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Acer · ASUS · Dell · Gigabyte · HP · MSINVIDIA DGX SparkDesk tier · £2.8k – £8.5k

DGX Spark 64 GB (partner builds)

Half the memory for less money — the GB10 platform with 64 GB, sold since October 2026 by NVIDIA's partners rather than as a Founders Edition.

Price

from $4,499

Gigabyte 1 TB at Newegg (2 TB $4,999) · NVIDIA starting price $4,999 · Dell ~$5,437 (1 TB) · UK models listed (Scan: ASUS, MSI) but unpriced

verified 2026-11 · supply & lease options in every proposal

64 GB
Unified memory

one coherent CPU+GPU pool

273 GB/s
Memory bandwidth

the tok/s ceiling

1 PFLOP
Sparse FP4 compute

1,000 AI TOPS

240 W
Wall power

standard socket, near-silent

The machine

On 23 October 2026 NVIDIA's partners — Acer, ASUS, Dell, Gigabyte, HP and MSI, but not NVIDIA itself or Lenovo — began selling the GB10 platform with 64 GB of LPDDR5x instead of 128 GB. Everything else is the same: the Grace Blackwell superchip, 273 GB/s of bandwidth, ConnectX-7 clustering and DGX OS.

64 GB is sized for ~100B-parameter models at FP4 and the efficient tier — gpt-oss-20b, Qwen3.8 27B, Gemma 4 31B — while gpt-oss-120b (~65 GB) doesn't fit one unit. NVIDIA's starting price is $4,999 and Gigabyte's 1 TB build is $4,499 at Newegg; UK retailers list ASUS and MSI 64 GB models but hadn't priced them at time of writing.

vs its siblings: The cheapest way onto the GB10 platform; partner-only, with each vendor's own storage and support.

Memory, to scale

64 GB model-visible · bandwidth is the speed limit

Unified LPDDR5x

64 GB · 273 GB/s

LPDDR5x

For scale

RTX PRO 6000 — 96 GB @ 1.79 TB/s

Mac Studio M5 Ultra — 512 GB @ 1.2 TB/s

DGX Station GB300 — 748 GB coherent

Links over ConnectX-7 like the 128 GB Spark — two 64 GB units pool 128 GB for ~200B-parameter models.

Capability

What it actually runs

Declared from research and benchmarks, not computed marketing — tokens-per-second figures are cited where a real measurement exists.

  • Qwen3.8 27BQ4with headroom~17 GB — the best small model on AA's v5 index
  • Gemma 4 31BQAT 4-bitwith headroom~23 GB — Google's own 4-bit build
  • Qwen3.6 35BQ4with headroom3B active — fast triage
  • gpt-oss-20bMXFP4with headroom~14 GB — pipeline workhorse
  • gpt-oss-120bMXFP42+ units linked~65 GB — two linked 64 GB units, or the 128 GB Spark
Specification

The full sheet

Compute

Superchip
NVIDIA GB10 Grace Blackwell
GPU
Blackwell architecture — 6,144 CUDA cores, 5th-gen Tensor Cores
AI performance
Up to 1 PFLOP (1,000 TOPS) sparse FP4
CPU
20-core Arm — 10× Cortex-X925 + 10× Cortex-A725
Interconnect
NVLink-C2C (CPU ↔ GPU, coherent)

Memory

Unified memory
64 GB LPDDR5x, 256-bit
Bandwidth
273 GB/s
Model capacity
~100B params FP4 inference · ~200B with two linked units

Storage

NVMe
1–4 TB by vendor

Networking & I/O

Smart NIC
ConnectX-7, 200 Gb/s total (2× QSFP) — up to three units by direct cable, four via a switch
Ethernet / wireless
1× 10 GbE RJ-45 · WiFi 7 · BT 5.4
Ports
4× USB4 Type-C · HDMI 2.1a

Software

OS
NVIDIA DGX OS 7 (Ubuntu 24.04 base)
Stack
NVIDIA AI stack, NGC catalogue, NIM microservices, Blueprints, DGX Spark playbooks
Licence
90-day NVIDIA AI Enterprise included (3-year purchasable)

Where it shines

  • The cheapest entry to the GB10 platform and its CUDA stack
  • Same 273 GB/s, ConnectX-7 clustering and DGX OS as the 128 GB box
  • Plenty for efficient-tier (27–35B) models with long context

The trade-offs

  • 64 GB rules out 120B-class models on one unit
  • Partner-only — no Founders Edition, and UK prices not yet published
  • Same 273 GB/s bandwidth ceiling as every Spark

Buy this box for

Development boxes for teams building on NVIDIA's stackTriage, extraction and drafting on efficient-tier modelsA first Spark where 120B-class models aren't needed yet
The platform

Understanding NVIDIA DGX Spark

GB10 Grace Blackwell Superchip

The DGX Spark is NVIDIA's reference design for desk-scale AI, built on the GB10 Grace Blackwell Superchip: a 20-core Arm CPU and a Blackwell GPU sharing 128 GB of unified LPDDR5x memory over NVLink-C2C. Because CPU and GPU see one memory pool, sparse models up to ~200 billion parameters (FP4) load whole — no VRAM juggling — and it fine-tunes up to 70B.

It ships as the NVIDIA Founders Edition and as near-identical partner machines from ASUS, Dell, HP, MSI, Lenovo, Acer and Gigabyte — the same superchip with different storage, support and price. Since 23 October 2026 the partners (not NVIDIA or Lenovo) also sell a 64 GB configuration, from $4,999, sized for ~100B-parameter models. NVIDIA now supports up to four linked units over their ConnectX-7 200 Gb ports — two (256 GB) serve ~400B-parameter models, four (512 GB) ~700B — and manual clusters of up to eight. Every box arrives with DGX OS, the NVIDIA AI stack, NIM microservices and a 90-day NVIDIA AI Enterprise licence.

The trade-off is bandwidth: 273 GB/s of LPDDR5x is the tokens-per-second ceiling (gpt-oss-120b runs at ~35–40 tok/s). It is a superb prototyping and small-team inference machine, not a high-concurrency server. And the memory shortage keeps repricing it: the Founders Edition went from $3,999 to $4,699 in February 2026 and is now $6,950 at NVIDIA's US store (£5,999 at Scan), with UK partner boxes from £5,099 to £8,303.

Siblings on the same silicon

NVIDIA DGX Spark Founders Edition — champagne-gold desk AI supercomputer, front three-quarter view

NVIDIA

DGX Spark Founders Edition

The reference machine: NVIDIA's own champagne-gold 1.2 kg desk supercomputer, 4 TB standard, dual QSFP exposed for clustering.

Memory
128 GB
Bandwidth
273 GB/s
AI perf
1 PFLOP FP4

£5,999 inc VAT

Scan UK, in stock (NVIDIA UK store £6,390) · US $6,950 at NVIDIA's store, out of stock (was $4,699 in Feb 2026)

Details
Lenovo ThinkStation PGX — GB10 AI workstation, front-right three-quarter view

Lenovo

ThinkStation PGX

The GB10 goes corporate — ThinkStation build quality, Premier support options and direct UK supply from Lenovo.

Memory
128 GB
Bandwidth
273 GB/s
AI perf
1 PFLOP FP4

from £5,200 inc VAT

Lenovo UK, 1 TB (30KL000CUC), delivery ~17–19 Nov · 2 TB £6,499.01 · 4 TB Gen5 £7,699.01 · US $5,859 (1 TB)

Details
Acer Veriton GN100 — GB10 AI mini workstation with LED light bar, front three-quarter view

Acer

Veriton GN100

4 TB standard, sold through Acer's business partners — and one of the first boxes with the new 64 GB option.

Memory
128 GB
Bandwidth
273 GB/s
AI perf
1 PFLOP FP4

Quote-only (UK)

Via Acer partners — no named UK retailer price verified · US price unverified

Details
ASUS Ascent GX10 — compact GB10 AI mini-PC, front view

ASUS

Ascent GX10

The volume workhorse — the cheapest verified UK Spark at £5,099, with a TAA-compliant public-sector SKU and a stackable chassis.

Memory
128 GB
Bandwidth
273 GB/s
AI perf
1 PFLOP FP4

£5,099 inc VAT

Scan UK, 1 TB, in stock · Overclockers 2 TB £5,899.99 / 4 TB £6,229.99 (pre-order) · US $5,999 (1 TB), NVIDIA store

Details
MSI EdgeXpert MS-C931 — GB10 desk AI supercomputer, front three-quarter view

MSI

EdgeXpert MS-C931

The clustering-friendly build — a QSFP cable in the box on Gen5 SKUs, and the cheapest 4 TB GB10 in the UK.

Memory
128 GB
Bandwidth
273 GB/s
AI perf
1 PFLOP FP4

£5,099 inc VAT

NVIDIA UK store, 4 TB · Scan £5,699, in stock · US $7,999.99 (13SUS), NVIDIA store

Details
Gigabyte AI TOP ATOM — GB10 desk AI supercomputer, front three-quarter view

Gigabyte

AI TOP ATOM

The GB10 wired into Gigabyte's AI TOP tuning ecosystem — a neat 1-litre box with up to 4 TB of Gen5 storage.

Memory
128 GB
Bandwidth
273 GB/s
AI perf
1 PFLOP FP4

from £5,199.95 inc VAT

Overclockers UK pre-order: 1 TB Gen4 £5,199.95 · 4 TB Gen4 £5,299.99 · 4 TB Gen5 £6,399.95 · US 64 GB from $4,499 (Newegg)

Details

Sources & verification

Specifications and prices verified 2026-11 against the sources below. The memory shortage is repricing this market monthly — we re-verify at quote.

Compare against the rest of the catalogue, or have us spec it against your workloads.

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Make it real

Spec the DGX Spark 64 GB (partner builds) for your workload

We'll size it against your real token volumes, quote supply and lease options, and stand up the models with evals that prove they do the job — or tell you honestly that a different box (or the API) wins.