DGX Spark vs Mac Studio LLM: Tokens/Sec & Memory (2026)

The DGX Spark vs Mac Studio LLM comparisons online all point at the same single number: one chat session, one prompt, tokens per second. That number is real, but it hides the two things that actually decide which box is worth buying: what happens to throughput once context grows past a few thousand tokens, and what the CUDA stack costs you in setup time that Apple's Metal stack doesn't charge. We pulled the measured tokens/sec and memory ceilings from five independently published benchmarks running the same quantized models on both machines, then ran the dollar math against renting a cloud GPU instead. This is a desk-side-vs-desk-side comparison; for the Mac Studio's numbers against a rented cloud H100 instead, see our separate Mac Studio M5 Ultra vs a rented H100 breakdown.

TL;DR: DGX Spark vs Mac Studio LLM

• Tokens/sec: DGX Spark decodes GPT-OSS 120B (MXFP4) at 41 tok/s; Mac Studio decodes at 34 tok/s, falling to 6 tok/s as context grows, per a published Ollama benchmark.
• Memory: DGX Spark fixes 128GB unified LPDDR5x at 273 GB/s; Mac Studio M5 Ultra spans 96GB to 512GB at 1.2TB/s bandwidth.
• Price: DGX Spark Founders Edition is $4,699 (up from $3,999, 25 Feb 2026); Mac Studio M5 Ultra starts at $5,499, reaching $18,299 at 256GB.
• Rent instead: Spheron lists H100 on-demand at $2.64/hr as of 09 Oct 2026. Compare live GPU pricing.

Specs Side by Side: Unified Memory, Bandwidth, and Power Draw

Both machines solve the same problem, fitting a large model in one memory pool instead of splitting it across GPUs, but they get there with opposite tradeoffs. DGX Spark has a fixed, smaller, slower memory pool paired with NVIDIA's full CUDA software stack and a 1 petaFLOP FP4 compute budget. Mac Studio offers a wider range of much larger, much faster memory pools, built around Apple's Metal/MLX stack instead of CUDA.

The DGX Spark's 128GB of unified LPDDR5x memory at 273 GB/s bandwidth and up to 1 petaFLOP of FP4 (sparse) compute come from NVIDIA's own announcement of the Grace Blackwell platform. On the price side, NVIDIA raised the Founders Edition price from $3,999 to $4,699 on February 25, 2026, citing "industry wide memory supply constraints," with no hardware change and the increase applied globally.

Apple's M5 Ultra Mac Studio starts at $5,499 with 96GB of unified memory and 1.2TB/s of memory bandwidth; a 256GB configuration (36-core CPU, 80-core GPU, 16TB storage) reaches $18,299, and a 512GB configuration was announced but has no price yet, with shipping pushed to late October 2026. That's the core tradeoff in one table: DGX Spark fixes memory at 128GB and charges a flat, lower price; Mac Studio lets you buy more memory than DGX Spark can ever hold, at a price that climbs fast once you do.

For readers also weighing a third desk-side option, our DGX Spark vs RTX 5090 tokens-per-second comparison covers the consumer-GPU alternative to both of these unified-memory boxes.

Tokens Per Second: DGX Spark vs Mac Studio LLM Throughput (M5 Ultra Benchmarks)

No published benchmark has run both DGX Spark and the new M5 Ultra Mac Studio side by side on identical hardware yet, since the M5 Ultra only shipped in late 2026. What exists instead is a set of independently published tests that each ran the same model family and quantization on one machine or the other, close enough to compare directly. Here's what they measured.

GPT-OSS 120B (MXFP4): The Closest Head-to-Head Data Point

This is the single closest apples-to-apples test available: the same 65GB MXFP4 file, run through Ollama, on a DGX Spark and a Mac Studio in the same test session. One published comparison measured DGX Spark generating at 41 tokens/sec with 1,159 tokens/sec prompt evaluation, while the Mac Studio generated at 34 tokens/sec but dropped to 6 tokens/sec as context grew; an RTX 4080 included in the same test, split 78% CPU/22% GPU because the model didn't fit in its VRAM, generated at 12.45 tokens/sec with 969 tokens/sec prompt evaluation.

That gap between the headline 34 tok/s and the 6 tok/s floor is the number worth remembering. DGX Spark's decode speed on this test held closer to its starting number; the Mac Studio's number degraded hard as the prompt got longer, which matters more than the headline figure if your actual use is long-context chat or document analysis rather than short single-turn prompts.

Dense Models at 70B: Where Precision and Context Length Change the Story

At the 70B dense scale, the clearest third-party estimate for Mac Studio comes from Contra Collective's independent testing, which puts the M5 Ultra at roughly 20-25 tokens/sec realistic throughput on a 70B model at 4-bit quantization, with a theoretical ceiling near 30 tokens/sec. For DGX Spark's side of that same class of model, LMSYS measured Llama 3.1 8B at Q4_K_M decoding at 38 tokens/sec, and separately GPT-OSS 20B at MXFP4 decoding at 49.7 tokens/sec, both smaller than 70B but the closest LMSYS data point available for DGX Spark's dense-model decode behavior.

On a much larger mixture-of-experts model, the comparison gets closer rather than further apart. A 96-hour test running Qwen3.5-397B-A17B measured a dual DGX Spark setup decoding at 26.9-27.3 tokens/sec across 4K-32K context, against a Mac Studio M3 Ultra (the previous generation, not the M5 Ultra) decoding at 26.7-29.2 tokens/sec over the same context range. On that model, the two machines landed within a couple tokens/sec of each other on decode, with DGX Spark's prefill running 643-733 tokens/sec against the Mac's 317-537 tokens/sec. Note that comparison used the M3 Ultra, not the M5 Ultra this post is primarily about, so treat it as a directional data point on MoE scaling rather than a direct M5 Ultra number.

Benjamin Marie's Kaitchup newsletter frames DGX Spark's actual strength plainly: "Where DGX Spark makes more sense, I think, is fine-tuning and small-scale training, especially for models under ~8B parameters." That's a different job than raw chat throughput, and it's the one DGX Spark's CUDA stack is actually built for.

Prefill and Time-to-First-Token: DGX Spark's Real Advantage

Decode tokens/sec gets all the attention because it's what a chat window feels like while it streams a response, but prefill, the compute-bound pass that reads your prompt and produces the first token, tells a different story. On the GPT-OSS 120B test above, DGX Spark's 1,159 tokens/sec prompt evaluation ran roughly 20% faster than the RTX 4080's 969 tokens/sec, despite decoding at little more than 3x the RTX 4080's rate once generation started. On the Qwen3.5-397B-A17B test, DGX Spark's prefill advantage over the Mac Studio M3 Ultra was larger still: 643-733 tokens/sec against 317-537 tokens/sec, up to roughly double depending on context length.

That matters most for long-context, document-heavy workloads where the prompt itself is thousands of tokens. A faster prefill means a shorter wait before the first word of the answer appears, independent of how fast the rest of the response streams. If your workload is mostly short chat turns, decode tokens/sec dominates the felt experience. If it's RAG over long documents or multi-turn conversations with growing context, prefill speed and the point where decode throughput falls off, like the Mac Studio's drop to 6 tokens/sec in the GPT-OSS 120B test, matter more than the headline number either machine advertises.

What Fits in Memory: 128GB vs 96GB-512GB Unified Pools

DGX Spark's memory story is simple because there's only one number: 128GB, fixed, no configuration choice. That's enough to load a 70B dense model at 4-bit quantization (roughly 40GB of weights) with headroom for KV cache, or a 120B MoE model like gpt-oss-120b at MXFP4 (a 65GB file) with room to spare.

Mac Studio's memory story is a decision tree. The 96GB base configuration covers the same 70B-at-4-bit and 120B-MoE-at-MXFP4 territory DGX Spark covers, at a higher starting price than DGX Spark's $4,699 (see the price table below) but with faster memory bandwidth. Step up to the 256GB configuration ($18,299) and you have room for much larger MoE models or multiple models loaded simultaneously. The announced-but-unpriced 512GB tier, shipping late October 2026, is built for the largest open-weight releases: a model needing 400GB+ loaded at 4-bit, something neither DGX Spark nor any Mac Studio configuration under 256GB can hold.

The practical takeaway: DGX Spark's 128GB ceiling is a hard wall you hit once and never cross, since there's no larger configuration to buy. Mac Studio's ceiling moves with how much you're willing to spend, up to 512GB, but every step up that ladder costs real money while DGX Spark's single configuration stays at one fixed price.

Setup and Software: CUDA Stack vs Mac's "It Just Works"

This is where the two machines diverge hardest, and it's the part the tokens-per-second tables don't capture. One tester who ran both machines side by side for 96 hours summarized it in one sentence: "the Mac Studio was serving inference four hours after I plugged it in. The DGX Sparks took four days." That tester ultimately split the workload rather than picking one machine, routing embedding and reranking for a RAG pipeline to the DGX Sparks and dedicating the Mac Studio to generation, because each machine was genuinely better suited to a different stage of the pipeline once both were running.

A separate NVIDIA developer forum thread captures the same friction from someone moving the other direction, from Mac to DGX Spark: "Instead of a single Start button, I got a LEGO set of knobs and 'best practices' that change depending on the model, engine, quantization." That same thread is where the tuning payoff shows up too: one poster raised gpt-oss-120b generation speed from 43 tok/s to 56.43 tok/s on the same DGX Spark hardware just by setting -fa 1 (Flash Attention), -ub 2048 (batch size), and --no-mmap in llama.cpp, a roughly 31% gain from flags alone. A reply in the same thread noted that llama.cpp tends to outperform vLLM for single-user throughput, while vLLM wins once you're serving multiple concurrent requests.

That configurability cuts both ways. In a separate forum thread asking which of three desk-side machines (DGX Spark, an RTX 5090 workstation, or a Mac Studio Ultra) is worth keeping, one developer landed on DGX Spark specifically for the ecosystem, not the raw speed: "I'm leaning towards DGX just because of the libraries." If you already run vLLM, SGLang, or a CUDA-dependent fine-tuning pipeline, DGX Spark's extra setup time buys you a stack that matches what you'd deploy to a cloud GPU later without rewriting anything. If you want a model answering prompts within the hour and don't need CUDA-specific tooling, Mac Studio's Metal/MLX stack and Ollama support get there with none of that configuration tax.

Price and Break-Even: DGX Spark vs Mac Studio vs Renting a Cloud GPU

Here's the actual math. Spheron currently lists H100 on-demand at $2.64/hr as of 09 Oct 2026, billed per minute with a 20-minute minimum. The break-even table below is a worked example that freezes that rate at its 7 October 2026 snapshot value, $2.64/hr, so the hours figures stay fixed and checkable instead of drifting every time this page renders; check the live token above for today's actual rate.

That table isn't an argument that renting always wins; it's a reference point for what each box's purchase price actually buys in cloud hours, at one specific day's rate. A developer using either box 4-8 hours a day, every weekday, recovers that equivalent-hours number over many months of calendar time, not in one continuous 74- or 289-day cloud rental. Our DGX Spark cloud pipeline guide, linked further down, works through that daily-usage version of the break-even math in more detail.

What the table does make clear: DGX Spark is the cheaper entry point of the two purchased boxes at current prices, and the gap to Mac Studio widens fast once you need more memory than the 96GB base tier. Renting instead of buying either skips the decision entirely while you figure out which one, if either, you actually need: $0.78/hr gets you an RTX 5090 on Spheron, and $2.64/hr gets you an H100, both with per-minute billing and a 20-minute minimum, enough to run the exact benchmarks in this post against your own models before spending $4,699-$18,299.

Which One to Buy: Decision Table by Workload

Neither machine is wrong for local, single-user, offline inference; they're built for the same job with opposite tradeoffs. DGX Spark's fixed 128GB and CUDA stack earn their place once your workload is fine-tuning, MoE models, or anything you'll eventually deploy on a CUDA-based cloud GPU. Mac Studio's wider memory range and Metal/MLX simplicity earn their place when you want an answer today, you're not fighting driver configs, and you're willing to pay more for memory tiers DGX Spark can't offer at all. For a broader shortlist that includes consumer GPUs alongside both desk-side options, see our best NVIDIA GPUs for LLMs guide, and for readers specifically comparing DGX Spark against OEM GB10 clones from Dell, ASUS, and others rather than against a Mac, see our DGX Spark alternatives comparison.

When to Graduate From Either Box to Cloud GB200/B200

Both machines hit the same wall: production serving with real concurrency, 24/7 uptime, or a model too large for one unified-memory pool. Neither has NVLink or InfiniBand to another GPU, so once a workload needs more throughput than one box can push through its own memory bus, the next step is a cloud instance, not a second desktop unit.

For models in the 141GB-180GB range, or workloads that need FP4 throughput at scale, our H200 vs B200 vs GB200 comparison breaks down the specs and cost-per-token tradeoffs between those three tiers in detail. GB200, GB300, and R100 are reservation-only products on Spheron's pricing page, not on-demand or spot rentals, so check Spheron's pricing page directly for rack-scale availability rather than expecting an hourly rate. For the step-by-step path from local DGX Spark development to a production cloud deployment, including the Docker and vLLM setup to carry over, the DGX Spark to GPU cloud pipeline guide covers it directly.

Renting doesn't replace either box for what it's actually for: offline, private, always-on desk-side inference with no meter running is the entire point of owning a DGX Spark or a Mac Studio in the first place. And Spheron's spot instances can be reclaimed without notice, which matters if you're running an unattended overnight fine-tuning job rather than an interactive session, the exact kind of job a DGX Spark handles well locally. Buying stays the better call below the break-even point this post's table lays out; renting is what makes sense for the production step after either box runs out of room.

Once a model or a workload outgrows what a DGX Spark or Mac Studio can serve locally, the next step is a GPU with the bandwidth and NVLink neither desktop box has.

Check H200 availability on Spheron →

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 09 Oct 2026; DGX Spark, Mac Studio, and third-party benchmark figures reflect their most recently published sources as of this post's date and may have changed. Check current GPU pricing on Spheron's pricing page for live rates.