Builds / Nvidia GB10 (unified)

DGX Spark (GB10, 128 GB): 23 models on vLLM at 128K context, Qwen3.6 35B-A3B up to 102 tok/s

Name: DGX Spark (GB10, 128 GB): 23 models on vLLM at 128K context, Qwen3.6 35B-A3B up to 102 tok/s
Creator: RigBase
License: https://creativecommons.org/licenses/by/4.0/

Source: ctala.github.io ↗📏 How measured: reported by the original source; RigBase re-checked the numbers at that source

GPU(s): Nvidia GB10 (unified)
VRAM / unified memory (GB): 128
CPU: Nvidia GB10 Grace (Arm)
Amazon · eBay (used) · Newegg
Motherboard: not stated
RAM: 128 GB LPDDR5X unified (~121 GB usable)
PSU: not stated
Other: NVIDIA DGX Spark, DGX OS (aarch64), FP8 KV cache, 128K context per conversation
Reported total cost: not stated
Power: not stated

Measured speed (generation tokens/sec)

Model	Quant	Engine	tok/s	Details
Qwen3.6 35B-A3B	NVFP4 + MTP (k=2)	vLLM 0.31.0, 128K ctx, 1 user	102.3	⚡ spec-decode (MTP)
Qwen3.6 35B-A3B	NVFP4, no speculative decoding	vLLM 0.31.0, 128K ctx, 1 user	78.1
gpt-oss 20B		vLLM 0.31.0, 128K ctx, 1 user	96.2
Nemotron 3.5 Lightning 30B-A3B	+ DSpark speculative decoding (k=3)	vLLM 0.31.0, 128K ctx, 1 user	94.4	⚡ spec-decode (DSpark)
Nemotron 3.5 Lightning 30B-A3B	no speculative decoding	vLLM 0.31.0, 128K ctx, 1 user	70.3
Nemotron 3 Nano Omni 30B		vLLM 0.31.0, 128K ctx, 1 user	58.8
Gemma 4 26B-A4B	+ official MTP drafter (k=3)	vLLM 0.31.0, 128K ctx, 1 user	56.5	⚡ spec-decode (MTP)
Gemma 4 26B-A4B	no speculative decoding	vLLM 0.31.0, 128K ctx, 1 user	30.8
gpt-oss 120B		vLLM 0.31.0, 128K ctx, 1 user	44.4
Qwen3.8 Flash-Next	+ MTP (k=2)	patched vLLM (qwen38-flash-dgx), 128K ctx, 1 user	26.8	⚡ spec-decode (MTP)
Qwen3.8 27B	+ MTP (k=2)	vLLM 0.31.0, 128K ctx, 1 user	19.0	⚡ spec-decode (MTP)
Qwen3.8 27B	no speculative decoding	vLLM 0.31.0, 128K ctx, 1 user	11.0
Nemotron 3 Super 120B-A12B legacy	+ MTP (k=3)	vLLM 0.31.0, 128K ctx, 1 user	23.0	⚡ spec-decode (MTP)
Nemotron 3 Super 120B-A12B legacy	no speculative decoding	vLLM 0.31.0, 128K ctx, 1 user	16.1
Llama 3.1 8B legacy		NVIDIA NIM 2.0.9, 128K ctx, 1 user	26.4

Cristian Tala, last updated 6 Oct 2026. Single-user decode tok/s: median of 3 streamed requests with a ~200-word prompt; every model loaded with 128K context and memory budgeted for 4 concurrent users. Quant only stated for Qwen3.6 35B-A3B (NVFP4). With 4 users, total throughput was higher (e.g. 240 tok/s for Qwen3.6 35B-A3B + MTP). Eagle3 slowed gpt-oss down (96.2 to 59.4 on 20B), so it is not listed.

Source: https://ctala.github.io/local-llm-agentic-workflows/benchmarks/

Blank fields = not stated in the source. We never estimate build data; GPU street prices above are separately sourced and dated.

Rent before you buy: try this model on a cloud GPU for a few dollars first: Runpod (referral link; sign up with Google to get the new-user bonus)

Compare: DGX Spark vs Mac Studio vs Strix Halo: measured tok/s and tok/s per watt

Quick answers: Can DGX Spark (GB10) run Gemma 4 26B-A4B? · Can DGX Spark (GB10) run Nemotron 3 Nano Omni 30B? · Can DGX Spark (GB10) run Nemotron 3 Super 120B-A12B? · Can DGX Spark (GB10) run Nemotron 3.5 Lightning 30B-A3B? · Can DGX Spark (GB10) run Qwen3.6 35B-A3B? · Can DGX Spark (GB10) run Qwen3.8 27B? · Can DGX Spark (GB10) run Qwen3.8 Flash-Next? · Can DGX Spark (GB10) run gpt-oss 120B? · Can DGX Spark (GB10) run gpt-oss 20B?