NVIDIA DGX Spark 64GB starts at $4,999 in the United States. The new configuration became available through Acer, ASUS, Dell, Gigabyte, HP, and MSI on October 23, 2026. It combines the GB10 Grace Blackwell Superchip with 64GB of unified memory, giving developers a lower-priced entry point into NVIDIA’s desktop AI platform. However, its 100-billion-parameter model claim needs some context before you decide to buy.
If you’re considering this compact AI supercomputer, the biggest question is whether 64GB is enough for your workloads. The answer depends on model size, quantization, context length, and the applications you plan to run. This guide examines the price, specifications, manufacturer availability, local AI capabilities, and limitations of the new configuration.
Pricing and product information checked November 9, 2026. Availability and retailer prices can change.
Affiliate disclosure: This article may contain affiliate links in future updates. If you purchase through an eligible link, MetaDictory may earn a commission at no additional cost to you. No verified affiliate purchase links are included in this edition.
NVIDIA DGX Spark 64GB Price: How Much Does It Cost?
The NVIDIA DGX Spark 64GB has an announced starting price of $4,999 in the United States. NVIDIA introduced this configuration on October 2, 2026, and announced availability through six manufacturing partners beginning October 23.
That starting price gives buyers a lower entry point than the 128GB configuration. However, $4,999 should not be treated as a guaranteed checkout price from every manufacturer.
Official $4,999 starting price
The 64GB model retains the GB10 Grace Blackwell platform and NVIDIA’s AI software ecosystem. The main configuration difference is its unified memory capacity.
Here is what the announced price means for buyers:
- Starting price: $4,999.
- Market: United States.
- Availability date: October 23, 2026.
- Sales channels: Participating OEM manufacturers.
- Configuration: 64GB unified memory.
The important distinction is that NVIDIA announced a starting price rather than one universal retail price for every manufacturer.
NVIDIA DGX Spark 64GB pricing vs actual OEM prices
The actual purchase price may differ depending on the manufacturer, storage configuration, product bundle, warranty and retailer.
Some manufacturers may offer different configurations or regional packages. Buyers should check the complete specification and final checkout price before ordering.
| Pricing factor | What buyers should check |
|---|---|
| Announced starting price | $4,999 |
| OEM configuration | Exact memory and storage |
| Warranty | Included coverage and support |
| Retailer pricing | Actual listed price |
| Taxes | Applicable US sales tax |
| Accessories | Networking cables and other requirements |
For comparison, October 2026 reporting placed the 128GB DGX Spark configuration around $6,950, although OEM prices can vary. Treat that figure as a reported market price, not a universal price guarantee.
Buying insight: A lower starting price does not automatically make the 64GB model the better value. If your applications regularly require more than 64GB of available memory, the additional cost of a larger configuration may be justified.
Where to Buy NVIDIA DGX Spark 64GB in the USA
NVIDIA announced six manufacturing partners for the 64GB configuration: Acer, ASUS, Dell, Gigabyte, HP, and MSI. The systems became available through these partners on October 23, 2026.
NVIDIA’s official product page identifies participating OEM partners and explains that hardware support is provided by the vendor that sells the system. NVIDIA DGX Spark
Acer DGX Spark 64GB availability
Acer is one of the announced manufacturers for the 64GB configuration. Buyers should check Acer’s official product and purchasing channels for the exact model number, storage capacity, warranty and final price.
Official website: Acer US.
ASUS DGX Spark 64GB availability
ASUS is another participating manufacturer. Check that the product listing specifically identifies the DGX Spark 64GB configuration rather than a different NVIDIA-based AI workstation.
Official website: ASUS US.
Dell DGX Spark 64GB availability
Dell is included in NVIDIA’s partner announcement. Enterprise buyers should also review vendor support, warranty options and purchasing terms before placing an order.
Official website: Dell US.
Gigabyte DGX Spark 64GB availability
Gigabyte is a participating manufacturer. Check the exact product identity carefully because Gigabyte also offers other AI-focused desktop systems that are not necessarily DGX Spark configurations.
Official website: Gigabyte US.
HP DGX Spark 64GB availability
HP is among the six OEM partners. Businesses purchasing for development teams should compare support coverage and the configuration offered by their preferred HP sales channel.
Official website: HP US.
MSI DGX Spark 64GB availability
MSI is also included in the announcement. Check the manufacturer’s listing for the exact DGX Spark model and included components.
Official website: MSI US.
Availability note: NVIDIA’s announcement confirms these six partners, but it does not establish that every partner has identical stock, pricing or shipping dates. I could not independently verify live inventory and final checkout prices for all six manufacturers in this research.
CyberGeek DGX Spark 64GB availability
CyberGeek DGX Spark Compact AI Workstation, GB10 Grace Blackwell Superchip, 128GB LPDDR5x Unified Memory, 20-Core Arm CPU, Customized NVMe SSD up to 4TB, AI Development & Fine-Tuning, DGX OS
NVIDIA DGX Spark 64GB Specifications
The DGX Spark 64GB uses NVIDIA’s GB10 Grace Blackwell Superchip. It combines Arm CPU cores, Blackwell GPU technology, unified memory and high-speed networking in a compact desktop system.
Complete specification overview
| Specification | DGX Spark 64GB |
|---|---|
| Architecture | NVIDIA Grace Blackwell |
| Superchip | NVIDIA GB10 |
| CPU | 20-core Arm |
| CPU cores | 10 Cortex-X925 + 10 Cortex-A725 |
| GPU | NVIDIA Blackwell |
| Tensor cores | 5th generation |
| RT cores | 4th generation |
| AI performance | Up to 1 PFLOP FP4 |
| Unified memory | 64GB LPDDR5X |
| Memory interface | 256-bit |
| Memory bandwidth | 273 GB/s |
| Networking | NVIDIA ConnectX-7 |
| Network speed | Up to 200 Gbps |
| Ethernet | 10GbE |
| Wireless | Wi-Fi 7 |
| Bluetooth | 5.4 |
| Storage | M.2 NVMe, up to 4TB |
| Operating system | NVIDIA DGX OS |
| USB | 4 x USB Type-C |
| Display output | HDMI 2.1 |
| AI model capacity | Up to 100B parameters, according to NVIDIA |
Sources: NVIDIA DGX Spark official specifications and DGX Spark hardware documentation.
What does 64GB unified memory mean?
Unlike a conventional desktop with separate system RAM and dedicated graphics memory, DGX Spark uses a unified memory architecture.
The CPU and GPU can work with a shared memory pool. This is useful for local AI workloads because large models need memory for their parameters, intermediate computations, context and other runtime data.
However, 64GB does not mean every application can use the entire capacity for model weights. The operating system, services and inference framework also need memory.
This distinction becomes particularly important when loading large language models.
Why 273 GB/s memory bandwidth matters
Memory bandwidth describes how quickly data can move between memory and the processor.
The DGX Spark’s 273 GB/s bandwidth supports demanding AI workloads, but it should not be confused with model inference speed. Actual token generation depends on model architecture, precision, software optimization, batch size and context length.
A higher memory capacity helps accommodate larger workloads. Higher bandwidth helps move the data required to process them. Both matter, but they solve different problems.
NVIDIA DGX Spark 64GB vs 128GB: Which Is Better?
The biggest difference between the two configurations is memory capacity. Both use the GB10 Grace Blackwell platform, but the 128GB version provides twice the unified memory for larger models and more demanding workloads.
| Feature | DGX Spark 64GB | DGX Spark 128GB |
|---|---|---|
| Unified memory | 64GB | 128GB |
| Memory technology | LPDDR5X | LPDDR5X |
| Memory bandwidth | 273 GB/s | 273 GB/s |
| Processor | GB10 Grace Blackwell | GB10 Grace Blackwell |
| AI performance | Up to 1 PFLOP FP4 | Up to 1 PFLOP FP4 |
| NVIDIA model capacity claim | Up to 100B | Up to 200B |
| Best suited for | Smaller local AI workloads | Larger local AI workloads |
| Starting price | $4,999 | Check current OEM pricing |
Memory and model capacity
The 64GB version targets developers who want to run capable AI models locally without paying for the larger memory configuration.
The 128GB version provides more room for large model weights, longer context windows, concurrent requests and demanding development workloads.
One important point: doubling memory does not automatically double inference performance. Both configurations use the same underlying GB10 platform, and actual performance depends on the workload.
Price and value comparison
The 64GB version has a clear entry-price advantage at $4,999. The 128GB configuration has been reported at approximately $6,950, although buyers should confirm current OEM pricing before making a comparison.
The difference is approximately $1,951 based on those reported prices.
That makes the decision more interesting than simply choosing the cheapest system.
If your workload fits comfortably within 64GB, spending more on the larger configuration may not provide enough additional value. If you regularly work with larger models, the additional memory can be more useful than trying to optimize every workload around a smaller memory pool.
Which configuration should you choose?
| Your workload | Recommended configuration |
|---|---|
| Local AI experimentation | 64GB |
| Coding assistants | 64GB, depending on model |
| Small and medium LLM inference | 64GB |
| Larger LLM inference | 128GB |
| Longer context windows | 128GB |
| Larger model fine-tuning | 128GB, depending on workload |
| Multiple demanding concurrent workloads | 128GB |
These are workload-based recommendations, not guarantees of model compatibility or performance.
Can NVIDIA DGX Spark 64GB Run 100B AI Models?
Yes. NVIDIA advertises support for models with up to 100 billion parameters on one DGX Spark 64GB system. However, this is a maximum model-capacity claim, not a promise that every 100B model will run comfortably or deliver useful inference speeds.
To understand the limitation, you need to consider how much memory model parameters actually require.
Understanding the 100-billion-parameter claim
An AI model’s parameter count describes the number of learned numerical values it contains. Each parameter requires memory, and the amount depends on its numerical precision.
For example, consider a simplified 100-billion-parameter model:
| Precision | Approximate weight memory |
|---|---|
| FP16 | 200GB |
| INT8 | 100GB |
| 8-bit quantization | Approximately 100GB |
| 4-bit quantization | Approximately 50GB |
These figures are theoretical estimates using decimal units. Actual memory requirements can differ because of quantization metadata, model architecture and implementation.
A 100B model stored at 4-bit precision would already use approximately 50GB for its weights. That leaves limited space within a 64GB system for the operating system, inference runtime, context cache and other processes.
This is why quantization matters so much.
How quantization affects model memory
Quantization reduces the numerical precision used to represent model weights.
For example, a model that normally uses 16-bit weights can sometimes be converted to a 4-bit representation. This can significantly reduce its memory requirements.
However, lower precision can also affect model quality and output behavior. The impact varies by model, quantization method and workload.
A practical buyer should evaluate:
- The actual memory footprint of the selected model.
- Whether the model has a supported quantized version.
- The required context length.
- The inference framework’s memory overhead.
- The available memory after the operating system and other services start.
A model that technically loads is not necessarily a model that performs well.
Context length and runtime overhead
Model weights are only one part of the memory requirement.
Inference systems also need memory for the key-value cache, temporary tensors, intermediate operations and other runtime components.
Longer context windows can increase memory consumption substantially. Running multiple requests simultaneously can add further pressure.
For this reason, a 100B model with a small context and optimized quantization may be possible under conditions where the same model with a long context or multiple concurrent requests would not fit.
The practical lesson: Choose DGX Spark 64GB based on the memory requirements of your actual models, not just their advertised parameter counts.
Can DGX Spark 64GB Run Local AI Without Cloud Access?
Yes. NVIDIA designed DGX Spark as a local AI development platform. You can run supported models and inference frameworks directly on the system without sending every request to a cloud AI service.
This is useful for developers working with private documents, internal datasets, coding agents and local AI applications.
Running local LLMs
A local large language model runs on your own hardware rather than depending on a remote inference API.
Potential applications include:
- Private document analysis.
- Local coding assistants.
- Retrieval-augmented generation (RAG).
- AI agent development.
- Local inference experiments.
- Model evaluation and fine-tuning experiments.
The important advantage is control over the computing environment and data flow.
However, local execution does not automatically guarantee privacy. Network connections, application telemetry, remote integrations and data-handling settings still matter.
Ollama, vLLM and other supported frameworks
NVIDIA identifies several inference frameworks for getting started with DGX Spark:
| Framework | Typical purpose |
|---|---|
| Ollama | Running local language models |
| vLLM | Model serving and inference |
| llama.cpp | Efficient local model execution |
| LM Studio | Desktop-based local AI workflows |
NVIDIA’s official getting-started instructions include downloading a supported inference framework and selecting a recommended local model. NVIDIA Blog
Before installing a model, check its hardware requirements and compatibility with the selected framework.
Local AI workflows without cloud dependency
A developer can use DGX Spark as a dedicated inference machine while working from a separate laptop or desktop.
For example, a coding assistant could send inference requests to a model running on DGX Spark over a local network. The development computer handles the editor and other applications, while DGX Spark handles the AI workload.
This arrangement can be useful for teams that want to keep their model infrastructure under their own control.
Limitations of offline AI computing
Local AI is not the same as having access to every cloud AI capability.
You still need:
- Downloaded model files and software.
- Compatible drivers and frameworks.
- Sufficient local storage.
- Appropriate network configuration for other devices.
- Periodic updates and maintenance.
Once the necessary components are installed, many inference workloads can run without an internet connection. Cloud services remain necessary for applications that explicitly depend on remote models, external APIs or online data.
Can Two DGX Spark 64GB Systems Work Together?
Yes. Two DGX Spark 64GB systems can connect through NVIDIA ConnectX-7 networking and NVIDIA Sync Cluster Assistant. NVIDIA states that the configuration provides 128GB of combined memory for supported distributed workloads.
This creates another purchasing option for developers who need more capacity than one 64GB system provides.
NVIDIA Sync Cluster Assistant explained
NVIDIA Sync Cluster Assistant helps configure and manage multi-system DGX Spark workloads.
According to NVIDIA, two systems can connect through their ConnectX-7 networking ports using a QSFP cable. The software helps configure the network and route workloads between the systems.
The main advantage is the ability to scale beyond the memory capacity of one machine.
How two systems provide 128GB of combined memory
Configuration comparison
| Configuration | Combined memory |
|---|---|
| One DGX Spark 64GB | 64GB |
| Two DGX Spark 64GB | 128GB |
| One DGX Spark 128GB | 128GB |
| Four DGX Spark 64GB | 256GB |
NVIDIA’s official documentation also describes configurations with up to four connected DGX Spark systems. A four-system arrangement requires an external network switch, while two or three systems can use direct QSFP connections. NVIDIA DGX Spark
Remember that distributed memory is not identical to one physically unified memory pool inside a single machine. Software must distribute the model and workload across the systems. Networking, communication overhead and workload parallelism affect performance.
What does a two-system setup cost?
Using the announced starting price:
2 X $4,999 = $9,998
That is the starting hardware cost for two 64GB systems, before additional equipment, taxes and other expenses.
A multi-system configuration may be useful if you need distributed inference or larger model capacity. However, it is not automatically cheaper or faster than buying one 128GB system.
200B models and performance limitations
NVIDIA’s product information associates two 64GB systems with up to 200B parameters. This should be understood as a supported model-capacity target, not a guarantee that all 200B models will fit or perform well under every configuration.
The same considerations apply: quantization, context length, model architecture, runtime overhead and distributed execution all influence feasibility.
NVIDIA also reports up to 1.7x performance for two clustered systems compared with one system in its Qwen 3.8 27B test. This is a manufacturer-reported result for a particular workload, not a universal performance improvement. NVIDIA Blog
Why Did NVIDIA Introduce the 64GB DGX Spark?
NVIDIA introduced the 64GB configuration to give developers, researchers and AI enthusiasts another entry point into its desktop AI platform.
The launch also comes during a period of strong demand for AI computing infrastructure and memory. AI data centers need substantial amounts of high-bandwidth memory, and manufacturers face growing pressure to supply components for increasingly demanding systems.
AI hardware costs and memory demand
The 64GB model reduces the memory capacity compared with the original 128GB configuration while retaining the GB10 Grace Blackwell platform and NVIDIA software ecosystem.
This gives buyers a lower starting price without requiring NVIDIA to introduce an entirely different computing architecture.
However, it is important to distinguish market context from proven causation. NVIDIA has not established that consumer RAM shortages directly caused this particular configuration decision.
For a broader understanding of the memory market, read our analysis of why RAM is so expensive in 2026.
What does the new configuration mean for buyers?
The introduction of the 64GB model creates a more accessible entry point for people who do not need 128GB of unified memory.
It also gives developers another way to build local AI infrastructure and expand their capacity through clustering.
The wider memory market remains important when evaluating future hardware costs. See our analysis of whether RAM prices will fall in 2027 for more information about potential supply and pricing developments.
Who Should Buy NVIDIA DGX Spark 64GB?
The DGX Spark 64GB is best suited to developers and technical users who need a dedicated local AI computing system. Its value depends on how frequently you run local models and whether your workloads fit within its memory capacity.
AI developers and software engineers
Developers building coding assistants, AI agents, RAG applications and local inference workflows may benefit from a dedicated AI system.
It can also serve as a separate inference machine for applications running on other computers.
AI researchers and data scientists
Researchers can use the system to experiment with supported models, local inference and fine-tuning workloads.
However, anyone working with large models should calculate memory requirements before choosing the 64GB configuration.
AI startups and independent developers
A dedicated local AI workstation may help small teams experiment with models without depending on a cloud inference service for every development task.
The initial hardware investment is substantial, though. Buyers should compare it with the cost of their expected cloud usage, software requirements, electricity and ongoing maintenance.
Who should consider another configuration?
The 64GB version may not be the right choice if you regularly need:
- Large models with substantial memory requirements.
- Long context windows.
- Multiple concurrent inference workloads.
- Larger fine-tuning workloads.
- More memory headroom for future projects.
In these situations, the 128GB configuration or a multi-system arrangement may be more suitable.
Frequently Asked Questions
1. How much does NVIDIA DGX Spark 64GB cost?
The NVIDIA DGX Spark 64GB has an announced starting price of $4,999 in the United States. It became available through Acer, ASUS, Dell, Gigabyte, HP and MSI on October 23, 2026. Actual retail prices may vary depending on the manufacturer and configuration.
2. Is $4,999 the official DGX Spark 64GB price?
Yes. NVIDIA announced $4,999 as the starting price for its 64GB configuration. It is not a guarantee that every retailer or OEM will sell every configuration at exactly that price.
3. When did DGX Spark 64GB become available?
NVIDIA announced the 64GB configuration on October 2, 2026, with availability beginning October 23, 2026. The six announced manufacturing partners are Acer, ASUS, Dell, Gigabyte, HP and MSI.
4. Can I buy DGX Spark 64GB directly from NVIDIA?
The 64GB configuration is distributed exclusively through participating OEM partners rather than as a standard NVIDIA direct-sale configuration. NVIDIA provides links to authorized purchasing channels through its official product page.
5. Which companies manufacture DGX Spark 64GB?
The six announced manufacturing partners are Acer, ASUS, Dell, Gigabyte, HP and MSI. Buyers should check each manufacturer’s product listings for exact configurations, prices, stock and warranty information.
6. Is DGX Spark 64GB enough for local AI?
It can be suitable for many local AI workloads, including supported language models, coding assistants and AI agent development. However, the available memory limits the size and complexity of practical workloads. Model quantization, context length and runtime overhead also matter.
7. Can DGX Spark 64GB run a 100B parameter LLM?
NVIDIA advertises support for models up to 100 billion parameters on one 64GB system. Actual feasibility depends on quantization, model architecture, context length and inference overhead. The advertised maximum should not be interpreted as a guarantee of comfortable performance for every 100B model.
8. Can two DGX Spark 64GB systems work together?
Yes. NVIDIA Sync Cluster Assistant supports connecting two systems through ConnectX-7 networking. NVIDIA describes the configuration as providing 128GB of combined memory for supported distributed workloads. Actual performance depends on software and workload configuration.
9. Is DGX Spark 64GB upgradeable to 128GB?
Do not assume that the 64GB model can be upgraded by adding memory modules. Its unified memory is part of the GB10 platform architecture. Buyers who need 128GB should consider the 128GB configuration or an officially supported multi-system arrangement.
10. Does DGX Spark require an internet connection?
No permanent internet connection is required for every local inference task. Once the operating system, software and model files are installed, supported local workloads can run offline. Online services remain necessary for applications that depend on external APIs or cloud-based information.
Should You Buy NVIDIA DGX Spark 64GB?
The NVIDIA DGX Spark 64GB offers a $4,999 starting point for developers who want dedicated local AI computing. Its GB10 Grace Blackwell platform, unified memory architecture and ConnectX-7 networking make it an interesting option for local model development and inference.
However, the 100B parameter claim should not be the only reason to buy it. Your actual workload, model precision, context requirements and available memory matter much more than a headline parameter count.
If your projects fit comfortably within 64GB, this configuration deserves consideration. If you regularly work with larger models, compare the 128GB version and multi-system costs before purchasing.
Next step: Check the current configuration and warranty from NVIDIA’s participating OEM partners before committing to the $4,999 starting price.
Sources and further reading
- NVIDIA official announcement of DGX Spark 64GB.
- NVIDIA DGX Spark product specifications.
- NVIDIA DGX Spark hardware documentation.
- StorageReview: DGX Spark 64GB pricing and clustering analysis.
Belayet Hossain is a Senior Systems Analyst and Web Infrastructure Expert with a Master’s in Computer Science & Engineering (CSE). Specializing in the “Meta” of the digital world, he applies his engineering background to rigorously test hosting services, domain strategies, and enterprise tech stacks. Belayet translates technical specs into actionable business intelligence. Connect with Belayet Hossain on Facebook, Twitter, or read more about Belayet Hossain.