DGX Spark vs Mac Studio M5 Ultra: Local LLM Inference Benchmarked (October 2026)

The M5 Ultra configuration benchmarked in September 2026 is a step up from the chip's original 2026 launch spec. It is a quad die design connected through Apple's UltraFusion interconnect, with memory bandwidth measured at 1.2 terabytes per second and configurations running from 96GB up to 256GB of unified memory, with a 512GB option arriving in late October 2026. Pricing starts at 5,499 dollars for the base 30 core CPU, 64 core GPU, 96GB configuration and reaches 10,799 dollars for the top 36 core CPU, 80 core GPU, 256GB build, with the jump from 96GB to 256GB of memory alone costing an additional 4,000 dollars.

On real workloads, that memory bandwidth shows up directly in decode speed. Running Qwen3.8 Flash Next, a mixture of experts model, the top configuration decoded at 90.7 tokens per second at a 4K context window and held 60.6 tokens per second even at 128K context, with prefill on 16K prompts measured at 2,887 tokens per second. On a dense model, Qwen 3.8 27B run through llama.cpp, decode measured 46.3 tokens per second at minimal context and 30.1 tokens per second at 128K context. GLM 5.3 Flash decoded at 41 tokens per second with prefill at 1,107 tokens per second.

Single stream: the Mac wins decisively

Against a single DGX Spark running the same Qwen 3.8 27B model, the gap is stark. The Spark decoded at 11.8 tokens per second against the Mac's 46.3, nearly a four times difference on identical single stream decode. That tracks the specification gap directly: the M5 Ultra's 1.2 TB/s of bandwidth is more than four times the Spark's roughly 273 GB/s, and for single stream decode, which is almost purely bandwidth bound, throughput scales close to linearly with bandwidth. If your workload is one user or one agent pulling from one model at a time, which describes most individual developer and small team use, a single M5 Ultra is the faster machine at every price point NVIDIA currently ships a Spark at, a pattern consistent with what we measured on the earlier generation M5 Max against a single DGX Spark, where the unified memory Apple chip also held a bandwidth advantage over the Spark on large model decode.

Multi user concurrency: cluster the Sparks instead

The comparison flips once the workload is several concurrent requests rather than one. Two DGX Spark units networked together over NVIDIA's GX10 interconnect reached 63.7 tokens per second in aggregate across four concurrent streams, ahead of the 41 tokens per second a single M5 Ultra delivered running one stream at a time on a comparable model. The Mac Studio is built around a single powerful node serving one request well. The Spark's design intent, a small unified memory box meant to be networked, lets it scale horizontally in a way a single Mac Studio cannot, because there is no supported way to pool two Mac Studios into one inference target the way GX10 pools Sparks. If your actual workload is several engineers or several agents hitting a shared local model concurrently rather than one person running one session, a small Spark cluster closes the single stream gap and then overtakes it on the metric that matters for that use case.

The decision in practice

Buy a single M5 Ultra if you are provisioning for one primary user or one agent pipeline that runs one model at a time and you want the fastest single stream experience money can buy on unified memory hardware, especially if you expect to need the 256GB or 512GB memory ceiling for a larger model than 27B. The efficiency side of that decision, what the extra bandwidth costs in power over the chip's life, is covered in our tokens per watt comparison across the M5 lineup. Buy a Spark cluster if your actual constraint is serving several people or several parallel agent workloads at once and you are comfortable operating NVIDIA's CUDA ecosystem and networking two or more small boxes together, where the community tooling and recipe ecosystem is also simply larger than what exists for Apple Silicon inference today.

FAQ

Is the new M5 Ultra configuration the same chip covered in earlier 2026 benchmarks? It is the same chip family at a higher memory and bandwidth configuration than the specs covered in our original M5 Ultra inference guide from earlier in the year, reflecting the configuration options available as of September 2026.

Does a single DGX Spark ever beat a single M5 Ultra? Not on single stream decode throughput in these tests. The Spark's advantage shows up specifically when clustered for concurrent, multi stream workloads, not in a one to one single stream comparison.

How many DGX Spark units do you need to match an M5 Ultra on concurrency? Two networked Spark nodes already exceeded the Mac's single stream throughput when serving four concurrent streams in this benchmark, though the exact crossover point depends on your specific model and context length.

Does memory capacity or bandwidth matter more for local inference? Both matter, but they answer different questions. Bandwidth sets your decode speed ceiling. Memory capacity sets which models you can load at all. The M5 Ultra's 256GB and upcoming 512GB configurations extend that ceiling well past what a single Spark's 128GB supports.

Which machine is simpler to operate for a small engineering team? A single Mac Studio has no clustering or networking configuration to manage, which is a real operational advantage if your team does not already run CUDA infrastructure. A Spark cluster requires setting up and maintaining the GX10 networking layer between nodes.

When this applies to your stack

If you are provisioning local inference hardware for a single developer, a single agent pipeline, or a workload where one request at a time is the norm, buy the M5 Ultra and take the single stream throughput win along with the larger memory ceiling. If your actual bottleneck is serving a team or a fleet of concurrent agents against a shared local model, price out a small Spark cluster before you default to the Mac, because that is the workload where NVIDIA's networking advantage shows up in the numbers. Contra Collective designs local and hybrid inference infrastructure for engineering teams who need the hardware decision to match the actual workload, not the benchmark that is easiest to find.