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NVIDIA DGX Spark 128GB vs NVIDIA GeForce RTX 5090

For running models locally, priced in the US. At 64k context the first runs 42 models fully in memory, the second 27.

In short

The NVIDIA DGX Spark 128GB has more memory for models: 128 GB against 32 GB. At 64k context the NVIDIA DGX Spark 128GB runs 42 of the models we track fully in memory and the NVIDIA GeForce RTX 5090 27; only the NVIDIA DGX Spark 128GB holds DeepSeek V4 Flash. On the models both run fully in memory, the NVIDIA GeForce RTX 5090 is about 6.6× as fast as the NVIDIA DGX Spark 128GB (estimated, median across models). In the US the NVIDIA DGX Spark 128GB costs $7,399 at eBay and the NVIDIA GeForce RTX 5090 $4,921 at eBay: the NVIDIA GeForce RTX 5090 is $2,478 cheaper.

Updated . Speeds are estimates; how they are worked out.

NVIDIA DGX Spark 128GB

$7,399

eBay

Add to build
NVIDIA GeForce RTX 5090

$4,921

eBay

Add to build

Specifications

SpecNVIDIA DGX Spark 128GBNVIDIA GeForce RTX 5090
ChipGB10 Grace Blackwell–
Memory bus256-bit512-bit
Memory bandwidth273 GB/s1,792 GB/s
FP16 compute62 TFLOPS209 TFLOPS
SoftwareCUDACUDA
Unified memory128 GB–
GPU can use by default119 GB–
GPU can use, tuned122 GB–
Operating systemdgx-os–
Processor20-core Arm (GB10)–
Storage4000 GB–
GPU–GeForce RTX 5090
Memory–32 GB GDDR7
Board power–575 W
PCIe generation–Gen 5
PCIe lanes–x16

What each runs

Estimated decode speed with the most precise quant that fits each one est.

ModelNVIDIA DGX Spark 128GBNVIDIA GeForce RTX 5090
DeepSeek V4 Flash8tok/sUD-Q2_K_XLwon't run
GLM 5.3 Flash11tok/sUD-Q2_K_XLwon't run
Qwen 3.8 Flash Next3tok/sUD-Q4_K_XLwon't run
Qwen 3.8 27B5tok/sQ8_042tok/sUD-Q6_K
MiniMax M2.75tok/sUD-Q3_K_Mwon't run
Qwen 3.5 122B-A10B10tok/sQ6_Koffload
Qwen 3.6 27B5tok/sQ8_041tok/sQ6_K
gpt-oss 120B18tok/sMXFP4won't run
Qwen3 Coder Next18tok/sQ8_0offload
Nemotron 3 Super 120B-A12B9tok/sUD-Q6_Koffload
Gemma 4 31B4tok/sQ8_035tok/sQ6_K
Mistral Small 4 119B14tok/sUD-Q6_Koffload
Qwen 3.6 35B-A3B23tok/sQ8_0173tok/sUD-Q6_K
Qwen 3.5 27B5tok/sQ8_041tok/sQ6_K
Gemma 4 26B-A4B18tok/sQ8_0117tok/sQ8_0
GLM 4.5 Air4tok/sQ6_Koffload
Qwen 3.5 35B-A3B23tok/sQ8_0175tok/sQ6_K
Qwen3 Next 80B-A3B21tok/sQ8_0offload
GLM 4.7 Flash12tok/sQ8_087tok/sQ6_K
Devstral Small 2 24B5tok/sQ8_037tok/sQ6_K
Nemotron 3.5 Lightning 30B-A3B27tok/sUD-Q6_K_XLoffload
Kimi Linear 48B-A3B27tok/sQ8_0277tok/sQ4_K_M
gpt-oss 20B25tok/sMXFP4165tok/sMXFP4
Qwen3 Coder 30B-A3B11tok/sQ8_075tok/sQ6_K
Qwen3 32B3tok/sQ8_0offload
Gemma 4 12B12tok/sQ8_079tok/sQ8_0
Qwen 3.5 9B14tok/sQ8_095tok/sQ8_0
Gemma 3 27B5tok/sQ8_040tok/sQ6_K
Mistral Small 3.2 24B5tok/sQ8_037tok/sQ6_K
DeepSeek R1 Distill Qwen 32B3tok/sQ8_0offload
Llama 3.3 70B2tok/sQ8_0won't run
Ministral 3 14B7tok/sQ8_044tok/sQ8_0
Qwen3 14B6tok/sQ8_042tok/sQ8_0
DeepSeek R1 0528 Qwen3 8B9tok/sQ8_061tok/sQ8_0
Qwen3 8B9tok/sQ8_061tok/sQ8_0
Phi-4 14B6tok/sQ8_038tok/sQ8_0
Qwen 3.5 4B25tok/sQ8_0166tok/sQ8_0
Ministral 3 8B9tok/sQ8_061tok/sQ8_0
Granite 4.2 8B8tok/sQ8_055tok/sQ8_0
Gemma 4 E4B17tok/sQ8_0110tok/sQ8_0
LFM2.5 8B-A1B24tok/sQ8_0156tok/sQ8_0
Llama 3.1 8B11tok/sQ6_K73tok/sQ6_K

Questions

Which is better for local AI, the NVIDIA DGX Spark 128GB or the NVIDIA GeForce RTX 5090?
Choose the NVIDIA DGX Spark 128GB to run bigger models. Choose the NVIDIA GeForce RTX 5090 for speed. Choose the NVIDIA GeForce RTX 5090 to spend less.
Which can run bigger models, the NVIDIA DGX Spark 128GB or the NVIDIA GeForce RTX 5090?
At 64k context the NVIDIA DGX Spark 128GB runs 42 of the models we track fully in memory and the NVIDIA GeForce RTX 5090 27; only the NVIDIA DGX Spark 128GB holds DeepSeek V4 Flash.
Which is faster for local AI, the NVIDIA DGX Spark 128GB or the NVIDIA GeForce RTX 5090?
On the models both run fully in memory, the NVIDIA GeForce RTX 5090 is about 6.6× as fast as the NVIDIA DGX Spark 128GB (estimated, median across models).
What is the price difference between the NVIDIA DGX Spark 128GB and the NVIDIA GeForce RTX 5090?
In the US the NVIDIA DGX Spark 128GB costs $7,399 at eBay and the NVIDIA GeForce RTX 5090 $4,921 at eBay: the NVIDIA GeForce RTX 5090 is $2,478 cheaper.

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