AI Workstation Comparison: RTX 5090 vs GB10 (HP ZGX)

The RTX 5090 and the GB10 are the two machines every local-AI builder is weighing right now — and they are almost perfect opposites. The 5090 is a 32 GB bandwidth monster. The GB10 — the Grace Blackwell superchip inside both NVIDIA's DGX Spark and HP's ZGX — is a 128 GB unified-memory box on a comparatively narrow memory bus. One runs small models blisteringly fast; the other runs models the 5090 cannot even load. This is the comparison that actually decides a local AI workstation, across the five things that matter: memory, speed, MoE, long context, and power.

(The GB10 is the same silicon in the HP ZGX and the NVIDIA DGX Spark, so published DGX Spark figures apply to the ZGX. Benchmark numbers below are from published RTX 5090 and DGX Spark/GB10 testing.)

The two machines at a glance

Same ballpark money, opposite philosophies: the 5090 maximizes bandwidth per dollar; the GB10 maximizes memory per watt.

Memory capacity: what each can actually hold

This is the first fork in the road. The 5090's 32 GB comfortably runs dense models up to ~32B at 4-bit, plus larger MoE models if they fit — but a 70B needs ~40 GB+ at 4-bit and simply will not load. The GB10's 128 GB of unified memory runs 70B models without brutal quantization, 120B-class MoE at 4-bit, and — paired over its ConnectX networking — even Qwen3-235B across two units. Capacity is the entire reason the GB10 exists. (One caveat: unified memory is shared with the OS and CPU, so usable headroom is a bit under 128 GB.)

Inference speed: read two numbers, not one (PP and TG)

Every LLM has two speeds, and they are the heart of this comparison. Prompt Processing (PP, "prefill") is the model reading your prompt — it is compute-bound and runs in the thousands of tok/s. Token Generation (TG, "decode") is the model writing the answer one token at a time — it is memory-bandwidth-bound and runs in the tens to low hundreds. If you look at only one of them, you will misjudge both machines. (New to this split? Start with Prompt Processing vs Token Generation.)

Because TG is bandwidth-bound, the 5090's ~1.8 TB/s versus the GB10's ~273 GB/s (≈6.5×) is decisive — for models that fit in 32 GB:

Read it this way: when a model fits the 5090, it wins both PP and TG by roughly 5–6×. The instant a model does not fit 32 GB, the 5090's speed is moot — it cannot run the thing — and the GB10 is the only box that finishes the job at all.

MoE models: the great equalizer

Mixture-of-Experts models (Kimi K2.6, Qwen3 MoE, gpt-oss) activate only a few billion parameters per token, so they read far less memory per token than a dense model of the same size. That has two consequences here:

• The 5090 loves small MoE. It posts ~234 tok/s on a 30B-parameter MoE — faster than its own dense 8B — because each token only touches the active experts.
• The GB10 was built for big MoE. NVIDIA explicitly tuned GB10 for "Blackwell 4-bit MoE inference." Its 128 GB holds giant MoE models the 5090 can't (gpt-oss-120B at ~40–55 tok/s; Qwen3-235B across two units), and because MoE reads only the active experts per token, the GB10's bandwidth penalty hurts less than it would on a dense model of the same total size.

Net: small MoE → the 5090 (raw speed); giant 4-bit MoE that won't fit 32 GB → the GB10 (the only option, at genuinely usable speeds).

Long-context testing: capacity versus throughput

Long context is where the two phases and the two machines collide. The KV cache grows with every token of context (why), so a long session needs both room to store the cache and bandwidth to stream it each step:

• Capacity (GB10 wins): 128 GB can hold a large model and a long-context KV cache the 5090 has no room for. On the 5090 you hit an out-of-memory wall well before the GB10 does.
• Ingest throughput (5090 wins): feeding a 100K-token prompt is PP — compute-bound — and the 5090 ingests it ~6× faster. But then it has to keep the model and that cache resident, which on big models it can't.
• Streaming under long context (mixed): as context grows, TG slows on both, but the GB10's narrow bus feels it more.

Practical verdict: for long-context work on a large model, the GB10 is often the only machine that completes the run; the 5090 is faster only inside its 32 GB ceiling.

What a GB10 pair actually did, three months on

Everything above is a spec-sheet comparison, published in June. Since then we spent a month running three flagship models on two GB10s at TP=2, so the capacity argument can be replaced with measurements. Full detail in Best Model for a Dual DGX Spark.

Three things worth pulling out.

The capacity is real, not theoretical. 835,348 tokens retrieved exactly, at three depths — a measurement, not a configured maximum. The 5090 is not in this conversation at all: the smallest published quantisation of that model is 72.5 GB against a 32.6 GB card.

The interconnect is a non-event. Tensor-parallel traffic sits at 1.3% of the 200 Gb link during decode, about 3.3 MB of all-reduce per generated token. A far slower cable would produce the same numbers. If you were worried about needing exotic networking to pair two boxes, don't be.

And the PP/TG split above produces a result that reverses the ordering. Ask both machines to look at something rather than write, and the desktop card wins: the same twelve video frames took 0.52 s each on one 5090 against 1.26 s on the pair. A vision request is almost all prefill — the compute-bound half — and one 5090 has more compute than two GB10s. Decode is the opposite regime. Two machines, two workloads, and the ordering flips between them, which is this article's argument arriving from an unexpected direction.

One practical note: each of those flagships occupies roughly 119 GB of the 121 GB a node reports, so a pair runs exactly one at a time and switching is about twelve minutes of no service.

Power consumption: not close

The 5090's GPU alone draws 575 W, and a full 5090 rig (CPU, board, fans) pulls 700–900 W from the wall, needs a beefy PSU, and dumps a lot of heat. The entire GB10 system peaks at 240 W (the GB10 SoC itself is 140 W), runs off a small power brick, and sits near-silent on a desk.

So for the big models only the GB10 can run, it does the job at a fraction of the power. For models that fit the 5090, the 5090 still wins tokens-per-watt (it's simply doing far more tok/s) — but it wins by burning a lot more watts to get there.

Which one should you buy?

The honest take: these aren't really competitors — they're complements. The 5090 is a drag racer; the GB10 is a cargo van. The dream local setup, if you can swing it, is a 5090 for fast iteration on small models plus a GB10 for the big-model and long-context jobs the 5090 can't touch (and you can pair two GB10s for 235B-class models — we ran a 304B model across two of them).

Bottom line

• Memory: 32 GB vs 128 GB — the GB10 runs what the 5090 can't.
• Speed: the 5090 is ~5–6× faster on both PP and TG — on models that fit its 32 GB.
• MoE: 5090 for small MoE; GB10 for the giant 4-bit MoE it was built for.
• Long context: ingest throughput (5090) vs the capacity to finish at all (GB10) — pick your bottleneck.
• Power: the GB10 sips ~240 W system vs the 5090's 575 W GPU.

Related reading

• Best Model for a Dual DGX Spark: DeepSeek-V4-Flash vs Qwen3.8-Flash-Next vs GLM-5.3-Flash — a month of measurements on two of these boxes

• Prompt Processing vs Token Generation: the Two Speeds of an LLM

• Will This LLM Fit My GPU? VRAM Requirements for Every Model Size

• The KV Cache: the trick that makes LLMs fast — and slow

• Quantization Methods Compared: GGUF, AWQ, GPTQ, EXL2, NVFP4