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Nvidia announced a 64GB unified-memory version of DGX Spark on October 2, 2026. It keeps the GB10 Grace Blackwell Superchip, DGX OS and Nvidia’s AI software stack, with Acer, ASUS, Dell, Gigabyte, HP and MSI expected to begin offering systems on October 23 at a starting price of $4,999, according to Nvidia.
What the 64GB DGX Spark changes
The new configuration cuts the memory capacity of DGX Spark from the 128GB configuration to 64GB while retaining the same core platform and software environment, Nvidia says. Because the announcement describes a manufacturer-partner product, the exact hardware details of each vendor’s system should be checked in its listing when sales begin.
| # | Preview | Product | Price | |
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Dell NVIDIA Tesla V100 GPU SXM2 32GB NWWWX by DELL | $854.96 | Buy on Amazon |
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Gigabyte NVIDIA GeForce RTX 3060 Gaming OC V2 Graphics Card - 12GB GDDR6, 192-bit, PCI-E 4.0,... | $695.00 | Buy on Amazon |
| announced detail | 64GB DGX Spark |
|---|---|
| Unified memory | 64GB, according to Nvidia |
| Processor platform | GB10 Grace Blackwell Superchip |
| Operating system and software | DGX OS and Nvidia AI software stack |
| Model capacity claim | Up to 100 billion parameters, depending on model and workload |
| Starting price | $4,999, announced by Nvidia |
| Availability | Scheduled for October 23, 2026, through named manufacturer partners |
What it can run—and what the parameter figure does not tell you
Nvidia says one 64GB system supports local models of up to 100 billion parameters. That is a capacity claim, not a promise of a particular generation speed, context length, numerical precision or output quality. Those results vary with the model, quantization, framework, prompt length and workload.
Local development uses
- Running inference without sending prompts or data to a remote service.
- Building and testing local AI agents.
- Fine-tuning models and experimenting with data-science workflows.
- Developing applications intended for edge deployment.
The unified-memory design can let the processor and GPU access the same memory pool, but 64GB is still a finite budget shared by the model, runtime and application. A model advertised as fitting in memory may leave less room for long contexts, multiple concurrent requests or other processes.
#1 Best Overall
- GPU Chipset: NVIDIA
- Memory: HBM2
- Programming Interface: CUDA
- Memory Capacity: 32GB
- Slot Compatibility: SXM2
Scaling to 128GB with two systems
Nvidia says two 64GB units can be connected over a 200 GbE fabric using its Sync Cluster Assistant. The company says the systems can be linked directly with a QSFP cable; Sync detects the connected units and configures the ConnectX-7 networking. In Nvidia’s description, the pair pools memory to 128GB and expands supported model sizes to as much as 200 billion parameters.
This is a two-system purchase, not a free upgrade to a single 128GB machine. It also requires the networking setup and software configuration Nvidia describes. Nvidia reports up to 1.7× performance for two systems versus one in a Qwen 3.8 27B test. That is a vendor result for the named model and setup; it should not be treated as a general result for every model or workload.
Rank #2
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- Core Clock: 1837MHz
- WINDFORCE 3X Cooler
How it compares with the 128GB configuration
Nvidia’s public product page and hardware guide list detailed specifications for a 128GB DGX Spark, including 4TB NVMe storage, 273GB/s memory bandwidth, ConnectX-7 networking, Wi‑Fi 7 and up to 1 PFLOP FP4 performance. Those figures are explicitly for the 128GB system. The announcement does not establish that every one applies to the 64GB SKU, so buyers should verify storage, bandwidth, networking, wireless and performance specifications with the specific OEM listing.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Decision factor | What to verify before buying the 64GB model |
|---|---|
| Memory | 64GB unified memory and the amount available after system overhead |
| Workload fit | Your model’s actual memory, context and concurrency requirements |
| Software | DGX OS version, CUDA/framework support and vendor-specific configuration |
| Performance | Measured results on your intended model, not only Nvidia’s Qwen 3.8 27B result |
| Scaling | Whether a second unit, 200 GbE connection and Sync Cluster Assistant fit your budget and environment |
| Hardware | Storage, ports, wireless, power and physical dimensions for the exact partner system |
| Price and availability | Final OEM price, configuration and stock after the planned October 23 launch |
Who should consider it
Good fit
- Developers who need a local Nvidia environment for large-model inference or agent development.
- Teams whose models fit within 64GB and who value keeping data on a local workstation.
- Buyers who may later deploy a second unit for larger models or distributed workloads.
Reasons to wait or choose another system
- You need independently measured performance before committing to a new configuration.
- Your workload requires more than 64GB once context, batching and other processes are included.
- You need confirmed storage, connectivity or chassis specifications that Nvidia has only published for the 128GB system.
- The $4,999 starting price exceeds your budget; it is a relative reduction within the DGX Spark range, not an indication that the system is inexpensive for every buyer.
What to check when partner listings appear
- Confirm that the listing is the 64GB unified-memory configuration, not the 128GB model.
- Check the final price, included storage, networking, wireless features, power requirements and warranty.
- Match the system’s memory capacity to the exact models, quantization, context lengths and concurrency you plan to use.
- If you may cluster two units, confirm QSFP and 200 GbE compatibility and the supported Sync Cluster Assistant software.
- Look for workload-specific benchmarks or test the model yourself; do not extrapolate Nvidia’s 1.7× Qwen result to unrelated applications.
The Bottom Line
The 64GB DGX Spark gives local-AI developers a lower entry point than the 128GB configuration, with Nvidia claiming support for models up to 100 billion parameters and a path to 128GB pooled memory through two systems. Its $4,999 starting price and October 23, 2026 availability are announced figures, while partner-specific specifications and real-world performance still need verification.
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.
