkill unless you specifically need CUDA or the ~200B-class headroom

When to pick something else: If you don't need CUDA, a 128GB Strix Halo box does the same local-LLM job for far less. If you want the highest memory bandwidth, look at Apple.

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Apple Mac Studio (and Mac Mini) — Best Apple option#

The list would be incomplete without Apple. The Mac Studio with an M4 Max takes up to 128GB of unified memory at roughly 546GB/s — more than double Strix Halo's ~256GB/s — and the M3 Ultra configuration goes up to 512GB of unified memory, far beyond anything else here. The cheaper Mac Mini is a legitimate entry point for smaller models. Apple's local-LLM tooling (MLX, LM Studio) is mature and well-supported.

Bandwidth is why Apple belongs in the conversation. On dense models memory is the bottleneck, and the M4 Max's lead shows up directly — on a 70B-class model it's measurably quicker than the low-single-digit rate you see on Strix Halo. That's not a landslide, but it's real, and the M3 Ultra's huge memory pool opens up models the 128GB boxes can't hold at all.

Pricing scales steeply with memory. A 128GB M4 Max Mac Studio sits in the same rough band as the high-end Strix Halo workstations; a maxed-out 512GB M3 Ultra is a five-figure machine. Treat Apple as the bandwidth-and-capacity play, not the value play.

Who it's for: People already in macOS, anyone who wants the highest memory bandwidth for dense-model inference, and researchers who need more than 128GB of unified memory (M3 Ultra).

Real trade-offs:

• No CUDA — some frameworks and models are CUDA-first and need porting or run through MLX/Metal
• The big-memory configs are expensive, and the shortage hasn't spared Apple
• macOS-only; if your stack assumes Linux/Windows, factor the switch

When to pick something else: If your tooling is CUDA-bound, the DGX Spark. If you want the best price-per-128GB, a Strix Halo box.

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Minisforum AI X1 Pro-470 — Best sub-$1,400 starter#

The AI X1 Pro-470 is built on AMD's Ryzen AI 9 HX 470 (Gorgon Point, a Zen 5 refresh of Strix Point) in a 32GB configuration, at roughly $1,300–1,400 as of mid-2026 (a figure the shortage could nudge upward). It's the machine for serious local-AI dabbling without crossing into the 128GB tier.

32GB is the practical floor for local AI in 2026. You can run 7B and 13B models comfortably, push into 30B with quantization, and handle most agentic workflows — but you can't load 70B-class models, dense or MoE, the way the Strix Halo boxes can.

One underrated bonus at this price: it includes an OCuLink port, so you can attach an external NVIDIA GPU for CUDA workloads later — the eGPU escape hatch that pricier boxes charge more for.

Who it's for: Developers getting started with local inference, and power users whose daily models are sub-30B, who want AMD's Gorgon Point architecture without paying Strix Halo prices.

Real trade-offs:

• 32GB is the minimum — you'll feel the ceiling as you push past ~30B models
• Its integrated GPU has meaningfully less compute than the Max+ 395; large-model inference speed reflects that
• Minisforum build quality is good but not premium; thermals under sustained AI load deserve attention

When to pick something else: If you regularly need 70B-class models at usable speeds, save for a 128GB Strix Halo box. The gap here is both capacity and bandwidth.

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Geekom A9 Max — Best budget entry point#

At roughly $1,150–1,200 as of mid-2026, the Geekom A9 Max slots in just below the AI X1 Pro-470 in price but uses the AMD Ryzen AI 9 HX 370 — an earlier Strix Point chip — with 32GB DDR5 and a 1TB SSD included in the base price.

The HX 370 is a capable chip for local AI. It handles 7B–13B models well and is a legitimate starting point for anyone who wants local inference without a four-figure commitment on the CPU alone.

Who it's for: Budget-first buyers who want AMD's Ryzen AI architecture and a complete out-of-the-box package (SSD included) without stretching to the newer Gorgon Point or Max+ 395 tiers.

Real trade-offs:

• The HX 370 is an earlier Strix Point chip — the AI X1 Pro-470's newer silicon has meaningfully better AI throughput
• 32GB caps your model ceiling the same as the AI X1 Pro-470, with less headroom in inference speed
• Geekom's software ecosystem is thin; plan to bring your own stack

When to pick something else: If you can stretch to the AI X1 Pro-470, its newer silicon is the better investment for 2026 workloads. The A9 Max is for buyers where the price gap genuinely matters.

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ASUS NUC 14 Pro AI — Best Intel alternative#

The NUC 14 Pro AI runs Intel's Core Ultra Series 2 (Lunar Lake) platform with a dedicated NPU, and ships with 32GB LPDDR5X. Intel rates the platform high on "TOPS" — the NPU alone at up to around 48 TOPS, with the Intel Arc GPU pushing the total well higher — but, as this guide argues throughout, treat TOPS as marketing; memory capacity is what decides which models you can run.

Intel's Lunar Lake is a legitimate local-AI architecture, and the NUC brand carries real ecosystem credibility — ASUS's support and build quality are a step above most white-box mini-PC makers. The trade-off is a lower unified-memory ceiling than AMD's Strix Halo platform, which matters the moment you want to load larger models in their entirety.

Who it's for: Users already invested in the Intel/Windows AI ecosystem, buyers who prioritize build quality and manufacturer support over raw model-size headroom, and anyone for whom Windows-on-Intel compatibility is a workflow requirement.

Real trade-offs:

• 32GB memory ceiling is a real constraint vs. AMD's 128GB Strix Halo options
• Intel Arc GPU inference trails AMD's integrated GPU in the Max+ 395 for LLM workloads at this tier
• Pricing is competitive but varies by config; you're buying into the smaller-model tier either way

When to pick something else: If running 30B+ models is a current or near-term goal, the AMD Strix Halo options are the better platform. The NUC 14 Pro AI is the right call when Intel compatibility, support, and brand trust outweigh raw AI memory headroom.

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Comparison#

Dashes mark figures we couldn't verify to a specific number; don't read them as low.

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How to choose#

1. Capacity sets the ceiling; bandwidth sets the speed. Marketing loves TOPS. What determines which models you can run is unified memory capacity: 32GB handles 7B–13B (and 30B quantized); 128GB is the target for 70B-class work. But capacity isn't speed — memory bandwidth is. That's why Apple's M4 Max (~546GB/s) is faster on dense models than any Strix Halo box (~256GB/s) even at the same 128GB. Decide capacity first, then bandwidth.

2. Understand MoE vs. dense — it's the biggest performance surprise. The same 128GB Strix Halo machine runs a dense 70B model at low single-digit tokens per second, but a 30B-class MoE runs several times faster, and even a large MoE like gpt-oss 120B stays usable. Sparse (MoE) models activate only a fraction of their weights per token, so they fly on these bandwidth-limited boxes while dense models of similar size crawl. If your target models are MoE, these machines are far better than the dense-70B numbers suggest — and the reverse is just as true.

3. The Ryzen AI Max+ 395 is the x86 value pick, not the outright best. For price-per-128GB on a Windows/Linux box, Strix Halo is the chip to beat in 2026 — it's in most machines here for good reason. But "best overall" depends on the axis: Apple's M4 Max has more than double the memory bandwidth, and NVIDIA's GB10 (DGX Spark) adds CUDA and far more compute. Strix Halo wins on value and availability, not on every metric.

4. Factor the 2026 memory shortage. DRAM contract prices jumped sharply in early 2026, and it shows: the DGX Spark's price rose and every 128GB box got more expensive. Soldered-LPDDR5X machines (all the Strix Halo boxes) weathered it better than socketed-RAM systems. Every price here is approximate and moving — check current listings, and don't assume last quarter's number holds.

5. Know your model ceiling before you buy. If your real workflow is an 8B coding assistant, you don't need an ~$1,800 machine — a 32GB Minisforum or Geekom is fine. If you want to run 70B-class or large-MoE models, you need a 128GB box. And if you need CUDA or ECC, that decides the machine before price does. Be honest about which tier you actually need.

6. Consider the eGPU escape hatch. Boxes with an OCuLink port — the budget Minisforum AI X1 Pro-470, or GMKtec's pricier EVO-X3 tower — let you attach an external NVIDIA GPU for CUDA acceleration later. If you might need CUDA but don't want to buy a DGX Spark today, that expansion path is a real hedge. (The popular GMKtec EVO-X2 does not have OCuLink — a common mix-up.)

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Our picks#

🏆 Top pick — Beelink GTR9 Pro (best overall). Ryzen AI Max+ 395 with up to 128GB unified LPDDR5X (~256GB/s) at roughly $1,800–2,000 as of mid-2026 — the best-rounded always-on 128GB Strix Halo box, with dual 10GbE and heavy-duty cooling.