Alex Ziskind's latest video carries a headline that does its job: "AMD's Strix Successor Just Caught the M4 Pro." And on one benchmark, it genuinely did. But the machine in the video — the Beelink SER10 Max, built on AMD's brand-new Ryzen AI 9 HX 470 "Gorgon Point" chip — is a more interesting and more cautionary buy than the title suggests, especially if you're shopping it to run local LLMs. We haven't tested one; what follows summarizes Ziskind's measured results and grounds them in primary specs and the wider local-AI picture.
First, the name games: this is Strix Point's successor, not Strix Halo's
This matters more than it sounds. The local-AI crowd has spent 2026 obsessing over Strix Halo — the Ryzen AI Max+ 395, with its 256-bit memory bus and up to 128 GB of unified memory. The chip in the Beelink SER10 Max is not that. It's Gorgon Point, a mid-cycle refresh of the smaller Strix Point laptop platform. Per AMD's own spec sheet, the Ryzen AI 9 HX 470 is a 12-core part (4 Zen 5 + 8 Zen 5c) boosting to ~5.2 GHz, paired with the same Radeon 890M iGPU (16 RDNA 3.5 compute units) and a 55-TOPS XDNA 2 NPU that the year-old SER9 already had. Independent coverage confirms Gorgon Point is exactly that — a clock-and-polish refresh on Strix Point's bones, not a new architecture.
So set expectations accordingly: this is a capable thin-and-light-class mini PC, not a 70B-model machine. The SER10 Max ships with 32 or 64 GB (a 96 GB/2 TB config exists for around $2,300), and about half the memory can be carved out for the iGPU — Ziskind's 64 GB unit exposed ~29 GB as graphics memory.
The CPU story: one big leap, mostly flat, and a Geekbench number worth double-checking
Ziskind's developer suite tells a split-screen story. On the V8 Web Tooling benchmark (a real TypeScript/Babel/Terser toolchain), the SER10 jumped from the SER9's ~20 to 34.14 on the TypeScript score — a ~65% generational gain that lands it within 5% of both the base M4 and M4 Pro Mac minis (~35.99). That's the "caught the Mac" moment, and it's real.
Everywhere else, it's flat. His all-core Python test: SER9 28.64s vs SER10 28.9s — a quarter-second apart, i.e. the same. His synthetic .NET compile: SER9 ~91s vs SER10 ~90.9s — again, identical. On the real-world Umbraco .NET build, the SER10 actually came in slightly slower (161s) than the SER9 (149s). None of that is a knock on the chip so much as physics: same iGPU, same core count, mild clock bump.
One number deserves a flag. The video's chart shows the M4 Pro at ~15,321 Geekbench 6 multi-core — and a sharp commenter (@charlievarley) caught that this looks like a base-M4 score, not an M4 Pro one. They're right: Geekbench's published Mac mini M4 Pro (14-core) average is ~3,821 single / ~22,430 multi across 5,000+ results. Using the correct figure, the multi-core gap between these mini PCs and the M4 Pro is far wider than the video's chart implied — the M4 Pro stays comfortably ahead on heavy compiles, which matches the Umbraco result. Worth knowing before you read "caught the M4 Pro" as a multi-core claim.
The local-LLM story: your AI runs on the wrong chip by default
Here's the part that earns this box a place on a local-AI site. The SER10 has three things that can run a model — CPU, the Radeon 890M iGPU, and the XDNA 2 NPU — and out of the box on Windows, Ollama uses the CPU. Ziskind ran Qwen 2.5 7B and watched the CPU peg while the GPU and NPU sat idle, landing a sluggish 14.2 tok/s. This isn't a one-off: AMD's own GAIA bug tracker (issue #1295) documents the same "appears CPU-bound / not GPU-accelerated by default" behavior on Strix-class parts.
The fix is a single environment flag. Setting OLLAMA_VULKAN=true moves inference onto the iGPU via llama.cpp's Vulkan backend — necessary because ROCm still doesn't support these RDNA 3.5 iGPUs (gfx1150) on Windows, so Vulkan is the real path. Flip it, and Ziskind's numbers jumped: Llama 3.2 3B went 27 → 37.5 tok/s, Qwen 2.5 1.5B went 47.5 → 68.3 tok/s. (If you want the click-by-click, this Vulkan-for-Ollama walkthrough covers it.)
The NPU is the wildcard everyone asks about and nobody benchmarks. Using AMD's Lemonade Server — an OpenAI-compatible local server that routes prefill to the NPU and decode to the iGPU — Ziskind got ~14.3 tok/s on a 1B model. The theory is sound: prefill (prompt processing) is compute-bound and the NPU cuts time-to-first-token roughly in half, while decode is memory-bound and better left to the GPU. In practice today it's promising plumbing, not a reason to buy.
The number that should drive your decision: memory bandwidth
Token generation is memory-bandwidth-bound — every token requires reading the model's weights out of RAM — so for local LLMs, bandwidth predicts speed better than any CPU score. And this is where the "successor" gets awkward:
- SER10 Max — DDR5-5600 (SODIMM, upgradeable): ~90 GB/s. Ziskind notes Beelink moved to user-upgradeable DDR5 here.
- SER9 — LPDDR5X-7500 (soldered): ~120 GB/s. The older machine's memory is faster (per Beelink's own SER9 spec).
- Strix Halo (Ryzen AI Max+ 395) — LPDDR5X-8000, 256-bit: ~256 GB/s. Nearly 3× the SER10.
Read that again: the new SER10 Max may trade away token-generation headroom versus the SER9 it replaces, because Beelink swapped fast soldered LPDDR5X for slower upgradeable DDR5. A commenter (@taggerung890) spotted it instantly — "5600 vs 8000 on the 395 Max+." It's a real trade: you gain upgradeable, cheaper RAM and capacity; you give up the one spec that most governs LLM speed. For 7–14B models at Q4 that's fine. For anything bigger, the bandwidth wall — not the core count — is what you'll hit.
So which one should you actually buy?
Ziskind frames it as three machines, and that's the right framing:
- Want a small, quiet dev box that also dabbles in local AI? The SER10 Max is a fine pick — just budget the 10 minutes to enable Vulkan, and treat it as a 7–14B machine.
- Value-shopping? The SER9 is usually a couple hundred dollars cheaper, has the same 890M iGPU, and — thanks to its faster soldered memory — may match or beat the SER10 at token generation. Used units turn up on eBay too.
- Doing serious CPU/compile work or living in macOS? The M4 Pro Mac mini still leads single-core and IO-heavy builds, with a genuine ~22,400 multi-core — but a 1 TB / more-RAM config runs ~$2,000+ and Apple's memory pricing is brutal.
- Actually buying this to run big local models? Save for a real Strix Halo box. The r/LocalLLaMA consensus, as aggregated buyer guides note, has largely settled on the 128 GB Ryzen AI Max+ 395 minis — like the GMKtec EVO-X2 — for capacity-per-dollar. We cover those in our Strix Halo vs DGX Spark and Framework Desktop guides.
What viewers and owners are saying
The comment section under Ziskind's video skews skeptical — which, for a flat-ish refresh, is fair. The loudest themes:
- Test real models, not toys: "benchmarks of models and context sizes that people are actually using… who's using a 7b model on one of these machines?" — @mrhappy678 on YouTube. (Reasonable; though 7–14B is genuinely what a 890M box is for.)
- The bandwidth catch: "wait the ser10 doesn't use high speed RAM? 5600mt/s vs the 8000mt/s on the 395 max+?" — @taggerung890 on YouTube.
- The Geekbench correction above, from @charlievarley.
- "I'd rather wait for the big one": "Patiently waiting for the 495+ with 192GB RAM…" — @TotusEius on YouTube — i.e. the Strix Halo successor, again underscoring that this isn't that chip.
- One genuinely useful pro-tip on the AMD/Vulkan path: "using Vulkan, you can combine dissimilar GPUs. I'm running a 16 gig RTX and a 16 GB Radeon, Gemma 31B, 128K context, entirely on GPUs." — @MichaelRainabbaRichardson on YouTube.
On the price grumbling ("in 2024 I bought my GMKtec Ryzen 9 7940HS 32GB for $700; now something similar is $1,300" — @Ahamshep), they're not wrong — the 2025–26 memory crunch pushed this whole mini-PC class up, which is exactly why the SER9's value case is stronger than usual.
Sources & how we researched this
We have not tested a SER10 Max first-hand; this piece summarizes Alex Ziskind's "AMD's Strix Successor Just Caught the M4 Pro" (we skipped the sponsor segment) and cross-checks his findings against primary sources: AMD's HX 470 spec page and Gorgon Point coverage for the chip; Geekbench Browser for the corrected M4 Pro scores; the llama.cpp Vulkan discussion and AMD GAIA issue #1295 for the "CPU-by-default" behavior; AMD's Lemonade playbook for the NPU+iGPU hybrid path; and Beelink's SER9 LLM notes for the memory spec. Tokens/sec are Ziskind's measured figures; treat single-run numbers as directional, not gospel.
Related guides
- Strix Halo vs DGX Spark: Running 70B Locally, According to People Who Own Both
- Framework Desktop Buyer's Guide: The Repairable Strix Halo Box
- Can I run it? — check which models fit your machine · Quant picker
Bottom line: the SER10 Max is a solid little Windows dev box that, with one flag flipped, makes a perfectly good 7–14B local-AI machine. Just don't let the "caught the M4 Pro" headline talk you into it as a heavy-compile or big-model workhorse — on multi-core it isn't, and on the spec that matters most for LLM speed, memory bandwidth, it's a step behind both its own predecessor and the Strix Halo boxes the local-AI crowd actually reaches for.
