Local inference

NVIDIA DGX Spark: which models actually run on it

The DGX Spark is the cheapest box that runs frontier-class open weights locally: 128GB of unified memory on the GB10 Grace Blackwell superchip, 273GB/s of bandwidth, in a quiet desktop form factor. Stack two over ConnectX-7 and you get a 256GB unified pool - enough for DeepSeek V4 Flash 0731's lossless 4-bit build. Below is every model in the catalog that fits, split by what runs on one Spark versus what needs two.

The box

Memory
128GB unified
Bandwidth
273GB/s
Chip
GB10 (DGX Spark)
Price
$4,699

Water-cooled, roughly 35 dBA, launched 2025. Unified memory means a model loads into one pool shared across the whole SoC - no offload between discrete cards. Founders Edition price; see the hardware page for full specs.

Featured local run

DeepSeek V4 Flash 0731 on two DGX Sparks

The 2026-07-31 iterative update of V4 Flash (a 10-point Artificial Analysis Intelligence Index jump to 50) landed local inference via Unsloth's Dynamic 2.0 GGUFs. The lossless 4-bit build (UD-Q4_K_XL) needs about 168GB in RAM - it does not fit a single 128GB Spark, but two stacked over ConnectX-7 give a 256GB unified pool and it runs. The 8-bit full-precision build (UD-Q8_K_XL, about 175GB) fits the same pair.

Runs on a single 128GB Spark

These models have a local variant that fits in 128GB. The quant listed is the smallest build that fits; a larger quant on the same model may also fit and run at higher quality.

GLM-5.3-Flash MoE UD-IQ1_S
93GB weights runs in 100GB comfortable in 110GB GEN 91
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Qwen3.8-Flash-Next MoE EXL3_3BPW
85GB weights runs in 24GB comfortable in 96GB GEN 89
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Pokee-Isaac 28B Q4_K_M
16GB weights runs in 18GB comfortable in 22GB GEN 88
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Qwen3.6 35B A3B MoE Q4_K_M
20GB weights runs in 20GB comfortable in 22GB GEN 87
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Ornith-1.5-35B-A3B MoE Q4_K_M
22GB weights runs in 22GB comfortable in 24GB GEN 86
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Gemma 4 31B QAT
18GB weights runs in 19GB comfortable in 22GB GEN 85
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Qwen3 235B A22B MoE Q2_K
68GB weights runs in 68GB comfortable in 80GB GEN 85
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Qwen3.8-27B UD-IQ1_S
6GB weights runs in 8GB comfortable in 10GB GEN 85
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Gemma 4 26B A4B MoE NVFP4
14GB weights runs in 15GB comfortable in 18GB GEN 84
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Qwen3.6 27B Q4_K_M
16GB weights runs in 16GB comfortable in 19GB GEN 82
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Llama 3.3 70B Instruct Q2_K
22GB weights runs in 22GB comfortable in 25GB GEN 80
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Qwen3 32B Q4_K_M
20GB weights runs in 20GB comfortable in 22GB GEN 78
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MiMo-V2.6-Flash MoE Q2_K
77GB weights runs in 80GB comfortable in 96GB GEN 76
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Nemotron 3.5 Lightning MoE Q4_K_M
32GB weights runs in 34GB comfortable in 40GB GEN 74
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Ornith-1.5-9B Q4_K_M
6GB weights runs in 6GB comfortable in 8GB GEN 74
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Gemma 4 12B Q4_K_M
7GB weights runs in 7GB comfortable in 9GB GEN 72
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Qwen3 30B A3B MoE Q4_K_M
16GB weights runs in 16GB comfortable in 17GB GEN 72
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Gemma 3 27B Q4_K_M
17GB weights runs in 17GB comfortable in 19GB GEN 72
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Xing4.0-29B-A4B MoE Q4_K_M
20GB weights runs in 15GB comfortable in 24GB GEN 72
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Phi-4 14B Q4_K_M
8GB weights runs in 8GB comfortable in 10GB GEN 70
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Qwen3 14B Q4_K_M
9GB weights runs in 9GB comfortable in 11GB GEN 70
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Mixtral 8x7B Instruct MoE Q3_K_M
19GB weights runs in 19GB comfortable in 21GB GEN 68
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Gemma 3 12B Q4_K_M
8GB weights runs in 8GB comfortable in 9GB GEN 68
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Qwen3 8B Q4_K_M
5GB weights runs in 5GB comfortable in 7GB GEN 65
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Mistral Nemo 12B Q4_K_M
7GB weights runs in 7GB comfortable in 9GB GEN 62
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Llama 3.1 8B Instruct Q4_K_M
4GB weights runs in 4GB comfortable in 6GB GEN 62
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LFM2.5 8B A1B MoE Q4_K_M
2GB weights runs in 2GB comfortable in 4GB GEN 58
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Gemma 3n E4B Q4_K_M
8GB weights runs in 8GB comfortable in 10GB GEN 58
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Qwen3 4B Q4_K_M
3GB weights runs in 3GB comfortable in 4GB GEN 56
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Gemma 3 4B Q4_K_M
3GB weights runs in 3GB comfortable in 4GB GEN 55
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Phi-4 Mini Q4_K_M
2GB weights runs in 2GB comfortable in 4GB GEN 52
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Llama 3.2 3B Instruct Q4_K_M
2GB weights runs in 2GB comfortable in 3GB GEN 50
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Gemma 3n E2B Q4_K_M
6GB weights runs in 6GB comfortable in 8GB GEN 48
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Gemma 3 1B Q4_K_M
1GB weights runs in 1GB comfortable in 2GB GEN 38
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Llama 3.2 1B Instruct Q4_K_M
1GB weights runs in 1GB comfortable in 2GB GEN 35
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Gemma 3 270M Q8_0
0GB weights runs in 0GB comfortable in 1GB GEN 25
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Laya Q4_K_M
0GB weights runs in 0GB comfortable in 1GB
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Muse Glimmer 30B Q4_K_M
18GB weights runs in 24GB comfortable in 36GB
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Jeff Q4_K_M
1GB weights runs in 1GB comfortable in 4GB
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Nemotron 3 Diarization Q8_0
0GB weights runs in 0GB comfortable in 1GB
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Nemotron 3 Nano 4B Q4_K_M
3GB weights runs in 3GB comfortable in 5GB
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Qwen-Image-2.1 Q8_0
16GB weights runs in 16GB comfortable in 20GB
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MiniMax M2.7 UD-IQ1_M
61GB weights runs in 61GB comfortable in 88GB
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GLiNER2.5-Decide LITERT
1GB weights runs in 1GB comfortable in 2GB
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Flux 3 Action BF16
16GB weights runs in 17GB comfortable in 24GB
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OpenJev Q4_K_M
0GB weights runs in 1GB comfortable in 1GB
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Why a Spark, not a cloud key

A DGX Spark is a one-time purchase that runs every open-weight model that fits, with no per-token bill and no data leaving your desk. For a team spending more than a few hundred dollars a month on API tokens, owning the box is usually cheaper inside the first year - and the model you run today is not the model you are locked into.