Source: https://getrigbase.com/compare/dgx-spark-vs-rtx-pro-6000-vs-mac-studio-vs-strix-halo/ Last updated: 2026-10-28 # DGX Spark vs RTX PRO 6000 vs Mac Studio vs Strix Halo for local LLMs Quick answer **Which is fastest: DGX Spark, RTX PRO 6000, Mac Studio or Strix Halo?** On the overlapping 2026 models in RigBase, RTX PRO 6000 leads when the model fits its 96 GB — e.g. Qwen3.8 27B at 150 tok/s. DGX Spark, Mac Studio and Strix Halo trade some speed for 128–512 GB unified memory and lower whole-box power. Sources use different engines and quants, so compare orders of magnitude. Last updated: 28 Oct 2026 Four common “big memory” options for home and lab local LLMs. Every cell is a measured RigBase result; engines and quants differ by source, so use the table for ballpark speed, not single-digit rankings. **Quick answer:** On Qwen3.8 27B, an RTX PRO 6000 measured **150** tok/s vs **14.5** on a DGX Spark (different quants/engines — see table). On gpt-oss 20B, RTX PRO 6000 reached **286.91** tok/s; a Mac Studio measured 116.08. Pick PRO 6000 for raw CUDA speed at 96 GB; Spark / Mac / Strix when you need 128 GB+ unified memory in a small box. ## Measured tok/s on current models | Model | DGX Spark | RTX PRO 6000 | Mac Studio | Strix Halo | |---|---|---|---|---| | [gpt-oss 120B](https://getrigbase.com/models/gpt-oss-120b/) | **44.4** / / [source](https://getrigbase.com/builds/dgx-spark-vllm-23-models-ctala-oct-2026/) | **196.31** / MXFP4 / [source](https://getrigbase.com/builds/rtx-pro-6000-96gb/) | **79.68** / MXFP4 / [source](https://getrigbase.com/builds/m2-ultra-76c-192gb/) | **55.8** / MXFP4 / [source](https://getrigbase.com/builds/minisforum-ms-s1-max-ryzen-ai-max-pro-495-192gb/) | | [Qwen3.8 27B](https://getrigbase.com/models/qwen3-8-27b/) | **14.5** / general, short prompt / [source](https://getrigbase.com/builds/surface-laptop-ultra-rtx-spark-microcenter/) | **150** ⚡ / NVFP4 (W4A16) + MTP (5 draft / [source](https://getrigbase.com/builds/rtx-pro-6000-maxq-qwen38-27b-vaditaslim/) | **50.89** / BF16, 8,192-token context / [source](https://getrigbase.com/builds/4x-mac-studio-m5-ultra-rdma-cluster/) | **12.6** / Q4_K_M / [source](https://getrigbase.com/builds/strix-halo-128gb-qwen38-27b-akitaonrails/) | | [gpt-oss 20B](https://getrigbase.com/models/gpt-oss-20b/) | **96.2** / / [source](https://getrigbase.com/builds/dgx-spark-vllm-23-models-ctala-oct-2026/) | **286.91** / MXFP4 / [source](https://getrigbase.com/builds/rtx-pro-6000-96gb/) | **116.08** / MXFP4 / [source](https://getrigbase.com/builds/m2-ultra-76c-192gb/) | – | | [Qwen3.8 Flash-Next](https://getrigbase.com/models/qwen3-8-flash-next/) | **54.4** / NVFP4 / [source](https://getrigbase.com/builds/mia-2x-dgx-spark-qwen3-8-flash-next-dual-dgx-sparks/) | **76.337** / IQ3_S, 32K prompt, all on th / [source](https://getrigbase.com/builds/epyc-7532-rtx-pro-6000-rtx-3090-ti-qwen38-flash-next/) | – | **82** / custom int4 experts / 6-bit / [source](https://getrigbase.com/builds/strix-halo-r9700-oculink-strixpro-qwen38-flash-next/) | | [GLM-5.3 Flash](https://getrigbase.com/models/glm-5-3-flash/) | **105.3** / NVFP4 / [source](https://getrigbase.com/builds/4x-dgx-spark-glm-53-flash-ringside/) | **265.9** ⚡ / EXL3 4-bit + DFlash2 / [source](https://getrigbase.com/builds/mia-4x-rtx-pro-6000-glm-5-3-flash-exl3-4x-rtx-pro-6000-tensorfold/) | – | – | | [DeepSeek V4.1 Flash](https://getrigbase.com/models/deepseek-v4-1-flash/) | **31.6** / EXL3 2.9-bit / [source](https://getrigbase.com/builds/mia-2x-dgx-spark-deepseek-v4-1-flash-exl3-2x-dgx-sparks/) | – | **30.8** ⚡ / not stated / [source](https://getrigbase.com/builds/omlx-m3-ultra-512gb-deepseek-v41-flash-mtp/) | – | | [Qwen3.6 35B-A3B](https://getrigbase.com/models/qwen3-6-35b-a3b/) | **89.5** / Ollama default, 64K context, / [source](https://getrigbase.com/builds/hp-omnibook-ultra-16-rtx-spark-128gb/) | – | – | **62** / Q4_K_M (Unsloth) / [source](https://getrigbase.com/builds/minisforum-ms-s1-max-strix-halo-qwen36-35b-vitorpontual/) | | [Gemma 4 26B-A4B](https://getrigbase.com/models/gemma-4-26b-a4b/) | **30.8** / no speculative decoding / [source](https://getrigbase.com/builds/dgx-spark-vllm-23-models-ctala-oct-2026/) | – | – | **51.8** / 4-bit / [source](https://getrigbase.com/builds/hp-zbook-ultra-g1a-strix-halo-laptop-petronella/) | ⚡ = speculative decoding (or the only available result used it). Blank = no measured result for that pair yet. Same-method power comparison (Spark vs Mac vs Strix only): [tok/s per watt page](https://getrigbase.com/compare/dgx-spark-vs-mac-studio-vs-strix-halo/). ## How to choose - **RTX PRO 6000 (96 GB)**: fastest CUDA single-card option in this set when the model fits. - **DGX Spark (128 GB GB10)**: small CUDA box, multi-Spark recipes for huge MoE; see [all Spark recipes](https://getrigbase.com/dgx-spark/). - **Mac Studio (up to 512 GB)**: most unified memory; MLX/llama.cpp; strong on large dense models. - **Strix Halo (128 GB)**: cheapest path to 128 GB unified; prefer Vulkan; pair with MTP on dense 27B. More: [all comparisons](https://getrigbase.com/compare/) · [Mac vs PC](https://getrigbase.com/guides/mac-vs-pc-local-ai/) · [VRAM calculator](https://getrigbase.com/can-it-run/) ## FAQ ### Which is fastest: DGX Spark, RTX PRO 6000, Mac Studio or Strix Halo? On the overlapping 2026 models in RigBase, RTX PRO 6000 leads when the model fits its 96 GB — e.g. Qwen3.8 27B at 150 tok/s. DGX Spark, Mac Studio and Strix Halo trade some speed for 128–512 GB unified memory and lower whole-box power. Sources use different engines and quants, so compare orders of magnitude. ### When do I pick the DGX Spark over an RTX PRO 6000? When you need more than ~96 GB for one model (or multi-Spark recipes for 300B+ MoE), CUDA/vLLM, and a small workstation. See the [DGX Spark recipes](https://getrigbase.com/dgx-spark/) page for 1–4 Spark measured setups. ### Where does Strix Halo fit? 128 GB unified memory at PC prices; dense 27B decode is single-digit without MTP. Close to a Spark on some MoE models, slower on dense ones. See the [AMD guide](https://getrigbase.com/guides/amd-rdna-local-llm/). Ad · this space is available **Sponsor this spot: reach people building local AI rigs.** One clearly-labeled slot per page, no tracking scripts. [Sponsor this spot →](https://getrigbase.com/advertise/#inquiry) **Not enough VRAM? Rent before you buy.** Run a 120B model on a rented 24–96 GB cloud GPU by the hour and see the real speed before spending €1,000+. [Try it on Runpod](https://runpod.io?ref=77qcpl2c) Referral link: if you sign up with Google and add $10, Runpod gives you and RigBase bonus credit ([terms](https://docs.runpod.io/get-started/referrals)). It never affects our data.