NVIDIA announced a 64GB DGX Spark on October 2, 2026 – same GB10 Grace Blackwell Superchip, half the unified memory, starting at $4,999 from October 23 through Acer, ASUS, Dell, Gigabyte, HP and MSI. NVIDIA says one box runs models up to 100 billion parameters, and two boxes linked over ConnectX-7 pool to 128GB for up to 200B-parameter models.
Every other site will repeat that press release. Here is what they skip: the 64GB box costs more per gigabyte than the 128GB box, what actually fits in 64GB in plain terms, and when two small boxes beat one big one. That is what this guide covers.
Source note: specs, prices and the 1.7x cluster claim below come from NVIDIA's announcement, blog and spec page (Oct 2, 2026). They are vendor-reported, not an independent VelsTech benchmark. Street prices move daily in the current memory market – treat them as direction, not quotes.
Background: new to the Spark family? Start with our DGX Spark vs RTX Spark vs Ryzen AI Halo showdown for the 128GB model, then come back – this page is only about what the 64GB version changes.
The short version: DGX Spark 64GB in 60 seconds
- What: same GB10 chip (20-core Arm + Blackwell GPU, 1 PFLOP FP4), same 273 GB/s bandwidth, same DGX OS + NVIDIA AI stack – but 64GB unified LPDDR5X instead of 128GB.
- Price / date: from $4,999, October 23, OEM partners only (no NVIDIA Founders Edition). The 128GB Founders Edition lists at $4,699 but street prices sit around $7,000–9,000.
- Runs: up to ~100B parameters on one box (FP4, vendor claim); up to ~200B on two clustered boxes (128GB pooled).
- Cluster trick: link two 64GB units with one QSFP cable via ConnectX-7 (200 GbE, no switch). NVIDIA's Sync Cluster Assistant sets up the network; two 64GB nodes hit up to 1.7x the speed of one box on Qwen3.8 27B in NVIDIA's test.
- New software: Sync Cluster Assistant (now) + Sync Model Launcher (end of October) – one-click Qwen3.8 27B across one or two nodes, exposed to your laptop, with OpenCode pre-wired.
- Catch: $78/GB vs $37/GB on the 128GB list price. You pay less total, more per gigabyte. Fine-tuning headroom also halves.
What the 64GB version actually is
Think of it as the same computer with smaller RAM sticks. The processor, networking, ports, size (150 × 150 × 50.5 mm, 1.2 kg), 240W power brick, Wi-Fi 7, 10 GbE, HDMI 2.1a and DGX OS are unchanged. Only the unified-memory capacity drops from 128GB to 64GB, and only the partner-built boxes (Acer, ASUS, Dell, Gigabyte, HP, MSI) sell it. Storage on partner SKUs varies – check the exact listing instead of assuming the Founders Edition's 4TB drive.
Why now? Two forces met: dense 27–35B models like Qwen3.8 27B got good enough that many developers no longer need 128GB for inference, and LPDDR5X prices exploded (the “RAMpocalypse”). A smaller-memory SKU lowers the ticket price even though memory itself got more expensive.
64GB vs 128GB: side by side
| Spec | DGX Spark 64GB (new) | DGX Spark 128GB |
|---|---|---|
| Chip | GB10 Grace Blackwell (20-core Arm + Blackwell GPU) | Same GB10 |
| AI compute | Up to 1 PFLOP FP4 | Up to 1 PFLOP FP4 |
| Unified memory | 64GB LPDDR5X, 256-bit, 273 GB/s | 128GB LPDDR5X, 256-bit, 273 GB/s |
| Vendor model ceiling | Up to 100B params (1 box) | Up to 200B params (1 box) |
| Fine-tuning (rule of thumb) | ~30B class comfortably | Up to 70B (NVIDIA guidance) |
| Networking | ConnectX-7 200 Gbps | ConnectX-7 200 Gbps |
| 2-unit cluster | 128GB pooled, up to 200B params | 256GB pooled, up to 400B params |
| OS / stack | DGX OS + CUDA-X, NIM, NeMo, Agent Toolkit, Ollama / vLLM / llama.cpp / LM Studio | Same |
| Sold by | OEM partners only | NVIDIA + partners |
| List price | From $4,999 | $4,699 (Founders; street $7,000–9,000) |
| Available | October 23, 2026 | Since Oct 2025 |
The fine-tuning row needs honesty: NVIDIA only rates fine-tuning to 70B on the 128GB box. Halve the memory and the comfortable fine-tune ceiling roughly halves too. Inference is the 64GB box's job; serious fine-tuning still wants the big box or the cloud.
What actually fits in 64GB?
Forget the “100B” headline for a second – that assumes FP4 and a short context. Here is the practical translation for the formats people actually download:
- 27–35B dense models (Qwen3.8 27B, similar): the sweet spot. ~16–20GB at Q4 plus plenty left for 32–128K context and an agent loop. This is why NVIDIA demos Qwen3.8 27B.
- 70B at Q4 (~40GB weights): fits with room for a useful KV cache. Tight but workable – the reason one 64GB box still matters in 2026.
- 100B at FP4: fits on paper per NVIDIA's ceiling, but context and overhead leave almost no slack. Possible, not comfortable.
- 200B+: needs two boxes. That is the cluster story below, not a single 64GB unit.
Not sure how weights + KV cache + context add up on your own card? Run your numbers through our LLM VRAM calculator and the how-much-VRAM guide – same math, smaller box.
The price catch nobody puts in the headline
$4,999 sounds cheaper than $7,000–9,000 street – and it is, in total cash. Per gigabyte it goes the other way:
- 64GB at $4,999 ≈ $78 per GB of unified memory.
- 128GB at $4,699 list ≈ $37 per GB. Even at $7,000 street that is ~$55/GB – still cheaper per GB than the new box.
The original 128GB Founders Edition launched at $3,999, rose to $4,699 in February 2026 on memory costs, then vanished into $7,000–9,000 street pricing. The 64GB SKU at $4,999 therefore starts above the original 128GB MSRP and even above today's 128GB list – you pay the RAMpocalypse tax either way. Buy the 64GB box to spend less total, not to get a better deal per gigabyte.
Clustering: two small boxes beat one big box (sometimes)
Every Spark ships with a ConnectX-7 NIC. Plug two 64GB units together with a single QSFP cable – no switch – and the Sync Cluster Assistant (inside the NVIDIA Sync app) discovers the peer, validates SSH / OS / GB10 hardware, configures the 200 GbE fabric and sets up key-based SSH between nodes. Same software image on both nodes, so a workload moves from one box to two without rebuilding the environment. Up to four Sparks can cluster (four needs a switch); playbooks cover NCCL, vLLM inference and PyTorch fine-tuning across nodes.
- 1× 64GB: 64GB pooled – up to ~100B params.
- 2× 64GB: 128GB pooled – up to ~200B params, ~2× bandwidth, up to 1.7x throughput vs one box on Qwen3.8 27B (NVIDIA internal test, not independent).
- 2× 128GB: 256GB pooled – up to ~400B params.
- 4× 128GB: 512GB pooled – up to ~700B params (NVIDIA scale-out chart).
The 1.7x number is the most misunderstood line in the launch. It is Qwen3.8 27B-specific, vendor-measured, and compares two nodes against one – not a general “clusters are 70% faster” promise. Expect the gain on compute-bound inference; memory-bound long-context runs scale differently. And a second box means buying, powering and housing a second full computer, not adding a RAM stick.
The second half of the software story lands end of October: Sync Model Launcher. Pick Qwen3.8 27B, click through, and it downloads, shards across one or two nodes and exposes an endpoint to laptops on your network – plus wires up OpenCode in the browser so you can code against the local model immediately. Supported runtimes include llama.cpp, Ollama, vLLM and LM Studio; agent starting points include NemoClaw, OpenClaw, Hermes Agent and OpenShell playbooks on build.nvidia.com.
The India angle
No India price or date yet – the six partners are global names, not a local stock list. For planning, convert conservatively:
- 64GB at $4,999 ≈ ₹4.4 lakh before shipping, GST and duty – realistically ₹5–5.5 lakh landed if partners bring it in.
- 128GB street $7,000–9,000 ≈ ₹6.2–8 lakh landed – well above the $4,699 list.
- For comparison, a used 24GB RTX 3090 sits around ₹50,000–60,000 in India but cannot hold a 70B model at Q4, while a Ryzen AI Max mini PC (~₹70k–1.1 lakh) holds 128GB unified but runs inference slower than Blackwell.
If you do import, budget for the QSFP cable and a second power drop if you plan to cluster later – the “add a second box later” path is the 64GB SKU's best feature, but only if you can actually buy that second box locally.
Who should buy it – and who should skip it
- Buy if: you run agents / inference on 27–70B models privately, want the GB10 + CUDA + DGX OS stack without paying street prices for 128GB, or want a cluster-on-ramp (start with one, add a second to reach 128GB pooled).
- Skip if: you fine-tune 70B models, need 200B on one box, or expected “half the RAM = half the price.” At $78/GB it is half the memory for more than half the original MSRP – the savings are versus today's inflated street, not versus the original deal.
- Consider instead: a 128GB partner box if you find one near list; a 24GB GPU + CPU offload for occasional 70B work; or a Ryzen AI Max mini PC for the cheapest unified-memory entry (see our three-way showdown).
What this means if you run local AI
From someone who benchmarks open models on a 12GB RX 6800M weekly: the 64GB Spark validates what local-AI builders already learned – dense 27B-class models are the new default, and 64GB is the new comfortable minimum for agent work with long context. Nothing here makes a home GPU obsolete; a Q4 27B still flies on 12–16GB. What changes is the ceiling: a quiet desktop box now holds 70B at Q4 without cloud, and two boxes hold 200B without re-platforming. Price per gigabyte got worse, but the entry ticket got smaller – and the one-click Model Launcher finally fixes the “great hardware, painful setup” complaint that kept Sparks in labs instead of on desks.
Bottom line
The 64GB DGX Spark is not a budget Spark – it is a smaller Spark for a memory-short market: same chip and stack, half the headroom, lower total but higher per-GB price, with a genuinely simple cluster path when you outgrow it. If your models fit in 64GB (most 27–70B agent setups do), it is the cheapest on-ramp to GB10 computing. If they do not, two 64GB boxes at ~$10,000 to match one 128GB box's memory is an expensive way to re-buy what you could have had – wait for 128GB stock near list instead.
Sources
- NVIDIA Blog: DGX Spark 64GB + Sync Cluster Assistant (Oct 2, 2026)
- NVIDIA: DGX Spark product + specs (64GB / 128GB)
- NVIDIA Docs: Sync Cluster Assistant guide
- Tom's Hardware: 64GB Spark in the RAMpocalypse (Oct 2, 2026)
- StorageReview: Oct 23, $4,999, 2-unit cluster detail (Oct 2, 2026)
- VideoCardz: price vs original 128GB MSRP
- Our DGX Spark vs RTX Spark vs Ryzen AI Halo showdown
FAQ
What is the DGX Spark 64GB?
A new OEM-only configuration of NVIDIA's DGX Spark desktop AI supercomputer with 64GB of unified LPDDR5X instead of 128GB. Same GB10 Grace Blackwell Superchip, same DGX OS and AI stack, from Acer, ASUS, Dell, Gigabyte, HP and MSI.
How much does the DGX Spark 64GB cost and when is it available?
From $4,999, on sale October 23, 2026 through partner stores. No India price announced – budget roughly Rs. 5–5.5 lakh landed after duties and GST.
What is the difference between the 64GB and 128GB DGX Spark?
Only memory capacity (and per-GB price): 64GB runs up to ~100B-parameter models vs ~200B on 128GB, with roughly half the fine-tuning headroom. Chip, bandwidth (273 GB/s), networking, ports and software are identical.
Can two 64GB DGX Sparks run a 200B model?
Yes, per NVIDIA: link two units over ConnectX-7 with one QSFP cable, let Sync Cluster Assistant configure the fabric, and the pooled 128GB supports up to 200B-parameter models with up to 1.7x the single-box speed on Qwen3.8 27B in NVIDIA's test.
Is the 64GB DGX Spark good value?
Lower total than inflated 128GB street prices ($7,000–9,000), but worse per gigabyte (~$78/GB vs ~$37/GB at list). Buy it to spend less upfront or to start clustering, not for a per-GB bargain.
Should I buy it for local AI in India?
Only if your models fit in 64GB (27–70B agents do) and you value private, offline GB10 compute with a cluster upgrade path. For fine-tuning 70B or single-box 200B inference, wait for 128GB stock near list or use cloud.