Quick Answer: The best DGX Spark alternative depends on why you wanted a Spark. If you need NVIDIA’s CUDA stack, buy the same GB10 chip in a cheaper partner box such as the ASUS Ascent GX10, which was listed at about $6,000 with 1 TB while the 128 GB Founders Edition rose to about $6,950 in October 2026 (ServeTheHome). If you just want to run big models fast, the Mac Studio M5 Max with 128 GB is cheaper at $5,099, has 614 GB/s of memory bandwidth versus the Spark’s 273 GB/s, and tbreak measured it at 75.65 tokens per second on gpt-oss-120b against 44.06 for a GB10 box. The cheapest route to 128 GB is a Strix Halo mini PC such as the GMKtec EVO-X2 at about $3,500. For models that fit in 24 to 48 GB, a discrete GPU is faster than all of them.
The DGX Spark was a $3,999 box when it launched in October 2025. NVIDIA raised it to $4,699 in February 2026, citing “worldwide constraints in memory supply” on its developer forum, and ServeTheHome reported the 128 GB model at about $6,950 on October 3, 2026. At that price it is worth checking what else puts 128 GB of model memory on a desk. This guide compares six alternatives on the four numbers that decide a local-AI box: memory capacity, memory bandwidth, CUDA support and price. We don’t test hardware ourselves. Speed figures below come from the publications and projects named next to them.
The alternatives at a glance
| Option | Model memory | Memory bandwidth | CUDA | Price found (source, date) | Best for |
|---|---|---|---|---|---|
| NVIDIA DGX Spark FE (reference) | 128 GB LPDDR5X | 273 GB/s | Yes | About $6,950 with 4 TB (ServeTheHome, Oct 3 2026; $3,999 at launch) | The baseline |
| ASUS Ascent GX10 (1 TB) | 128 GB LPDDR5X | 273 GB/s | Yes | About $6,000 (ASUS eShop price posted on NVIDIA's developer forum, Sep 2026) | Cheapest 128 GB GB10 |
| Dell Pro Max with GB10 | 128 GB LPDDR5X | 273 GB/s | Yes | $7,964 with 2 TB (dell.com configurator, Nov 4 2026) | Best-cooled GB10 |
| Mac Studio M5 Max, 40-core GPU, 128 GB | 128 GB unified | 614 GB/s | No (MLX, Metal) | $5,099 with 512 GB SSD (Apple pricing, Aug 2026) | Best overall |
| Mac Studio M5 Ultra, 256 GB | 256 GB unified | 1,200 GB/s | No (MLX, Metal) | $9,499 (Apple pricing, Aug 2026) | Models bigger than 128 GB |
| GMKtec EVO-X2 (Strix Halo, 1 TB) | 128 GB LPDDR5X-8000 | 256 GB/s | No (ROCm, Vulkan) | $3,499.99 (ComputingForGeeks, Oct 22 2026) | Cheapest 128 GB box |
| Framework Desktop (Strix Halo) | 128 GB LPDDR5X-8000 | 256 GB/s | No (ROCm, Vulkan) | $3,449 without SSD, pre-order (ComputingForGeeks, Oct 22 2026) | Best-built Strix Halo |
| 2× used RTX 3090 | 48 GB VRAM | 936 GB/s per card | Yes | About $2,710 for two cards (GPU Poet eBay average, Sep 2026), plus a host PC | Fastest under 48 GB |
| RTX 5090 | 32 GB VRAM | 1,792 GB/s | Yes | From $6,919.99 new (Newegg listings via Wccftech, Nov 3 2026) | Speed on models under 32 GB |
| RTX PRO 6000 Blackwell | 96 GB VRAM | 1,792 GB/s | Yes | About $16,000 new (NVIDIA Marketplace and Newegg, Nov 2026); 12-month median $10,895 (GPUPrix) | Fastest single box for 70B to 120B |
Two numbers in that table change the usual advice. A new RTX 5090 now costs about as much as a whole 128 GB DGX Spark, so “just buy a GPU instead” is no longer the cheap answer it was in 2025. And Apple’s August 2026 Mac Studio refresh means a 128 GB Mac with 2.2 times the Spark’s bandwidth costs about $1,850 less than the Founders Edition.
What the published benchmarks show
Generation speed on a local model is limited mostly by memory bandwidth, because every new token reads the model’s active weights from memory. The table below collects published gpt-oss-120b results. The tools and settings differ between sources, so compare systems within the same row’s source rather than across rows.
| System | gpt-oss-120b generation speed | Source (date, software) |
|---|---|---|
| DGX Spark | 58.7 tokens/s (42.8 at 32K context); prompt processing 2,444 tokens/s | llama.cpp official DGX Spark benchmarks (ggml-org, updated Feb 5 2026) |
| DGX Spark vs Strix Halo | 48.5 vs 51.8 tokens/s at short context; 34.5 vs 19.8 tokens/s at 32K context | Level1Techs (Nov 16 2025, llama.cpp) |
| Mac Studio M5 Max, 128 GB vs ASUS GX10 (GB10) | 75.65 vs 44.06 tokens/s; a 32 GB RTX 5090 could not load the model | tbreak (Sep 21 2026, Ollama; GX10 and 5090 figures from the reviewer's earlier PCMag testing on the June 2026 DGX OS build) |
| Beelink GTR9 Pro (Strix Halo) | 31.41 tokens/s | ServeTheHome (Oct 11 2025, LM Studio) |
| RTX PRO 6000 Blackwell | 196.3 tokens/s; prompt processing 5,518 tokens/s | llama.cpp discussion #15396 (Aug 18 2025) |
Three conclusions hold across the sources. First, on short prompts a Strix Halo box keeps up with the Spark on gpt-oss-120b, a mixture-of-experts model that reads only about 5 billion active parameters per token. Second, the Spark pulls ahead on long context and on prompt processing, where its Blackwell GPU does more compute: Level1Techs measured it at 34.5 tokens per second at 32K context against 19.8 for Strix Halo. Third, dense 70B models are slow on every 128 GB box. LMSYS measured 2.7 tokens per second for Llama 3.1 70B in FP8 on the DGX Spark, and ServeTheHome measured 4.89 tokens per second for Llama 3.3 70B on the Beelink GTR9 Pro.
What fits in each memory size
The memory size decides which models load at all. At 4-bit quantization, plan for roughly 0.5 to 0.6 GB per billion parameters plus a few GB for context.
| Model | Approx. memory at 4-bit | 64 GB box | 128 GB box (Spark, Strix Halo, M5 Max) | 256 GB M5 Ultra |
|---|---|---|---|---|
| Qwen3 32B | 20–24 GB | Yes | Yes | Yes |
| Llama 3.3 70B | 40–48 GB | Yes, short context | Yes | Yes |
| gpt-oss-120b (MXFP4) | About 61–65 GB | No | Yes | Yes |
| Qwen3 235B-A22B | About 130–140 GB | No | No (needs two linked Sparks) | Yes |
| Llama 3.1 405B | About 200–245 GB | No | No (two linked Sparks, per NVIDIA) | Very tight; 512 GB M5 Ultra |
1. Mac Studio M5 Max (128 GB) — Best DGX Spark Alternative Overall
Apple Mac Studio (M5 Max, 40-core GPU, 128 GB)
- Same 128 GB of model memory as the Spark, with 614 GB/s of bandwidth instead of 273 GB/s.
- tbreak measured 75.65 tokens per second on gpt-oss-120b with Ollama, against 44.06 in the reviewer's earlier test of the GB10-based ASUS GX10.
- $5,099 at Apple's prices, about $1,850 less than the 128 GB Founders Edition.
- Choose the 40-core GPU version, which Apple rates at 614 GB/s; the 32-core M5 Max is rated at 460 GB/s.
Apple replaced the M4 Max and M3 Ultra Mac Studios with M5 Max and M5 Ultra models on August 25, 2026. According to Apple’s spec page, the M5 Max takes 36 to 128 GB of unified memory and reaches 614 GB/s with the 40-core GPU. That is the rare alternative that is both cheaper and faster than the thing it replaces. The catch is software. There is no CUDA on a Mac, so you run models through MLX, llama.cpp, Ollama or LM Studio, and CUDA-only training or research code will not run. If your goal is to chat with, code with or serve open-weight models, that rarely matters. See our best Mac for local LLMs guide for the full M5 lineup.
2. ASUS Ascent GX10 — Best Same-Chip Alternative (Keeps CUDA)
ASUS Ascent GX10 (GB10, 128 GB, 1 TB)
- Identical GB10 Grace Blackwell chip, 128 GB LPDDR5X at 273 GB/s, DGX OS and ConnectX-7 for linking two units.
- The 1 TB version is the cheapest 128 GB GB10: about $6,000 against about $6,950 for the 4 TB Founders Edition.
- Check the seller's warranty and return terms before you buy: GB10 partners differ on both.
Every GB10 box runs models at the same speed because the chip, memory and bandwidth are identical. StorageReview compared five of them in March 2026 (Dell, the Founders Edition, ASUS, Acer and Gigabyte) and concluded that “differences are found in implementation choices, not raw processing power.” So buy on storage, cooling, warranty and price. The 1 TB ASUS is the cheapest way into the CUDA ecosystem with 128 GB. Models are big, so add an external NVMe drive or a NAS if you plan to keep many of them.
Other GB10 twins. The Dell Pro Max with GB10 costs more ($7,964 with a 2 TB SSD on dell.com on November 4, 2026), but Jeff Geerling found its front-to-back airflow and 280 W power adapter fix the Founders Edition’s heat and noise complaints. The Gigabyte AI TOP ATOM was $7,999 with 4 TB at CyberPowerPC on November 4, 2026. MSI (EdgeXpert), Lenovo (ThinkStation PGX), HP (ZGX Nano G1n) and Acer (Veriton GN100) sell the same platform; prices vary by storage and retailer.
3. GMKtec EVO-X2 or Framework Desktop — Cheapest 128 GB Alternative
AMD Ryzen AI Max+ 395 "Strix Halo" mini PC (128 GB)
- 128 GB LPDDR5X-8000 on a 256-bit bus, 256 GB/s: about 94% of the Spark's bandwidth for about half its price.
- GMKtec EVO-X2 128 GB/1 TB at $3,499.99; Framework Desktop 128 GB at $3,449 without an SSD (ComputingForGeeks, Oct 22 2026).
- The GPU can address about 96 GB on Windows and up to about 120 GB on Linux with a kernel setting change.
- Runs Windows or Linux, so it doubles as a normal PC.
Strix Halo is the value answer to the Spark. Level1Techs measured a Strix Halo system slightly ahead of the Spark on gpt-oss-120b at short context (51.8 versus 48.5 tokens per second). It fell well behind at 32K context and on prompt processing, so long documents and big codebases feel slower. Software runs through ROCm or Vulkan rather than CUDA, which is fine for llama.cpp, Ollama and LM Studio and more work for training code. Framework’s 128 GB Desktop launched at $1,999 and PCWorld reported a rise to $2,459 in January 2026, so these prices have climbed with memory costs too. The Beelink GTR9 Pro ($4,349 with 2 TB) and Corsair AI Workstation 300 ($4,399.99 with 1 TB on corsair.com, November 4, 2026) use the same chip at higher prices. Our best mini PC for local LLMs guide covers the Strix Halo boxes in more detail.
4. Mac Studio M5 Ultra (256 GB) — Best Alternative to Two Linked Sparks
Apple Mac Studio (M5 Ultra, 256 GB)
- 256 GB of unified memory at 1.2 TB/s, which Apple configures as 96, 256 or 512 GB.
- $9,499 at Apple's prices versus about $13,900 for two 128 GB Sparks at $6,950 each.
- Runs 200B-class mixture-of-experts models such as Qwen3 235B-A22B in one quiet box.
- A 512 GB version has been orderable since October 28, 2026, from $22,299 (MacRumors).
NVIDIA’s own pitch for the Spark includes linking two units over ConnectX-7 for models up to 405 billion parameters. If running those models is the goal, rather than developing distributed CUDA software, one M5 Ultra does it with more bandwidth and for about $4,400 less. If you need the multi-node NVIDIA software stack, the two-Spark cluster is the only option here that gives you that.
5. Two Used RTX 3090s — Fastest for Models Under 48 GB
2× NVIDIA GeForce RTX 3090 (24 GB each, used)
- 48 GB of VRAM in total, with 936 GB/s per card: more than three times the Spark's bandwidth.
- Runs Llama 3.3 70B at 4-bit split across both cards, with full CUDA support.
- About $2,710 for the pair (GPU Poet eBay average, September 2026), plus a desktop with room and power for two 350 W cards.
- Cannot load gpt-oss-120b or anything above 48 GB.
A discrete GPU is the opposite trade to the Spark: much less memory, much more bandwidth. LMSYS measured gpt-oss-20b at 205 tokens per second on an RTX 5090 against 49.7 on the DGX Spark, which shows how large the gap is on a model that fits. With new 5090s listed from $6,919.99 in November 2026, two used 3090s are now the sensible way to get CUDA speed on 32B to 70B models. They draw far more power and make far more noise than any box above. Our best GPU for local LLMs guide covers the used-card risks.
6. RTX PRO 6000 Blackwell (96 GB) — Fastest Single-Card Alternative
NVIDIA RTX PRO 6000 Blackwell (96 GB)
- 96 GB of GDDR7 at 1,792 GB/s holds gpt-oss-120b and 70B models in one card.
- 196.3 tokens per second on gpt-oss-120b in a llama.cpp community benchmark, more than three times the Spark's published figure.
- About $16,000 new in November 2026, and it needs a workstation with a 600 W power budget.
This is the alternative for people who bought a Spark for development and found it too slow for real work. It costs more than two Sparks, but it is in a different speed class. See our best GPU for AI guide for how it compares on price per GB of VRAM.
Also considered
- 64 GB DGX Spark (partner models only). Launched October 23, 2026 from $4,999 through Acer, ASUS, Dell, Gigabyte, HP and MSI, according to ServeTheHome. It is more expensive than the original 128 GB Spark at launch and too small for gpt-oss-120b.
- Mac mini M5 Pro (64 GB). Up to 64 GB at 307 GB/s for about $2,700. That is more bandwidth than the Spark at less than half the price, if 70B at short context is your ceiling.
- Framework Desktop with Ryzen AI Max+ PRO 495. 192 GB of LPDDR5X-8533 from $6,799 for the DIY Edition; pre-orders opened September 30, 2026 with first shipments planned for November (Phoronix). Too new for published benchmarks.
- NVIDIA RTX Spark laptops. Windows-on-Arm laptops with up to 128 GB of unified memory, on sale since October 16, 2026 from $2,599 according to NVIDIA. NVIDIA quotes the starting price and the 128 GB maximum separately, so check the price of the memory configuration you need.
- DGX Station GB300. The Spark’s big brother, with 748 GB of combined memory, listed at $99,999 for the ASUS version at Central Computers (Notebookcheck). Out of scope for a desk-side hobby budget.
How to choose
- Need CUDA and 128 GB? Buy a GB10 twin. The cheapest is usually the 1 TB ASUS Ascent GX10; the Dell runs coolest.
- Need to run big models quickly, CUDA optional? Buy the Mac Studio M5 Max with 128 GB. It has the most bandwidth per dollar of any 128 GB box.
- Need 128 GB on the smallest budget? Buy a Strix Halo mini PC, and favour mixture-of-experts models, which run far faster than dense models on its 256 GB/s.
- Your models fit in 24 to 48 GB? Buy discrete GPUs. Bandwidth beats capacity whenever the model fits.
- Need more than 128 GB? Buy the Mac Studio M5 Ultra with 256 GB instead of two Sparks, unless multi-node CUDA work is the point.
The bottom line
The DGX Spark is still the cheapest way to get NVIDIA’s full Grace Blackwell software stack on a desk, but at about $6,950 it is no longer the cheapest or fastest way to run large models. For most buyers the Mac Studio M5 Max with 128 GB is the better machine: $5,099, 614 GB/s and the fastest published gpt-oss-120b result of any 128 GB box. If CUDA is non-negotiable, buy the same chip as an ASUS Ascent GX10 with 1 TB of storage and save roughly $950. If price is everything, a 128 GB Strix Halo mini PC at about $3,500 holds the same models at about half the price, just more slowly on long prompts. For a closer look at the original matchup, see our DGX Spark vs Mac Studio comparison.
Sources: NVIDIA DGX Spark product page and developer forum price announcement (Feb 2026); ServeTheHome (Oct 3 2026; Oct 11 2025); dell.com configurator and corsair.com (Nov 4 2026); CyberPowerPC (Nov 4 2026); StorageReview GB10 comparison (Mar 3 2026); Jeff Geerling (Dec 26 2025); Apple Mac Studio and Mac mini spec pages; Notebookcheck and Daring Fireball (Aug 2026); MacRumors (Oct 28 2026); ComputingForGeeks (Oct 22 2026); PCWorld (Jan 13 2026); Phoronix (Sep 2026); llama.cpp DGX Spark benchmarks (Feb 5 2026) and discussion #15396; Level1Techs (Nov 16 2025); tbreak (Sep 21 2026); LMSYS (Oct 13 2025); Wccftech (Nov 3 2026); GPUPrix and Thunder Compute (Nov 2026); GPU Poet (Sep 2026); NVIDIA Newsroom (Oct 16 2026).