DGX Spark vs Strix Halo: 128 GB Local AI Desktops Compared

This page is the focused two-machine comparison. It does not rank custom GPU towers, Apple systems, rack servers, or every Canadian workstation. Buyers who still need that broader choice should start with which local AI computer to buy in Canada. Here the question is narrower: if a compact 128 GB shared-memory computer is already the desired shape, should its foundation be NVIDIA GB10 or AMD Ryzen AI Max+ 395?

The comparison was reviewed on 25 August 2026 against primary documentation. NVIDIA’s current DGX Spark hardware overview, system overview, release notes, porting guide, and build playbooks are the sources for Spark. AMD’s Ryzen AI Max+ 395 product specification, Ryzen AI Halo developer-platform specification, and ROCm documentation are the sources for Strix Halo. D-Central did not design either processor and has not substituted reseller benchmark charts for its own testing.

Official specifications, with the labels kept honest

Do not turn the power row into an electricity-cost promise. A power-adapter rating, a processor TDP, and measured wall draw are three different things. D-Central has not published a controlled wall-power dataset for these two offers. Likewise, the 273 GB/s and 256 GB/s figures are official architecture specifications, not measured application throughput.

Shared memory is the common feature, not a common implementation

Both machines avoid the conventional split in which an x86 CPU uses system RAM and a discrete GPU uses a much smaller isolated VRAM pool. That is why both are attractive for local inference: one physically shared pool can hold model weights that do not fit on a mainstream 24 GB card. But “128 GB unified” still does not mean that 128 GB is vacant, dedicated to the GPU, or usable by every framework without configuration.

NVIDIA’s DGX Spark porting guide describes a dynamic unified-memory architecture without a fixed iGPU carve-out. CPU and GPU share the physical memory space, reducing explicit copies between separate RAM and VRAM pools. The operating system, display, runtime, model weights, KV cache, and other processes still consume that capacity. NVIDIA’s release notes also document adjustable display-reserved memory and ongoing unified-memory out-of-memory handling, which is a useful reminder that the platform software affects the usable envelope.

AMD’s Strix Halo system-optimization guide explains its shared memory through GPU virtual memory, GTT mappings, and dynamic allocation. On Linux, the amount a process can map for GPU use can depend on kernel and TTM settings; AMD provides explicit configuration guidance rather than claiming that all 128 GB automatically appears as an application-ready GPU allocation. Firmware labels such as dedicated GPU memory, carve-out, GART, GTT, and VRAM can refer to different controls around the same physically shared pool.

The practical rule is simple: size a deployment from the exact model artifact and runtime, then leave room for context and concurrent requests. Parameter count alone is not enough. A sparse mixture-of-experts model, a dense model, a multimodal model, and a long-context model with the same headline parameter count can impose very different memory and compute requirements. Use VRAM vs unified memory for the underlying concepts and the local LLM memory calculator for a planning estimate.

Arm64 plus CUDA versus x86-64 plus ROCm

Spark’s strongest advantage is a coherent NVIDIA software path. DGX OS includes NVIDIA drivers and the CUDA ecosystem on an Arm64 system. NVIDIA’s Spark site publishes task-specific, copyable workflows rather than leaving the buyer to assemble a stack from forum fragments. The official llama.cpp on DGX Spark playbook compiles the CUDA backend for GB10 and launches an OpenAI-compatible server. The official vLLM playbook lists Spark as supported hardware, points to tested model recipes, and documents single-node and multi-node prerequisites.

That ecosystem does not erase Arm64. Any closed binary, native Python wheel, container base image, browser component, database extension, or monitoring agent that only ships for x86-64 needs investigation. Some packages are multi-architecture, some can be compiled, and some remain blockers. A buyer whose “AI workstation” must also run ordinary Windows-only line-of-business software should not assume Spark can replace an x86 desktop.

Strix Halo starts from the other side. Ryzen AI Max+ 395 is x86-64, and AMD lists Windows 11, Ubuntu, and RHEL families in the processor’s operating-system support. The familiar architecture can reduce friction for non-AI applications and x86-only dependencies. GPU acceleration still depends on the current ROCm support envelope. AMD’s current ROCm compatibility matrix lists Radeon 8060S and the precise supported Ubuntu and Windows versions. Do not freeze an August matrix into a timeless claim: check that official matrix again before installation.

AMD documents vLLM and llama.cpp routes for supported Ryzen APUs, including a Ryzen vLLM Docker workflow. That makes “AMD cannot run vLLM” too broad. The honest distinction is that CUDA-only containers do not become ROCm containers merely because a model fits in memory. Image, framework, model, kernel, driver, Python, and ROCm versions must form a supported combination. Windows support can also cover a narrower framework surface than Linux. The decision is not CUDA good, ROCm bad; it is whether the required production path is documented for the selected platform.

Runtime fit: what can be promised before benchmarking

Nothing in this table ranks output speed. D-Central has not run a controlled cross-platform benchmark with fixed prompts, versions, model files, context lengths, batch sizes, and wall-power measurement. Until that dataset exists, the defensible comparison is official specifications, supported software paths, and operational fit. Capacity lets a workload enter the race; it does not declare the winner.

DGX Spark operational caveats

• Arm64 is a purchase constraint. Verify native packages and container architectures before ordering.
• DGX OS is an appliance stack that still needs lifecycle management. NVIDIA’s release notes list current DGX OS, driver, CUDA, kernel, firmware, and known-issue changes. Partner GB10 systems may receive updates on different schedules.
• The supplied power adapter matters. NVIDIA warns that a lower-rated replacement can reduce performance, prevent boot, or cause shutdowns.
• Model downloads and containers may require external accounts. An offline production target still needs a controlled process for acquiring weights, images, licences, and security updates.
• Large-model support is conditional. “Up to 200B” is NVIDIA’s platform statement; quantization, context, cache, and the chosen serving recipe still decide practical fit.
• Clustering is a system project. ConnectX-7 ports make high-speed links possible, but cables, IP configuration, orchestration, and a runtime that supports distributed execution are still required.

Strix Halo operational caveats

• The exact chassis matters. Storage, network interfaces, firmware controls, cooling, acoustic behaviour, and warranty are system-vendor properties, not Ryzen AI Max+ 395 processor properties. D-Central confirms them before order.
• Memory mapping may need configuration. AMD’s optimization guide documents GTT/TTM limits for large GPU allocations on Linux. “128 GB installed” is not a substitute for checking usable shared GPU memory.
• ROCm is a versioned support matrix. Match Radeon 8060S, operating system, kernel or driver, framework, and ROCm version.
• Windows and Linux are not feature-identical. Choose the operating system around the required framework, not only user familiarity.
• The memory is soldered. Buy the required capacity at the start; this is not a tower with replaceable DIMMs or GPU cards.
• CUDA-only software remains CUDA-only. Portability needs evidence from the application’s support documentation or a tested alternative backend.

Single machine versus multi-machine growth

DGX Spark has the clearer vendor-documented scale-out story. NVIDIA’s ConnectX-7 networking guide documents QSFP connectivity and the network configuration needed to cluster Spark systems. Current release notes describe NVIDIA Sync assistance for several directly connected devices and NCCL improvements. The vLLM playbook identifies QSFP, passwordless SSH, and Ray as prerequisites for its multi-node path.

That does not make two Sparks one ordinary 256 GB desktop. Distributed model execution depends on the runtime and recipe. It adds another operating system, another failure domain, cabling, network configuration, synchronization, and support work. A buyer who already knows one 128 GB pool is insufficient should compare a designed multi-node deployment against a custom multi-GPU or rack system before simply doubling the cart.

The D-Central Strix Halo offer is a single-system product. D-Central does not advertise two AMD desktops as one supported shared-memory cluster. Networked services can distribute requests across independent machines, but that is a different architecture from splitting one model across them. If the requirement is high availability or multiple replicas rather than one oversized model, describe that workload explicitly.

Canadian merchant fit and total purchase boundary

D-Central lists a configured NVIDIA DGX Spark at $9,449 CAD and a configured AMD Strix Halo 128 GB desktop at $7,349 CAD, before tax and shipping. The difference buys different software and operating envelopes; it is not a D-Central claim that one provides a fixed percentage more performance.

The Spark offer names a 4 TB DGX system. The AMD offer names the processor and 128 GB capacity while D-Central confirms the sourced chassis, storage, operating system, lead time, and manufacturer warranty path before placing the order. Both include the agreed first-boot local inference entry point and a written model-fit note. Both are sourced manufacturer systems, not computers D-Central claims to have designed or manufactured.

A made-to-order purchase can become non-refundable once upstream hardware is ordered, subject to the stated dead-on-arrival, defect, warranty, and statutory rights. If the buyer needs a formal architecture, identity integration, private RAG, network hardening, logging, or an isolated update path, scope on-premises AI deployment separately. First boot and production integration are not the same service.

Decision matrix

Security, sovereignty, and compliance

Both systems can run inference locally, which can remove a routine prompt and document path to a foreign API. That architectural fact does not automatically establish Canadian sovereignty, Quebec Law 25 compliance, PIPEDA compliance, or security. Administrators, remote support, telemetry, model sources, licences, encryption keys, network exposure, retention, backups, and incident response remain part of the control boundary.

NVIDIA’s release notes now document enterprise provisioning, local update repositories, customized images, and air-gapped deployment support for Spark. Those are useful controls when implemented; they are not automatic properties of an unopened box. The AMD path can also be isolated, but its driver, framework, and model-transfer process must be designed for the selected operating system. Use the Canadian AI inference hub, CLOUD Act orientation, and Law 25 local-AI guide to frame the wider decision.

Frequently asked questions

Is DGX Spark faster than Strix Halo?

D-Central does not publish a universal speed winner without a controlled benchmark. Spark has the clearer path for CUDA-specific frameworks and NVIDIA’s own recipes. Strix Halo has a different x86 and ROCm operating envelope. Speed depends on model, quantization, prompt processing, generation, context, batch size, runtime version, and power configuration. Compare support first; compare measured performance only when the test conditions are stated.

Is 128 GB on Spark the same as 128 GB on Strix Halo?

Both are physically shared CPU/GPU pools, but platform software and memory management differ. The operating system and applications consume part of each pool. NVIDIA describes dynamic UMA; AMD describes GPUVM and configurable shared mappings. Treat 128 GB as total installed capacity, then calculate the workload’s usable headroom.

Can Strix Halo run vLLM?

AMD publishes a Ryzen APU vLLM Docker workflow and current support information. That is evidence of a supported path, not proof that every model or vLLM feature works on every Windows or Linux combination. Validate Radeon 8060S, ROCm, driver or kernel, container, vLLM, model, and quantization versions together.

Can DGX Spark run x86 containers?

Not natively as though it were an x86 host. Spark is Arm64. Use an Arm64 or multi-architecture image, compile the dependency where practical, or choose a supported alternative. Emulation may exist for some tools, but it should not be the foundation of a production purchase unless the workload has been tested.

Can two DGX Spark systems run a larger model?

NVIDIA documents ConnectX-7 clustering and multi-node serving. The chosen model and runtime still need a distributed recipe, and the deployment needs QSFP cabling, network configuration, orchestration, authentication, monitoring, and recovery. D-Central treats this as a scoped integration rather than promising that every application sees one automatic 256 GB pool.

Is the D-Central AMD desktop AMD’s Ryzen AI Halo developer platform?

No promise is made that it is AMD’s named retail developer-platform SKU. D-Central sells a sourcable Ryzen AI Max+ 395 system with 128 GB, confirms the chassis and storage on the order, and credits AMD for the processor. The official Halo developer-platform page is used here to source AMD’s platform-level specifications, not to mislabel D-Central’s inventory.

Which should a Canadian business buy?

Choose Spark when a verified NVIDIA software path and compact DGX appliance are the decisive requirements. Choose Strix Halo when x86, Windows-or-Linux choice, and the lower $7,349 CAD offer are decisive and the required AMD stack is documented. Choose neither when serviceability, replaceable cards, x86 CUDA, redundancy, or rack operations are the real requirement. For that broader decision, return to the Canadian local AI computer buyer guide.

Compare the actual offers: NVIDIA DGX Spark — $9,449 CAD · AMD Strix Halo 128 GB — $7,349 CAD · scope an on-premises deployment.