Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
NVIDIA has announced a 64GB unified-memory version of its DGX Spark desktop AI system. The configuration uses the same GB10 Grace Blackwell Superchip and DGX software stack as the 128GB model, but is sold exclusively through OEM partners. NVIDIA says partner systems will start at $4,999 and are scheduled to become available on October 23, 2026.
What is the 64GB NVIDIA DGX Spark?
It is a new, lower-memory DGX Spark configuration for developers who want to run AI models locally but do not need the 128GB version. NVIDIA says the 64GB system retains the GB10 Grace Blackwell Superchip, DGX OS and the same AI software stack used by the 128GB model.
This is a distinct partner-built configuration, not a 64GB Founders Edition that NVIDIA is selling directly. Acer, ASUS, Dell, Gigabyte, HP and MSI are the named manufacturers.
Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Price and availability
NVIDIA’s announced starting price is $4,999. Availability is scheduled for Friday, October 23, 2026. Those are announcement figures, not verified retail conditions: NVIDIA has not confirmed stock, regional pricing or the final specification of every model from each OEM.
#1 Best Overall
The $4,999 figure should not be confused with the Founders Edition price. NVIDIA said that edition’s MSRP was raised from $3,999 to $4,699 in February 2026 because of worldwide memory supply constraints. That adjustment does not establish a previous price for the new 64GB partner configuration.
What can one 64GB system run?
NVIDIA says a single 64GB DGX Spark can support models with up to 100 billion parameters on-device. That is a vendor capacity claim, not a guarantee that every 100-billion-parameter model will fit or run quickly.
Rank #2
- 900-5G172-2260-000
Actual requirements vary with quantization, context length, framework overhead, model architecture and workload. The announcement provides no independent performance test of the new 64GB configuration, so buyers should treat the model-size figure as an upper-bound positioning statement rather than a measured benchmark.
Can two DGX Spark systems combine their memory?
Yes. NVIDIA says two systems can pool 128GB through ConnectX-7 networking and Sync Cluster Assistant, enabling workloads of up to 200 billion parameters. NVIDIA also reports up to 1.7× performance for a specific two-system Qwen 3.8 27B test compared with one system.
Rank #3
- Extreme AI Performance: Powered by NVIDIA GB10 Grace Blackwell Superchip delivering 1 petaFLOP of AI performance and 128GB memory for 200B model fine-tuning.
- Developer-Optimized Platform: Designed for AI developers building secure, long-running agentic workflows, with compatibility across frameworks such as OpenClaw and NemoClaw, supporting private on-device inference, sandboxed execution, and governed data access.
- Scalable Architecture: Featuring NVIDIA NVLink-C2C for ultra-fast CPU-GPU memory communication and NVIDIA ConnectX-7 networking to support dual GX10 system stacking, unlocking superior scalability and performance.
- Advanced Thermal Design: Engineered cooling ensures sustained high performance and reliability in an ultra-small form factor.
- Full Stack AI Solution: The GB10 and NVIDIA AI software stack provide a full stack solution for AI development and deployment.
That 1.7× result is NVIDIA’s own test claim. It is not an independent evaluation, and performance will depend on the model, software, communication overhead and workload. Buying a second unit also means paying for another complete system; the announcement does not state a discounted cluster price.
64GB versus 128GB DGX Spark
| Item | 64GB configuration | 128GB configuration |
|---|---|---|
| Unified memory | 64GB | 128GB LPDDR5x |
| Chip and software | GB10 Grace Blackwell Superchip; DGX OS and NVIDIA AI software stack, according to NVIDIA | GB10 Grace Blackwell Superchip; the same software stack is described for the two configurations |
| NVIDIA’s stated model capacity | Up to 100-billion-parameter models on one system | Not separately stated in the 64GB announcement |
| Two-system scale-up | Two systems pool 128GB; NVIDIA says workloads up to 200 billion parameters | Two systems would provide 256GB of physical unified memory, but a corresponding capacity claim is not stated |
| Memory bandwidth | Not stated for OEM systems | 273 GB/s in NVIDIA’s documented 128GB system |
| Storage | OEM-specific; not stated | 1TB or 4TB NVMe in the documented system |
| Networking and power | OEM-specific; not stated | Wi-Fi 7, 10 GbE, ConnectX-7 and a 240W power supply in the documented system |
| 販売 channel | Exclusively through Acer, ASUS, Dell, Gigabyte, HP and MSI systems named by NVIDIA | Availability and edition depend on the product being purchased |
The 128GB specifications in the table come from NVIDIA’s hardware documentation and should not be assumed for every 64GB OEM model. In particular, storage capacity, ports, wireless hardware, networking and power supply may differ by manufacturer and SKU.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who should consider the 64GB model?
It makes sense when
- You need local inference or development and your target models fit within a 64GB unified-memory budget.
- You prefer a smaller initial purchase than a 128GB system and can accept OEM-specific configurations.
- You may add a second node later for distributed workloads.
Check carefully before ordering when
- Your model regularly exceeds 64GB after weights, runtime overhead and context are included.
- You require a specific SSD size, port layout, network adapter or power configuration.
- You need independently measured performance; no such test is provided for this new configuration in the announcement.
What to verify at launch
- Confirm that the listing is a 64GB DGX Spark configuration rather than a 128GB model or another workstation.
- Check the exact OEM SKU, region, memory, SSD capacity, ports, networking and included power supply.
- Verify the final price, taxes, shipping and actual stock on October 23, 2026 or later.
- Match your intended model’s quantization, context length and software requirements to the available memory before buying.
- If planning a two-node setup, confirm ConnectX-7 hardware, Sync Cluster Assistant support and the network equipment required for the cluster.
Bottom line
The 64GB DGX Spark gives local-AI developers a lower-capacity entry point while keeping NVIDIA’s GB10 platform and software stack. NVIDIA’s $4,999 starting price and October 23 availability are announced targets, and the practical value will depend on the final OEM specifications and whether 64GB is enough for your models. Treat the 100-billion-parameter capacity and 1.7× cluster result as NVIDIA claims, not independent performance guarantees.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
