Unified memory · NVIDIA

What AI models can an NVIDIA DGX Spark 128GB run?

128 GB is server territory. The constraint stops being memory and becomes bandwidth: everything fits, and what varies is how long you wait for it. Memory is shared with the CPU, so the practical ceiling is lower than the sticker number: macOS hands roughly three quarters of it to the GPU by default.

Memory128 GB
Bandwidth273 GB/s
Usable for a model96 GB
Runtime backendCUDA

The short answer

Assuming an 8k context window and default settings, these are the models worth downloading first.

See how fast it feels

NVIDIA DGX Spark 128GB running GPT OSS 120B at MXFP4

Wait for the first word61 ms
Then writes at37 tok/s
Whole answer4.7 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Where it sits

The GPU can use about 96 GB of this machine's 128 GB by default, so for planning it behaves like a 48 GB card with different bandwidth. The 48 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 273 GB/s decides how fast it answers.

Our indicative price for this machine is $3,999 new, reviewed 2026-09-01. That is 3.2 GB of memory and 7 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.

Devices in a similar price band, fastest memory first:

DevicePriceMemoryBandwidthBest all-round pick
Apple M3 Ultra 96GB $3,999 96 GB 819 GB/s GPT OSS 120B, 102.5 t/s
Apple M1 Ultra 128GB $3,400 128 GB 800 GB/s GPT OSS 120B, 100.1 t/s
Apple M2 Ultra 64GB $3,499 64 GB 800 GB/s Qwen3.5 27B, 20.2 t/s
Apple M2 Ultra 128GB $4,799 128 GB 800 GB/s GPT OSS 120B, 100.1 t/s

Every model, scored on this device

Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.

Model Params Verdict Download Memory used Speed Max context
GPT OSS 120B
GPT-OSS
116.83B (5.7B active) Runs great MXFP4 59.68 GB 37 t/s 128k
Qwen3 Coder Next
Qwen
79.67B (3.19B active) Runs great Q8_0 79.43 GB 34.2 t/s 256k
Qwen3 Next 80B A3B Instruct
Qwen
81.32B (3.19B active) Runs great Q8_0 81.07 GB 34.2 t/s 256k
Qwen3.5 35B A3B
Qwen
35.95B (2.9B active) Runs great Q8_0 36.13 GB 38.3 t/s 256k
Gemma 4 26B A4B Instruct
Gemma
25.81B (3.74B active) Runs great Q8_0 26.72 GB 25.8 t/s 256k
GLM 4.7 Flash
GLM
31.22B (3.66B active) Runs great Q8_0 31.64 GB 29.1 t/s 128k
Qwen3 30B A3B
Qwen
30.53B (3.34B active) Runs great Q8_0 31.3 GB 29 t/s 32k
GLM 4.5 Air
GLM
110.47B (13.4B active) Runs well Q6_K 86.21 GB 10 t/s 32k
GPT OSS 20B
GPT-OSS
20.91B (4.18B active) Runs great MXFP4 11.84 GB 48.8 t/s 128k
Qwen3 Coder 30B A3B Instruct
Qwen
30.53B (3.34B active) Runs great Q8_0 31.3 GB 29 t/s 32k
Qwen3.5 27B
Qwen
27.78B Runs well Q5_K_M 19.49 GB 11 t/s 256k
Qwen3.5 9B
Qwen
9.65B Runs well Q8_0 10.25 GB 22.7 t/s 256k
Gemma 4 31B Instruct
Gemma
31.27B Runs well IQ4_XS 19.2 GB 10.7 t/s 128k
Phi 3.5 MoE Instruct
Phi
41.87B (6.64B active) Runs well Q8_0 42.84 GB 15.4 t/s 128k
Gemma 4 12B Instruct
Gemma
11.96B Runs well Q8_0 13.55 GB 15.3 t/s 256k
Qwen3 32B
Qwen
32.76B Runs well Q4_K_M 20.86 GB 10.1 t/s 32k
Gemma 4 E4B Instruct
Gemma
8B Runs well Q8_0 8.42 GB 24.9 t/s 128k
Granite 4.1 8B
Granite
8.79B Runs well Q8_0 10.35 GB 20.2 t/s 128k
Granite 4.1 30B
Granite
28.87B Runs well Q4_K_M 18.64 GB 11 t/s 128k
Qwen3 8B
Qwen
8.19B Runs well Q8_0 9.64 GB 23.3 t/s 32k
Qwen3 14B
Qwen
14.77B Runs well Q8_0 16.31 GB 13.3 t/s 32k
Qwen2.5 VL 7B Instruct
Qwen
8.29B Runs well Q8_0 9.03 GB 24.8 t/s 64k
Gemma 3 27B Instruct
Gemma
27.43B Runs well Q5_K_M 19.71 GB 10.4 t/s 128k
Mixtral 8x7B Instruct V0.1
Mistral
46.7B (12.88B active) Runs well Q6_K 37.07 GB 10.8 t/s 32k
Qwen2.5 32B Instruct
Qwen
32.76B Runs well Q4_K_M 20.86 GB 10.1 t/s 32k
Qwen2.5 14B Instruct
Qwen
14.77B Runs well Q8_0 16.56 GB 13.1 t/s 32k
Gemma 3 12B Instruct
Gemma
12.19B Runs well Q8_0 13.27 GB 15.6 t/s 128k
Qwen2.5 Coder 14B Instruct
Qwen
14.77B Runs well Q8_0 16.56 GB 13.1 t/s 32k
Qwen2.5 Coder 32B Instruct
Qwen
32.76B Runs well Q4_K_M 20.86 GB 10.1 t/s 32k
Qwen2.5 7B Instruct
Qwen
7.62B Runs great Q8_0 8.37 GB 27 t/s 32k
Show the other 56 models
Llama 3.1 8B Instruct 8.03B Runs well Q8_0 9.35 GB 23.8 t/s 128k
Phi 4 14.66B Runs well Q8_0 16.51 GB 12.9 t/s 16k
Mistral Small 24B Instruct 2501 23.57B Runs well Q6_K 19.69 GB 10.7 t/s 32k
DeepSeek Coder V2 Lite Instruct 15.71B Runs great Q8_0 16.12 GB 40 t/s 128k
Mistral Nemo Instruct 2407 12.25B Runs well Q8_0 13.81 GB 15.8 t/s 128k
Qwen2.5 Coder 7B Instruct 7.62B Runs great Q8_0 8.37 GB 27 t/s 32k
OLMo 3 7B Instruct 7.3B Runs well Q8_0 10.13 GB 21.6 t/s 64k
Gemma 2 9B Instruct 9.24B Runs well Q8_0 11.5 GB 18.1 t/s 8k
Codestral 22B V0.1 22.25B Runs well Q6_K 19.21 GB 10.8 t/s 32k
Internlm3 8B Instruct 8.8B Runs well Q8_0 9.49 GB 23.5 t/s 32k
Mistral 7B Instruct V0.3 7.25B Runs well Q8_0 8.58 GB 25 t/s 32k
Gemma 4 E2B Instruct 5.12B Runs great Q8_0 5.44 GB 39.2 t/s 128k
Qwen3.5 4B 4.66B Runs great Q8_0 5.27 GB 40.9 t/s 256k
Mistral 7B Instruct V0.2 7.24B Runs well Q8_0 8.57 GB 25 t/s 32k
DeepSeek Coder 7B Instruct V1.5 6.91B Runs well Q8_0 10.99 GB 19.7 t/s 4k
Llama 3.3 70B Instruct 70.55B Fits, but slow Q8_0 72.85 GB 2.8 t/s 64k
Qwen2.5 VL 72B Instruct 73.41B Fits, but slow Q8_0 75.68 GB 2.7 t/s 64k
Qwen2.5 72B Instruct 72.71B Fits, but slow Q8_0 74.99 GB 2.7 t/s 32k
LFM2.5 8B A1B 8.47B Runs great Q8_0 8.81 GB 70.7 t/s 64k
DeepSeek Coder 6.7B Instruct 6.74B Runs well Q8_0 11.07 GB 19.1 t/s 16k
Qwen3 4B 4.02B Runs great Q8_0 5.46 GB 39.4 t/s 32k
Llama 3 3 Nemotron Super 49B V1 49.87B Fits, but slow Q8_0 69.89 GB 2.9 t/s 16k
Gemma 3 4B Instruct 4.3B Runs great Q8_0 4.91 GB 44.1 t/s 128k
Phi 4 Mini Instruct 3.84B Runs great Q8_0 5.17 GB 41.8 t/s 128k
Granite 4.1 3B 3.4B Runs great Q8_0 4.34 GB 50.3 t/s 128k
GLM 4.5 358.34B Fits only at 2 to 3 bits IQ2_XXS 89.25 GB 10.7 t/s 16k
Qwen2.5 3B Instruct 3.09B Runs great Q8_0 3.68 GB 60.2 t/s 32k
Phi 3 Mini 4k Instruct 3.82B Runs great Q8_0 4.9 GB 45.3 t/s 4k
Llama 3.2 3B Instruct 3.21B Runs great Q8_0 4.42 GB 49.6 t/s 128k
LFM2.5 2.6B 2.7B Runs great Q8_0 3.14 GB 71.8 t/s 128k
Qwen3.5 2B 2.27B Runs great Q8_0 2.7 GB 85.2 t/s 256k
Gemma 2 2B Instruct 2.61B Runs great Q8_0 3.54 GB 62.9 t/s 8k
Starcoder2 3B 3.03B Runs great Q8_0 3.49 GB 67.7 t/s 16k
Qwen3 1.7B 2.03B Runs great Q8_0 3.22 GB 69.7 t/s 32k
DeepSeek R1 Distill Qwen 1.5B 1.78B Runs great Q8_0 2.3 GB 114.8 t/s 128k
Qwen2.5 1.5B Instruct 1.54B Runs great Q8_0 2.06 GB 115.3 t/s 32k
SmolLM2 1.7B 1.71B Runs great Q8_0 3.53 GB 62.9 t/s 8k
OLMo 2 0425 1B 1.48B Runs great Q8_0 2.8 GB 88.8 t/s 4k
Llama 3.2 1B Instruct 1.24B Runs great Q8_0 1.82 GB 136 t/s 128k
LFM2.5 1.2B Instruct 1.17B Runs great Q8_0 1.59 GB 179.5 t/s 64k
Gemma 3 1B Instruct 1B Runs great Q8_0 1.35 GB 193.3 t/s 32k
Qwen3.5 0.8B 0.87B Runs great Q8_0 1.28 GB 206.5 t/s 256k
Qwen3 0.6B 0.75B Runs great Q8_0 1.92 GB 124.2 t/s 32k
Qwen2.5 0.5B Instruct 0.49B Runs great Q8_0 0.88 GB 347.1 t/s 32k
LFM2.5 350M 0.35B Runs great Q8_0 0.74 GB 537.5 t/s 64k
Qwen3.5 122B A10B 125.09B Just fits n/a 96.23 GB 17.8 t/s n/a
MiniMax M3 427.04B Fits only at 2 to 3 bits n/a 103.82 GB 16.4 t/s n/a
MiniMax M2.5 228.7B Just fits n/a 115.46 GB 15.8 t/s n/a
MiniMax M2.7 228.69B Just fits n/a 115.46 GB 15.8 t/s n/a
Qwen3 235B A22B 235.09B Just fits n/a 108.88 GB 10.1 t/s n/a
Kimi K2.6 1026.88B Will not run n/a 578.44 GB n/a n/a
Kimi K2 Instruct 1026.41B Will not run n/a 578.18 GB n/a n/a
GLM 5.1 753.86B Will not run n/a 425.04 GB n/a n/a
GLM 5.2 753.33B Will not run n/a 424.75 GB n/a n/a
DeepSeek V3.2 685.4B Will not run n/a 386.43 GB n/a n/a
DeepSeek R1 684.53B Will not run n/a 385.95 GB n/a n/a

What it will not run

11 of the 86 architectures we track are out of reach here, even at 2-bit precision.

Other devices with 128 GB

Same capacity, different speed. Once a model fits, bandwidth is what separates these.

Questions people ask about an NVIDIA DGX Spark 128GB

How much VRAM does an NVIDIA DGX Spark 128GB have?

The NVIDIA DGX Spark 128GB has 128 GB of unified memory shared with the CPU, of which roughly 96 GB is available to a model once the operating system takes its share.

Can an NVIDIA DGX Spark 128GB run a 70B model?

Yes. At Q8_0 a 70B model fits in 79.43 GB and generates around 34.2 tokens per second.

What is the best local model for an NVIDIA DGX Spark 128GB?

GPT OSS 120B at MXFP4 is the strongest all-round fit: 59.68 GB used, around 37 tokens per second at 8k context.