128GB VRAM

DGX Spark 128G — what LLMs can it run?

102 of 112 indexed models fit comfortably in 128GB at 4K context, each at the highest-quality quant that still leaves headroom.

The short answer for DGX Spark 128G

128 GB, 273 GB/s Unified LPDDR5X (One pool shared by the GB10's CPU and GPU and by DGX OS itself. NVIDIA publishes no fixed GPU share, so fits are judged against the whole pool — and nothing larger than it can load, because there is no separate system RAM to spill into.). 102 of 112 models in this index fit comfortably at 4K context; 103 load at all.

Biggest that fits
DBRX Instruct — 84.3 GB at Q4_K_M — 43.7 GB spare at 4K, so longer context comes out of a thin margin.
Room to grow
Nemotron 3.5 Super VL 120B-A12B — 75.1 GB at Q4_0 — under 60% of the card, which leaves 52.9 GB for a long context window or a second process.
Speed ceiling
Llama 3.1 8B Instruct at Q4_K_M reads 4.6 GB of weights per token, so 273 GB/s puts a hard ceiling near 59 tok/s. That is arithmetic on two published numbers, not a benchmark — real throughput lands below it. No run on this card has been measured here, so there is nothing to compare the ceiling against.
The wall you will hit
Bandwidth, and one pool. 128 GB holds models no single consumer card can, but every byte of it — CPU, GPU and DGX OS alike — comes from the same LPDDR5X at 273 GB/s, a fraction of a discrete flagship's. There is no system RAM behind it to spill into, so this site treats anything larger than the pool as not loading at all. The largest dense model here, Llama 3.2 90B Vision Instruct at Q4_K_M, has a ceiling of about 5 tok/s on it; mixture-of-experts models read only their active experts per token and run far faster than their size suggests.

70B+ · 13

DGX Spark 128G — what LLMs can it run? — 70B+
ModelQuantEst. VRAMHeadroomtok/s
DBRX Instruct132B MoEQ4_K_M84.31 GB+43.7 GB≤13Ceiling
Qwen3.8-Flash-Next125B-A6BQ4_K_M79.32 GB+48.7 GB≤79Ceiling
Nemotron 3.5 Super VL 120B-A12B120B-A12BQ4_075.12 GB+52.9 GB≤40Ceiling
GPT-OSS 120B117B MoEMXFP466.4 GB+61.6 GB≤104Ceiling
Llama 4 Scout 17B (16E)109B MoEQ4_K_M69.88 GB+58.1 GB≤28Ceiling
GLM-4.5-Air106B MoEQ4_K_M67.94 GB+60.1 GB≤40Ceiling
Llama 3.2 90B Vision Instruct90BQ4_K_M57.5 GB+70.5 GB≤5Ceiling
Nemotron 3.5 Bolt 74B-A9B74B-A9BIQ2_XXS23.05 GB+105 GB≤107Ceiling
Qwen2.5 72B Instruct72BQ5_K_M55.31 GB+72.7 GB≤6Ceiling
Llama 3.1 70B Instruct70BQ5_K_M53.75 GB+74.3 GB≤6Ceiling
Llama 3.3 70B Instruct70BQ5_K_M53.75 GB+74.3 GB≤6Ceiling
DeepSeek-R1-Distill-Llama-70B70BQ4_K_M46.1 GB+81.9 GB≤7Ceiling
Jamba 1.5 Mini52B-A12BQ4_K_M33.01 GB+95 GB≤39Ceiling

32B · 31

DGX Spark 128G — what LLMs can it run? — 32B
ModelQuantEst. VRAMHeadroomtok/s
Kimi Linear 48B-A3B Instruct48B-A3BQ8_053.33 GB+74.7 GB≤90Ceiling
Mixtral 8x7B Instruct47B MoEQ4_K_M30.13 GB+97.9 GB≤37Ceiling
Seed-OSS 36B Instruct36BQ5_K_M27.81 GB+100.2 GB≤11Ceiling
Qwen3.6 35B-A3B35B-A3BQ8_039.6 GB+88.4 GB≤90Ceiling
Command R 35B35BQ4_K_M22.86 GB+105.1 GB≤14Ceiling
Yi 1.5 34B Chat34BQ4_K_M22.82 GB+105.2 GB≤14Ceiling
Qwen3 32B Instruct32BQ4_K_M21.85 GB+106.2 GB11–14Derived
Qwen2.5 32B Instruct32BQ4_K_M21.69 GB+106.3 GB11–15Derived
Qwen2.5-Coder 32B Instruct32BQ4_K_M21.69 GB+106.3 GB11–15Derived
DeepSeek-R1-Distill-Qwen-32B32BQ4_K_M21.69 GB+106.3 GB11–15Derived
Nemotron 3.5 Lightning 30B-A3B30B-A3BQ8_035.14 GB+92.9 GB≤90Ceiling
Gemma 4 31B IT31BQ8_035.72 GB+92.3 GB7–9Derived
Qwen3 30B-A3B Instruct30B-A3BQ5_K_M23.04 GB+105 GB≤135Ceiling
Qwen3-Coder 30B-A3B Instruct30B-A3BQ5_K_M23.04 GB+105 GB≤135Ceiling
Qwen3-VL 30B-A3B Instruct30B-A3BQ8_034.28 GB+93.7 GB≤90Ceiling
North Mini Code 1.0 30B-A3B30B-A3BQ8_034.3 GB+93.7 GB≤90Ceiling
GLM-4.7-Flash30B-A3BQ8_033.47 GB+94.5 GB≤90Ceiling
Granite 4.2 30B30BQ8_033.61 GB+94.4 GB7–9Derived
Muse Glimmer 30B30BQ8_031.08 GB+96.9 GB7–10Derived
Qwen3.8 27B27BQ4_K_M17.47 GB+110.5 GB13–17Derived
Qwen3.6 27B27BQ8_030.71 GB+97.3 GB7–10Derived
Gemma 3 27B IT27BQ4_K_M18.37 GB+109.6 GB13–17Derived
Gemma 2 27B Instruct27BQ5_K_M21.76 GB+106.2 GB11–15Derived
Gemma 4 26B-A4B IT26B-A4BQ8_028.42 GB+99.6 GB≤68Ceiling
Mistral Small 24B Instruct24BEXL2 4.65bpw15.26 GB+112.7 GB≤21Ceiling
LFM2.5 24B-A2B24B-A2BQ8_026.73 GB+101.3 GB≤110Ceiling
Devstral Small 1.1 24B24BQ6_K20.91 GB+107.1 GB11–15Derived
Magistral Small 1.2 24B24BQ6_K20.91 GB+107.1 GB11–15Derived
Codestral 22B22BQ4_K_M15.03 GB+113 GB16–21Derived
ERNIE 4.5 21B-A3B21B-A3BQ8_024.44 GB+103.6 GB≤90Ceiling
GPT-OSS 20B21B MoEMXFP412.76 GB+115.2 GB≤138Ceiling

14B · 19

DGX Spark 128G — what LLMs can it run? — 14B
ModelQuantEst. VRAMHeadroomtok/s
InternLM2 20B Chat20BQ5_K_M15.59 GB+112.4 GB15–20Derived
DeepSeek-Coder-V2-Lite Instruct16B-A2.4BQ8_017.56 GB+110.4 GB≤113Ceiling
DeepSeek-V2-Lite Chat16B-A2.4BQ4_K_M10.08 GB+117.9 GB≤198Ceiling
StarCoder2 15B15BQ4_K_M10.19 GB+117.8 GB24–32Derived
Qwen3 14B Instruct14BQ5_K_M11.65 GB+116.4 GB21–27Derived
Qwen2.5 14B Instruct14BQ5_K_M11.73 GB+116.3 GB21–27Derived
DeepSeek-R1-Distill-Qwen-14B14BEXL2 4.65bpw9.75 GB+118.3 GB≤34Ceiling
Phi-4 14B14BQ5_K_M11.77 GB+116.2 GB21–27Derived
Phi-3 Medium 14B Instruct14BQ6_K12.86 GB+115.1 GB19–25Derived
Mistral Nemo 12B Instruct12BQ6_K11.14 GB+116.9 GB22–29Derived
Gemma 3 12B IT12BQ5_K_M9.84 GB+118.2 GB25–33Derived
Mellum2.1 12B-A2.5B Thinking12B-A2.5BQ8_013.6 GB+114.4 GB≤108Ceiling
Stable LM 2 12B Chat12BQ4_K_M8.35 GB+119.7 GB30–39Derived
Gemma 4 12B IT12BQ8_013.82 GB+114.2 GB17–23Derived
Llama 3.2 11B Vision Instruct11BQ8_012.9 GB+115.1 GB19–25Derived
Solar 10.7B Instruct11BQ4_K_M7.6 GB+120.4 GB33–44Derived
Falcon 3 10B Instruct10BQ4_K_M7.28 GB+120.7 GB34–45Derived
Gemma 2 9B Instruct9BQ8_011.7 GB+116.3 GB22–29Derived
Qwen3.5 9B9BQ8_010.37 GB+117.6 GB22–29Derived

7B · 25

DGX Spark 128G — what LLMs can it run? — 7B
ModelQuantEst. VRAMHeadroomtok/s
GLM-4-9B-Chat9BQ8_010.16 GB+117.8 GB23–30Derived
Qwen3-VL 8B Instruct8BQ8_010.39 GB+117.6 GB23–31Derived
Granite 4.2 8B8BQ8_010.45 GB+117.6 GB23–31Derived
Ministral 3 8B Instruct8BQ8_010.02 GB+118 GB24–32Derived
Qwen2-VL 7B Instruct7BQ4_K_M5.49 GB+122.5 GB43–57Derived
Granite 3.1 8B Instruct8BQ4_K_M5.88 GB+122.1 GB44–58Derived
Qwen3 8B Instruct8BQ6_K7.64 GB+120.4 GB32–43Derived
Llama 3.1 8B Instruct8BQ8_09.47 GB+118.5 GB25–34Derived
Nous Hermes 3 Llama 3.1 8B8BEXL2 4.65bpw5.43 GB+122.6 GB≤62Ceiling
Aya 23 8B8BQ4_K_M5.64 GB+122.4 GB45–59Derived
OpenChat 3.6 8B8BEXL2 4.65bpw5.43 GB+122.6 GB≤62Ceiling
DeepSeek-R1-Distill-Llama-8B8BQ5_K_M6.51 GB+121.5 GB38–50Derived
InternLM2 7B Chat7BQ4_K_M5.45 GB+122.6 GB46–61Derived
Qwen2.5 7B Instruct7BQ6_K6.77 GB+121.2 GB35–46Derived
Qwen2.5-Coder 7B Instruct7BEXL2 4.65bpw4.87 GB+123.1 GB≤65Ceiling
DeepSeek-R1-Distill-Qwen-7B7BEXL2 4.65bpw4.87 GB+123.1 GB≤65Ceiling
Gemma 4 E4B ITE4BQ8_08.46 GB+119.5 GB27–36Derived
OLMo 2 7B Instruct7BQ8_010.3 GB+117.7 GB28–37Derived
OLMo 3 7B Instruct7BQ8_010.3 GB+117.7 GB28–37Derived
WizardLM-2 7B7BQ4_K_M5.14 GB+122.9 GB49–65Derived
Mistral 7B Instruct v0.37BQ6_K6.75 GB+121.3 GB36–48Derived
Zephyr 7B Beta7BQ6_K6.75 GB+121.3 GB36–48Derived
Gemma 4 E2B ITE2BQ8_05.2 GB+122.8 GB≤58Ceiling
Qwen3.5 4B4BQ8_04.97 GB+123 GB≤62Ceiling
Gemma 3 4B IT4BQ8_05.05 GB+123 GB≤63Ceiling

≤3B · 14

DGX Spark 128G — what LLMs can it run? — ≤3B
ModelQuantEst. VRAMHeadroomtok/s
Qwen3 4B Instruct4BQ6_K4.05 GB+124 GB≤88Ceiling
Phi-4 Mini Instruct3.8BQ8_04.81 GB+123.2 GB≤70Ceiling
Phi-3.5 Mini Instruct3.8BQ8_05.89 GB+122.1 GB≤71Ceiling
Granite 4.2 3B3BQ8_04.41 GB+123.6 GB≤74Ceiling
Llama 3.2 3B Instruct3BQ8_04.05 GB+124 GB≤84Ceiling
Qwen2.5 3B Instruct3BQ8_03.59 GB+124.4 GB≤88Ceiling
SmolLM3 3B3BQ8_03.73 GB+124.3 GB≤88Ceiling
Gemma 2 2B Instruct2BQ8_03.34 GB+124.7 GB≤104Ceiling
Qwen3.5 2B2BQ8_02.23 GB+125.8 GB≤138Ceiling
Qwen3 1.7B Instruct1.7BQ8_02.39 GB+125.6 GB≤157Ceiling
Qwen2.5 1.5B Instruct1.5BQ8_01.83 GB+126.2 GB≤176Ceiling
Llama 3.2 1B Instruct1BQ8_01.51 GB+126.5 GB≤218Ceiling
Qwen3.5 0.8B0.8BQ8_00.94 GB+127.1 GB≤337Ceiling
Qwen2.5 0.5B Instruct0.5BQ8_00.6 GB+127.4 GB≤557Ceiling

How this list is built

Each row is the lowest-perplexity-loss quant of that model whose estimated total — weights plus KV cache at 4K context plus activation buffer — uses at most 88% of the card. That is the calculator's "green" threshold, so every row here has real headroom rather than only just fitting. Raise the context length and the list shortens; the calculator lets you check any combination directly. The speed column is this card: a measured run, a range scaled from this card's own Llama 2 7B benchmark, or a bandwidth ceiling. A figure that only exists for the RTX 4090 is labelled as that card.

A further 1 models load but with no headroom to spare (up to 105% of VRAM) — the Quant Hub’s GPU chips count those too, which is why its number is higher.

Measured on this card

No benchmark runs in this index were recorded on a DGX Spark 128G. Every figure on this page is calculated from the model architecture and the quant level — treat them as estimates, not measurements.

Cards with the same budget

The same 128GB in system memory is a different machine — everything on this page fits there, at a speed set by your DIMMs rather than a graphics bus: 128 GB RAM (CPU)

    Setup guides for this card

    Step-by-step guides written for the DGX Spark 128G's runtime or memory size, with the commands and the checks that prove the model is running on the card.

    Common questions

    What is the best local LLM for a DGX Spark 128G?

    For everyday use, Nemotron 3.5 Super VL 120B-A12B at Q4_0 — about 75.1 GB of the card's 128 GB at 4K context, leaving 52.9 GB for a longer window. If you want the largest thing that will load, that is DBRX Instruct at Q4_K_M (84.3 GB, 43.7 GB spare). "Best" here means best fit for the memory budget — this index does not run task benchmarks, so it cannot tell you which model is smarter.

    Can a DGX Spark 128G run Qwen3 235B-A22B Instruct?

    Not reliably. Its smallest build here, Q3_K_M, needs about 119.6 GB — 93% of the machine's 128 GB, and DGX OS runs from that same pool, so whether it loads depends on what the system itself is holding, with nothing left for a longer context. The largest model this machine does clear is DBRX Instruct.

    How many tokens per second does a DGX Spark 128G do on an 8B model at Q4?

    This index has no measured run on this card, so it does not publish a figure. What can be stated from specifications: generating a token requires reading every weight once, Llama 3.1 8B Instruct at Q4_K_M is 4.6 GB of weights, and this card moves 273 GB/s — a ceiling near 59 tok/s. Batch size, context length, the runtime and how much of the model sits in cache all take you below it.

    DGX Spark 128G or Mac M5 Max 128G for local LLMs?

    They hold the same models: 128 GB against 128 GB fits 102 and 102 of 112 respectively at 4K. The difference is throughput — 273 GB/s against 614 GB/s, a 2.2× gap in how fast the weights can be read, which is what token generation is bound by. The Mac M5 Max 128G generates faster on any model both can hold.

    102 of 112 indexed models fit comfortably in 128GB at 4K context, each at the highest-quality quant that still leaves headroom.

    This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml