x/nxpc · 
Yesterday, Jensen Huang dropped $12.93 billion on Hugging Face — the biggest open-weight model hub on the planet. This morning, Nvidia announced RTX Spark AI PCs landing in October. The cloud giants are consolidating, the market is rallying, and the message from the top of the industry couldn't be clearer: open models you can run yourself are the future.
Which raises the question every local LLM enthusiast asked in unison this morning: so where am I supposed to get enough VRAM to run those models without selling a kidney?
Because here's the brutal reality check I pulled from live listings today (September 4, 2026): an RTX 5090 — MSRP $1,999 — is going for $4,849 to $6,688 on Amazon right now. That's 32GB of VRAM at up to $209 per gigabyte. Meanwhile, sitting quietly in the same search results, there's a workstation card nobody talks about at brunch: the Intel Arc Pro B70 — also 32GB, for $1,299.
And the math gets unhinged when you buy three of them.
For local LLM inference, forget FLOPS. Token generation is a memory-bandwidth-and-capacity problem: every generated token requires reading model weights, and if the model doesn't fit in VRAM, you're offloading to system RAM and watching your tokens/sec fall off a cliff. A 70B model at Q4 quantization wants roughly 40–45GB of VRAM. A 100B-class MoE model wants even more (though MoE's sparse active parameters make it much friendlier to slow memory).
So the real question for an "Ultimate Local LLM Box" is: how much VRAM can you stack per dollar?
| Card | VRAM | Price (live, US) | $/GB VRAM | Bandwidth |
|---|---|---|---|---|
| Intel Arc Pro B70 (ASRock Creator) | 32GB GDDR6 | $1,299.99 | $40.62/GB | 608 GB/s |
| ASRock Arc Pro B60 | 24GB GDDR6 | $649.99 | $27.08/GB | 456 GB/s |
| Radeon AI PRO R9700 (ASRock) | 32GB GDDR6 | $1,699.99 | $53.12/GB | ~640 GB/s |
| RTX 5090 (MSRP) | 32GB GDDR7 | $1,999 (if you can find it) | $62.47/GB | 1,792 GB/s |
| RTX 5090 (Amazon street, today) | 32GB GDDR7 | $4,849–$6,688 | $152–$209/GB | 1,792 GB/s |
| Renewed RTX 3090 | 24GB GDDR6X | $1,549–$1,829 | $64.54–$76.21/GB | 936 GB/s |
The B70 is at roughly $41/GB — a quarter of the RTX 5090's street price per gigabyte, and it's brand new with a warranty, unlike those "renewed" 3090s that were probably mining in a previous life.
Yes, the 5090 has nearly 3× the memory bandwidth (1,792 vs 608 GB/s) and its CUDA stack is bulletproof. If single-GPU token velocity is your religion, it's still the king. But if your goal is running big models that fit, capacity wins — and Intel is playing a completely different game.
Pulled straight from ASRock's spec sheet:
It launched March 25, 2026 at $949 MSRP — Street pricing crept up since (supply and demand doing their thing), which reviewers like Alex Ziskind covered in his "cheapest path to 96GB of VRAM" deep-dive. Even at today's $1,299, nothing else touches it on VRAM per dollar for new silicon.
Let's be honest about methodology: these numbers come from different sources, quantizations, and context lengths — treat them as ranges, not gospel. But they're real user and lab data, and they tell a coherent story.
Single Arc Pro B70 (32GB):
| Model | Backend | Tok/s (generation) | Source |
|---|---|---|---|
| Qwen 3.6 27B (dense, Q4) | llama.cpp SYCL build | ~22 | LLMRequirements testing |
| Qwen3.6-35B-A3B (MoE) | llama.cpp Vulkan | ~54.6 | r/LocalLLM user benchmark |
| Qwen3.6-35B-A3B (MoE) | community re-test | ~65 | r/LocalLLaMA |
| Gemma 4 26B A4B (MoE) | SYCL + llama-ui | ~70 | Verified Amazon buyer (Germany) |
Multi-GPU scaling (the whole point of the triple-stack):
| Setup | Model | Throughput | Source |
|---|---|---|---|
| 1× B70 → 2× B70 | Llama 3.1 8B single-user | 35.4 → 70.3 tok/s (1.99×) | Puget Systems lab |
| 4× B70 | Llama 3.1 8B (batched serving) | ~12,000 tok/s aggregate | StorageReview |
| 4× B70 vs RTX Pro 6000 | Mistral Small 24B, full concurrency | +65% for the B70 quad | StorageReview |
The competition at 70B-class (where VRAM capacity decides everything):
| Setup | Llama 3 70B Q4 | Notes |
|---|---|---|
| RTX 5090 (32GB) | ~12.8 tok/s | Needs partial CPU offload; 14–22 with tuning |
| RTX 3090 (24GB) | ~5.2 tok/s | Heavy offload penalty; ~10 tok/s optimized |
| 2× RTX 3090 (48GB) | mid-to-high teens | Classic budget dual-card play |
| 3× B70 (96GB) | fits fully on GPU | 70B Q4/Q5/Q6 + long context, headroom for 100B+ MoE quants |
That last row is the pitch. A 70B model that just fits across 96GB generates tokens at full GPU speed instead of limping through system RAM — and you've got room for big KV caches at long context, plus the ability to load quantized 100B+ mixture-of-experts models that a 32GB card simply cannot hold.
The stack, at live US pricing:
| Component | Pick | Price (US) |
|---|---|---|
| GPU ×3 | ASRock Arc Pro B70 Creator 32GB | $1,299.99 × 3 = $3,899.97 |
| Platform | Threadripper / AM5 with PCIe 5.0 and enough lanes (bifurcation x8/x8/x8 or riser sled) | ~$800–1,200 |
| RAM | 128GB DDR5 (page file/KV overflow + everyday sanity) | ~$300 |
| PSU | 1200W 80+ Platinum (3× 230W GPUs + headroom) | ~$250 |
| Chassis | Full tower with stacked airflow | ~$200 |
Ballpark total: ~$5,500–5,800 USD. Newegg's insider writeup on the ABS 3× B70 AI workstation put a prebuilt at roughly one-third the cost of an RTX PRO 6000-based workstation with the same 96GB — and today, you could nearly buy this entire triple-GPU rig for the price of a single scalped RTX 5090. Let that sink in.
The B70's blower design makes the triple-stack actually thermally sane — no aftermarket cooling gymnastics like the legendary quad-3090 builds Digital Spaceport has been doing.
Here's where I have to be the honest reviewer and not just a hype merchant:
The consensus from every source I cross-referenced: CUDA/NVIDIA remains the "set it and forget it" path; B70 is the "70% of the experience for 40% of the money, and 3× the capacity" path. If your Saturday night joy is tweaking quantization parameters over a coffee, the B70 is your card. If you want to press one button and never open a terminal, buy NVIDIA and pay the tax.
My verdict: the software tax is real but shrinking, and the capacity win is permanent. For a hobbyist LLM lab — especially one that wants to host models for the whole household or a small team — three B70s is the most VRAM-per-dollar in new silicon today, full stop.
| Product | USD (Amazon US) | CAD (Amazon CA) | Stock/Notes |
|---|---|---|---|
| ASRock Arc Pro B70 Creator 32GB | $1,299.99 | CA$2,064.17 | In stock, 200+ bought/mo, 5.0★ |
| 3× B70 (the 96GB stack) | $3,899.97 | ~CA$6,192 | Blower-cooled, stackable |
| MAXSUN B70 32G Turbo | $1,999.00 | CA$2,793.60 | 10 left |
| GUNNIR B70 TF 32GB | $1,828.99 | CA$2,542.11 | 290W TBP variant |
| ASRock Arc Pro B60 24GB (budget alt) | $649.99 | CA$1,036.47 | Insane value at $27/GB |
| ASRock Radeon AI PRO R9700 32GB | $1,699.99 | limited CA availability | ~640 GB/s, faster than B70 |
| ASUS ROG Astral RTX 5090 32GB | $5,856.54 | CA$7,662–8,199 | Scalper-tier pricing |
| MSI RTX 5090 Suprim Liquid | $4,849.99 | CA$17,182 (lol) | "Cheapest" 5090 on Amazon US |
| RTX 3090 FE (Renewed) | $1,729.99 | — | 7 left, no warranty safety net |
| RTX 3090 HP OEM (Renewed) | $1,549.99 | — | Cheapest 24GB listed |
Canadian friends: at CA$2,064 per card, the triple-stack is ~CA$6,192 — right at the price of ONE street-priced RTX 5090 in Canada (CA$7,662+) with three times the memory. The math up north is even more lopsided in the B70's favor.
Nvidia buying Hugging Face for $12.93B and shipping RTX Spark AI PCs in October tells you where the puck is going: the open-model ecosystem is being taken very, very seriously at the highest level. Every model on that hub is a candidate for your local box. The gap between "what the frontier labs serve you" and "what you can run at home" keeps shrinking — and home-rig VRAM capacity is the bottleneck Intel is attacking.
When the biggest company in tech spends $13B to get closer to open weights, building a box to run those weights yourself stops looking like a hobby and starts looking like foresight.
Prices and availability verified live at time of writing and change fast — especially the 5090 street prices, which appear to be written in pencil by scalpers. Benchmark figures are aggregated from cited community and lab sources; your results will vary with quantization, context length, and backend build.