DGX Spark vs Mac Studio vs Ryzen AI Max+ 395: Local LLM Hardware Compared (2026)

DGX Spark vs Mac Studio comes down to software stack versus memory bandwidth. NVIDIA's DGX Spark gives you 128 GB, CUDA and 200 Gb/s clustering for $4,699. Apple's M5 Ultra Mac Studio offers 1.2 TB/s and 256 GB today, with 512 GB due in late October. AMD's Ryzen AI Halo matches Spark's memory on x86 for $3,999.

Desk-side AI systems with 128 GB or more of unified memory became a real category in the last year. For a platform or security team planning a local LLM rollout, they answer a practical question: where do engineers test open-weight models before anyone signs a purchase order for GPU servers?

This comparison uses the manufacturers’ spec sheets, press releases and price notices, checked on 27 September 2026. It does not quote tokens-per-second figures, because those depend on the model, quantization, runtime version and prompt length. Instead it shows how to reason from memory capacity and bandwidth to what each system can run.

The three platforms at a glance

Specifications verified on the vendors’ pages on 27 September 2026. Prices are US list prices.

A few notes behind the table. NVIDIA raised the Founders Edition price from $3,999 to $4,699 in the week of 25 February 2026, citing memory supply constraints. Apple announced the M5 Mac Studio on 25 August 2026 and began deliveries on 22 September, and memory upgrades above the base configurations add to the starting prices shown. AMD sells Ryzen AI Halo through Micro Center and lists no price on its own pages; the $3,999 figure for the 2 TB model comes from StorageReview’s launch-day review.

Memory capacity and bandwidth decide what runs

Capacity sets the largest model you can load. Bandwidth sets how fast tokens come out once it is loaded.

Generating each token requires reading the active weights from memory, so decode speed has a hard ceiling of roughly bandwidth ÷ bytes read per token. You can apply that method to any model and system. Relative to DGX Spark’s 273 GB/s, the M5 Ultra’s 1.2 TB/s is about 4.4 times higher, the 40-core M5 Max about 2.2 times, and Ryzen AI Halo’s 256 GB/s about the same. For the same model and quantization, the decode ceiling moves in those ratios. Real throughput lands below the ceiling, so measure it on your own prompts.

Prompt processing, or prefill, behaves differently. It is compute-bound, so accelerator throughput matters more than bandwidth. Coding agents and retrieval pipelines send long prompts on every turn, which makes prefill a large share of what users wait for. A system can win on decode and lose on prefill, and the reverse.

Two consequences follow for model choice. Mixture-of-experts models with few active parameters, such as gpt-oss-120b (5.1B active) or Qwen3-Coder-Next (3B active), read far fewer bytes per token than dense models of similar size, so they suit the 256 to 273 GB/s systems. Dense models in the 100B-plus range favour the Mac Studio’s bandwidth. Our local coding model shortlist lists active parameters for ten current models.

Compute figures are not comparable

The three vendors state compute in different units, so their headline numbers cannot be lined up.

• NVIDIA quotes up to 1 PFLOP of FP4, and its spec page notes that this is a theoretical figure using the sparsity feature.
• AMD quotes 60 FP16 TFLOPS for Ryzen AI Halo’s GPU, up to 50 TOPS for the NPU, and up to 126 TOPS for the whole Ryzen AI Max+ 395 chip.
• Apple publishes multiples rather than absolute figures. Its M5 Ultra release claims up to 4.5 times the peak GPU compute for AI of M3 Ultra, with a Neural Accelerator in each GPU core.

Sparse FP4 and dense FP16 differ by a large factor before any real workload runs. AMD also publishes its own tokens-per-second comparisons against DGX Spark on the Ryzen AI Halo page. Treat every vendor chart, including NVIDIA’s and Apple’s, as a claim to reproduce on your models.

Software stack: CUDA, MLX or ROCm

For an enterprise, the software stack often matters more than the silicon, because it decides whether work done on the desk transfers to production servers.

DGX Spark runs NVIDIA DGX OS with CUDA. NVIDIA publishes playbooks for vLLM, SGLang, TensorRT-LLM, NIM containers, Ollama and LM Studio, plus NVFP4 quantization and multi-Spark NCCL setups. The practical benefit is parity with data-centre Blackwell servers. NVIDIA’s Nemotron 3 Super model card, for example, lists one B200 or one DGX Spark as the minimum hardware for its NVFP4 checkpoint. NVIDIA’s playbook list also includes local-model guides for OpenClaw and Hermes Agent; our OpenClaw and Hermes Agent local setup guide covers the hardware-neutral version.

Mac Studio runs macOS. Apple points developers to MLX, its open-source framework for Apple silicon, and to Core AI, a new framework for running and deploying models on Apple silicon. MLX LM supports quantization and distributed inference across devices. llama.cpp treats Apple silicon as a first-class target through Metal, and LM Studio and Ollama both support M-series Macs. There is no CUDA, so serving recipes written for NVIDIA servers do not transfer as-is, and macOS is rarely part of a server fleet.

Ryzen AI Halo ships with Linux or Windows 11, and AMD says ROCm, its GPU software stack, supports the system on both. It is an ordinary x86 machine, so standard container tooling and internal images apply. AMD has also announced a Ryzen AI Halo model with the Ryzen AI Max+ PRO 495 and 192 GB of memory support, not yet available.

Clustering, power and the wider field

Clustering. Each DGX Spark has two QSFP ports at up to 200 Gb/s. NVIDIA’s user guide supports up to three units cabled directly and up to four through a switch, and NVIDIA states four systems can work with models of up to 700 billion parameters. Apple says Thunderbolt 5 with RDMA lets several Mac Studios cluster, and claims up to three times faster inference from four systems than from one. Ryzen AI Halo has a single 10 GbE port, which StorageReview notes caps multi-node clustering.

Power. DGX Spark uses a 240 W external supply around a 140 W GB10. AMD rates Ryzen AI Halo at 120 W TDP. Apple lists 480 W as the Mac Studio’s maximum continuous power, a ceiling rather than a typical draw. None needs data-centre power or cooling.

Other boxes on the same chips. NVIDIA lists ASUS, Dell, Gigabyte, HP, Lenovo, MSI and Acer as GB10 system partners. Gigabyte’s AI TOP ATOM, for example, matches Spark’s 128 GB, 273 GB/s, dual 200 Gb/s ConnectX-7 ports and 240 W adapter. The same processor family, in its Ryzen AI Max+ PRO 395 version, powers HP’s Z2 Mini G1a, which HP says can assign up to 96 GB of its 128 GB exclusively to the GPU. Check how much memory each system lets the GPU address before you size models against it. For smaller budgets, Apple’s Mac mini with M5 Pro supports up to 64 GB from $1,699. NVIDIA has also announced RTX Spark, a Windows PC platform pairing a Grace CPU with a Blackwell RTX GPU and up to 128 GB of unified memory, with laptops and compact desktops due this fall.

What each system can realistically run

The planning arithmetic: reserve about 20% of memory for the operating system, the runtime and a modest KV cache, then divide the remainder by roughly 0.55 bytes per parameter for 4-bit formats. Vendor-shipped low-precision checkpoints are listed at their published size.

NVIDIA’s and AMD’s “up to 200B parameters” claims for a single 128 GB system are consistent with this arithmetic at about half a byte per parameter. Two cautions apply. Linked Sparks are separate 128 GB pools joined by a network, so the model is split across nodes and every token crosses the link. And the KV cache grows with each concurrent session, so a model that fits for one user may not fit for five. Our GPU requirements guide shows the KV arithmetic for concurrency.

DGX Spark for enterprise teams: where desk-side systems fit

All three platforms are strong prototyping and evaluation tools. They suit:

• Model evaluation. Run your golden test set against candidate open-weight models before you size servers.
• Quantization checks. Validate the exact NVFP4, MXFP4, GGUF or MLX build you intend to deploy, not the vendor’s full-precision scores.
• Agent prototyping. Give developers a sandbox for coding agents and agent harnesses against a local endpoint.
• Isolated demos. Show a working local model in a room with no network connection.
• Fine-tuning trials. NVIDIA states DGX Spark can fine-tune models of up to 70 billion parameters.

They are a weak fit for shared production serving. One desk-side unit is a single point of failure. Every concurrent session shares one memory pool and 256 to 273 GB/s of bandwidth on Spark and Halo. Hardware that sits under a desk and holds model weights and source-code context needs the same physical security, asset tracking and patch discipline as a server, and it usually sits outside the tools that provide them.

Move to rack servers when any of these become true: more users than one system’s KV budget can hold at your context length, an availability target, several teams sharing one endpoint, or an audit requirement for every request. Our on-prem AI reference architecture maps server classes to model sizes, and the buy, lease or colocate analysis covers how to source them.

One rule helps whichever box you pick: match the desk system to the production stack. A team heading for NVIDIA servers gets the most transferable work from a GB10 system. A team standardising on AMD Instinct gets ROCm experience from Ryzen AI Halo. A Mac Studio is the strongest single-user machine for very large models, but little of its serving setup carries over to a Linux GPU fleet.

How VDF AI fits

VDF AI’s self-hosted packages ship as container images. Its self-hosting requirements page lists Linux on ARM64 as fully supported through multi-architecture images, and macOS through Docker Desktop for evaluation only, not production. A single-host pilot needs 4 to 8 vCPUs, 16 to 32 GB of memory and 100 GB of SSD.

VDF AI Router registers Ollama and custom on-premises model deployments alongside approved cloud models, and its air-gap mode restricts routing to local models. During a pilot, a desk-side system can host the model endpoint the router targets. When the workload moves to rack servers, applications keep pointing at the router while the endpoint behind it changes.