At Computex 2026, Jensen Huang put a new chip on a slide and called it the start of the PC's next chapter. RTX Spark pairs a 20-core Grace CPU with a Blackwell GPU and up to 128 GB of unified memory, soldered into laptops and compact desktops from Dell, HP, Microsoft, Lenovo, MSI, and ASUS. The headline number: 1 petaflop of AI compute, enough to run a 120-billion-parameter LLM locally with a million tokens of context. The first laptops ship October 16, 2026, from $2,599.99, and the compact desktops follow in November. For a local-AI desk today, you can buy a Mac Studio or a Strix Halo mini PC, neither with an NVIDIA logo on it.
Apple Mac Studio (M5 Max, 64 GB or 128 GB)
M5 Max · up to 128 GB unified · up to 614 GB/s · from $2,499 with 36 GB
Apple's unified memory treats GPU and CPU as one pool. At up to 614 GB/s on M5 Max and 1.2 TB/s on M5 Ultra, per Apple, that pool holds models too big for any consumer NVIDIA card. Configure 64 GB or more for Llama 3.3 70B at 4-bit, a 42.5 GB file, and the M5 Ultra goes to 512 GB.
- Fastest memory of the four machines here
- Silent under load. Sits on a desk and disappears
- Ollama, llama.cpp, MLX, and LM Studio all have first-class M-series support
- The Mac Studio has been the r/LocalLLaMA community's default recommendation for two years
- macOS only. No native CUDA, no Windows AI tooling
- Amazon lists only the 36 GB base, so the 64 GB and 128 GB configs are Apple orders
- For image and video diffusion workloads, NVIDIA's CUDA ecosystem still wins on throughput
The Mac Studio is the machine local-AI hobbyists tell new buyers to start with: install Ollama and a 70B model can be answering you the same afternoon. Apple replaced the M4 Max model with the M5 Max on August 25, 2026, shipping September 22, and the outgoing M4 Max with 64 GB now lists for about $4,299 through Amazon resellers, more than a new M5 Max. Configure 64 GB for 70B models at 4-bit, or 128 GB for higher precision or several models at once. Amazon carries only the 36 GB base, so this link goes to Apple.
GMKtec EVO-X2 AI Mini PC (128 GB)
Ryzen AI Max+ 395 · 128 GB LPDDR5X-8000 · 2 TB SSD · 54-140 W
If RTX Spark is the future of unified-memory AI PCs, the GMKtec EVO-X2 is what that future looks like with an AMD logo on it and a shipping date of last quarter. The Ryzen AI Max+ 395 pairs 16 Zen 5 cores with a 40-CU Radeon GPU and 128 GB of LPDDR5X-8000 unified memory soldered to the board. AMD made the same architectural bet NVIDIA just announced. The EVO-X2 packages it in a 35 dB box you can fit on a bookshelf.
- 128 GB of unified memory you can buy today
- Quad 8K display output, USB4, WiFi 7
- 35 dB at idle in Quiet Mode; sustained 50+ TOPS XDNA 2 NPU for agentic loads
- Ships now, no fall-2026 wait
- Strix Halo memory bandwidth sits close to RTX Spark's reported figure, well below the M5 Ultra's 1.2 TB/s
- ROCm on Windows is less mature than CUDA for AI tooling
- Performance Mode at 140 W spins the fans audibly louder than the 35 dB Quiet Mode rating
The EVO-X2 is the answer to 'what if I want RTX Spark but I want it now, and I don't need a laptop.' You get the unified-memory architecture, you get the 128 GB ceiling, you skip the wait. The Ryzen AI Max+ 395 is the same chip Framework puts in its Desktop (sold direct from frame.work, no Amazon SKU), so the category exists today. ROCm is improving; for local LLM inference via llama.cpp or LM Studio's ROCm path, the EVO-X2 runs 70B models. If you want CUDA specifically, this isn't the machine.
Beelink GTR9 Pro (128 GB, dual 10 GbE)
Ryzen AI Max+ 395 · 128 GB LPDDR5X · 2 TB SSD · dual 10 GbE · USB4 ×2
The Beelink GTR9 Pro takes the same Ryzen AI Max+ 395 chip as the GMKtec and adds dual 10 GbE Ethernet. A local AI lab eventually wants fast NAS access for models and datasets, and the 10 GbE covers it.
- 4.2 stars across 290 ratings, the deepest review base of the Strix Halo picks here
- Dual 10 GbE for NAS/SAN AI workflows
- Beelink markets DeepSeek 70B support out of the box
- Two USB4 ports (40 Gbps) for external storage or eGPU experiments
- White cube aesthetic. Looks like a smart speaker on a desk
- Beelink's first revision of a new chip historically ships with BIOS quirks that need a firmware update or two to settle
- Same ROCm-vs-CUDA tradeoff as the GMKtec
The GTR9 Pro is the pick if you've already decided on Strix Halo and you need fast networking. It costs about $700 more than the GMKtec EVO-X2 for the same 128 GB / 2 TB config, and the dual 10 GbE is what the extra buys: if you run a local AI lab with a Synology, a TrueNAS box, or a second machine for batch processing, 10 GbE between them changes what's possible. Single-user inference on the GTR9 Pro is identical to the EVO-X2 because they're the same silicon.
Apple Mac mini (M5 Pro, 64 GB)
M5 Pro · up to 64 GB unified · 307 GB/s · from $1,699 with 24 GB
The M5 Pro Mac mini is the cheapest Apple route to 64 GB of unified memory. Apple replaced the M4 Pro model on August 25; the M5 Pro starts at $1,699 with 24 GB, and you configure 64 GB at Apple for Llama 3.3 70B at 4-bit. It won't run 120B models.
- The cheapest Apple machine that takes 64 GB of unified memory
- Same software ecosystem as Mac Studio (Ollama, MLX, LM Studio)
- Small enough to sit behind a monitor
- 307 GB/s is half the M5 Max's memory bandwidth
- 64 GB is the chip ceiling; 128 GB means a Mac Studio
- Amazon lists only the 24 GB version, so the 64 GB config is an Apple order
If your goal is to learn local AI without Mac Studio money, the Mac mini is the entry point. The 64 GB config runs Llama 3.3 70B at 4-bit, Qwen 3 32B and smaller models, and it does so quietly. Its 307 GB/s bandwidth slows token generation on bigger models, so it suits one-at-a-time chat better than batch agent workflows. If you outgrow it, the Mac Studio runs the same software.
The numbers.
| Mac Studio M5 Max | GMKtec EVO-X2 | Beelink GTR9 Pro | Mac mini M5 Pro | |
|---|---|---|---|---|
| Architecture | Apple unified | AMD unified | AMD unified | Apple unified |
| Memory for AI | 64-128 GB | 128 GB | 128 GB | 64 GB |
| Memory bandwidth | 614 GB/s | ~256 GB/s | ~256 GB/s | 307 GB/s |
| Native CUDA | No | No | No | No |
| OS | macOS | Windows / Linux | Windows / Linux | macOS |
| Form factor | Desktop | Mini PC | Mini PC | Mini desktop |
| Price | from $2,499 | ~$3,650 | ~$4,349 | from $1,699 |
Other strong options.
NVIDIA DGX Spark (GB10 Grace Blackwell)
NVIDIA's reference local-AI workstation, shipping at $4,699 from NVIDIA's marketplace after a February 2026 price rise. GB10 Grace Blackwell Superchip with 128 GB of unified memory, also sold by ASUS, Dell, and HP; Amazon has only third-party resellers. Same Spark family name as the new RTX Spark consumer chip, different target buyer: data scientists who want NVIDIA's stack on a desk now, rather than Windows AI agent users waiting for fall. We list it as an editorial mention, with no affiliate link.
Framework Desktop (Ryzen AI Max+ 395)
Framework's first desktop sells direct from frame.work. Same Strix Halo silicon as the GMKtec EVO-X2 and Beelink GTR9 Pro, in Framework's signature open-hardware chassis with full repairability scores. Worth knowing about for the open-platform crowd that values long-term parts availability over price. Currently no Amazon SKU.
The buying guide.
Pick I, the LLM enthusiast
You mostly want to run 70B-class LLMs locally and you don't need Windows. Buy the Mac Studio M5 Max with 64 GB, or 128 GB if you want higher precision or several models loaded at once. The Mac Studio has been the r/LocalLLaMA default for two years, and the M5 Max shipped September 22, 2026.
Pick II, the Windows-native AI early adopter
You want unified-memory AI on Windows or Linux on your desk now, and RTX Spark desktops don't go on sale until November. Buy the GMKtec EVO-X2 or Beelink GTR9 Pro. Identical silicon, identical inference performance. The GMKtec costs about $700 less; the Beelink adds dual 10 GbE LAN. Either way you're betting on AMD's ROCm software stack maturing through 2026, which it is.
Pick III, the curious beginner
You want into the local AI hobby without a Mac Studio commitment. Buy the Mac mini M5 Pro with 64 GB. Its 307 GB/s bandwidth ceiling caps growth, but at this price the eventual upgrade to a Mac Studio is part of the plan. Same software ecosystem on the way up: no relearning when you move to the bigger machine.
For diffusion workloads, build your own
None of the four picks above suit image and video diffusion (Stable Diffusion 3, FLUX, Wan 2.2, Hunyuan Video). CUDA on an RTX 5090 still wins on those workloads per dollar, but there's no Amazon prebuild we'd recommend on its merits; the brand options at this tier are uneven. Build your own tower around an RTX 5090 instead. The GPU Buying Guide below covers current cards and pairing; the CPU and motherboard guides cover the rest of the parts list.
What to skip while waiting for RTX Spark
Skip any unified-memory machine under 64 GB; you'll outgrow it before RTX Spark ships and you'll regret the spend. Skip RTX 4070 / 4080 / 5070 / 5080 cards for LLM-focused builds: they're slower at inference per dollar than a Mac Mini and the VRAM ceiling is no better. Skip Qualcomm Snapdragon X Elite laptops marketed as 'AI PCs'; the 45 TOPS NPU is for Windows Recall, not for running 70B local models. Skip no-name prebuilt RTX 5090 towers on Amazon; the assembly quality is uneven and the brand support is thin.
FAQ.
Three different machines whose names overlap on purpose. DGX Spark (already shipping) is a $4,699 desktop AI workstation built around NVIDIA's GB10 Grace Blackwell Superchip. Sold by NVIDIA, ASUS, Dell, and HP direct. Single-user, 128 GB unified memory. Targets data scientists. RTX Spark (laptops ship October 16, 2026) is the consumer-PC version of the same idea: 20-core Grace CPU plus Blackwell GPU plus up to 128 GB unified, in laptops and compact desktops from Dell, HP, Lenovo, Microsoft, ASUS, MSI. Targets creators, developers, and Windows AI agent users. DGX Station for Windows (also announced at Computex 2026) is an enterprise deskside supercomputer for trillion-parameter models, bigger than both Sparks, targets dev teams. NVIDIA reused 'Spark' twice for different audiences.
For pure token-generation speed: yes. The M5 Ultra Mac Studio runs at 1.2 TB/s of memory bandwidth, per Apple. Press estimates put RTX Spark closer to 300 GB/s, which sits in Strix Halo territory. Bandwidth governs how fast tokens stream out of a loaded model. For raw AI compute (training, fine-tuning, image and video diffusion), RTX Spark's Blackwell tensor cores at FP4 will beat Apple Silicon on per-operation throughput. The answer depends on workload. Inference-heavy: Mac stays ahead. Compute-heavy: NVIDIA pulls ahead.
Yes, with caveats. RTX 4090 has 24 GB VRAM. That fits any 13B-class model at full precision, 30B-class at Q4, and Mixtral 8x7B at Q4. Llama 3.3 70B fits at Q2 with quality compromises, or at Q4 with partial CPU offloading. Image and video diffusion workloads (Stable Diffusion 3, FLUX, Wan, Hunyuan) all fit cleanly in 24 GB. The 24 GB ceiling does mean newer 70B reasoning models at FP8 or higher won't fit; you quantize aggressively or wait for newer cards. Otherwise: install Ollama or LM Studio, point it at a model file, and you're running local AI tonight.
Preorders opened October 7, 2026. Prices start at $2,599.99 for the Microsoft Surface Laptop Ultra and the ASUS ProArt P14, both with 24 GB and 512 GB, and the first units ship October 16. The 128 GB configurations run from $5,309 for the HP OmniBook Ultra 16, per Tom's Hardware, to $6,999.99 for the ASUS ProArt P16 with 2 TB. Our RTX Spark laptops guide lists every model and price.
It depends on how much local AI you do today. If you run local LLMs daily for work and have outgrown your current machine, preorder an RTX Spark laptop, which ships October 16 from $2,599.99, or buy one of the four above. If you've never run local AI and aren't sure you'd use it weekly, wait for reviews of retail units, due in the weeks after October 16. The curious beginner pick (the Mac mini M5 Pro, from $1,699) is the cheapest way to find out if you'd use local AI, with a full refund window if you decide you wouldn't.
For most LLM users: the Mac Studio M5 Max.
For unified-memory AI on Windows now: the GMKtec EVO-X2 or Beelink GTR9 Pro.
For the curious beginner: the Mac mini M5 Pro.
NVIDIA RTX Spark looks like the most interesting Windows PC platform of the decade, and the first laptops ship October 16. The four machines above let you start running local 70B models tonight on hardware that already exists. The Mac Studio M5 Max takes over as the LLM enthusiast's default; the Strix Halo mini PCs (GMKtec EVO-X2 and Beelink GTR9 Pro) are the closest architectural analog to RTX Spark you can buy today; the Mac mini M5 Pro is the curious beginner's entry point. For image and video diffusion specifically, build your own RTX 5090 tower from the GPU Buying Guide rather than buying an unbranded Amazon prebuild. Once retail RTX Spark laptops arrive, we'll review one against this lineup and update the rankings. For now: pick by budget and ecosystem.