TL;DR: A nine-card P106-100 rig bought for $35 just ran Qwen3.6-35B-A3B at 30+ tok/s in Ollama — 54GB of VRAM for less than one used RTX 3060 costs. The math checks out, but only for small-active MoE models, and NVIDIA’s driver support for these 2017 cards ends at the 580 branch. This is a project, not a plan.

	9× P106-100 (54GB)	Used RTX 3060 12GB	Used RTX 3090 24GB
Best for	Capacity on pennies, tinkering	Cheapest supported CUDA card	Speed + support, the default pick
Price (Oct 2026)	$17–$21/card in eBay lots ($155–$190 for nine)	$260–$296	$1,399–$1,450
The catch	192 GB/s per card, no Tensor Cores, driver EOL, ~1,080W TDP ceiling	12GB can’t even load a 21GB MoE	$58/GB of VRAM vs ~$3/GB

Honest take: If you have to ask whether to build this, buy the used RTX 3090 instead. Build the P106 rig only if you’d enjoy the project itself — the hardware is nearly free, but the speed, power draw, and 2026 driver cliff mean it’s a capacity hack, not a daily driver.

A Reddit user on r/LocalLLaMA picked up a retired nine-GPU mining rig for 3,000 rubles — about $35 — from a garage listing on Avito, the Russian classifieds site. The seller originally asked 5,000 and refused to let the buyer test it first, so $35 was the no-warranty gamble price. All nine cards worked. The rig had no SSD, so it boots from a USB stick, and the host is a weak Celeron with very little system RAM — a typical 2017 mining build, where the CPU’s only job was feeding shares to a pool.

The first real test: Qwen3.6-35B-A3B running in Ollama at over 30 tokens per second, split across nine NVIDIA P106-100 cards with 6GB each — 54GB of pooled VRAM at roughly $0.65 per gigabyte. Tom’s Hardware, Guru3D, and Wccftech all picked up the story within days, and the obvious question landed in our inbox the same week: should a US home-lab builder copy this?

The answer needs actual math, because both the impressive part and the trap are real.

What a P106-100 actually is

The P106-100 is a GTX 1060 6GB with the display outputs deleted. NVIDIA sold it to miners during the 2017 Ethereum boom as a compute-only card: same GP106 Pascal silicon, same 6GB of GDDR5 on a 192-bit bus, same 120W TDP with a single 6-pin connector.

Spec	P106-100
Architecture	Pascal (GP106), 2017
VRAM	6GB GDDR5, 192-bit
Memory bandwidth	192.2 GB/s
TDP	120W (one 6-pin)
CUDA compute capability	6.1
Tensor Cores	None
Display outputs	None
PCIe	Spec sheets conflict — Gigabyte’s own partner card lists PCI-E 1.1, others say 3.0 x16; in a mining frame they hang off x1 risers regardless

Two of those rows do most of the damage. No Tensor Cores means prompt processing — the compute-bound half of inference — is slow on any model. And 192 GB/s of bandwidth per card is about one-fifth of a used RTX 3090’s 936 GB/s, which caps decode speed on anything that has to read a lot of weights per token.

That last clause is the entire story. Here’s why.

Why 30 tok/s is believable — and why it only works for MoE

Decode speed is bandwidth-bound. The ceiling is simple arithmetic:

tok/s ceiling = memory bandwidth (GB/s) ÷ weights read per token (GB)

Qwen3.6-35B-A3B is a mixture-of-experts model: 35B total parameters, but only ~3B active per token. At Q4_K_M the full GGUF is ~21GB — which is why it fits comfortably in the rig’s 54GB pool — but each token only reads about 1.9GB of weights.

When Ollama splits a model across GPUs it splits by layers, and layers execute in sequence — so at any moment roughly one card is working. Effective bandwidth is one card’s 192 GB/s, and the ceiling is:

192 GB/s ÷ 1.9 GB/token ≈ 101 tok/s theoretical max

The observed 30+ tok/s is about 30% of that ceiling — low, as expected with x1 risers, a Celeron host, and USB-stick storage, but completely plausible. For calibration: the same model does 107 tok/s on a single used RTX 3090 and 120 tok/s on an RTX 4090.

Now run the same formula on a dense model and the rig falls apart. Llama 3.3 70B at Q4_K_M is 42.5GB — it fits in 54GB, which sounds like the whole point of the rig — but a dense model reads all 42.5GB every token:

192 GB/s ÷ 42.5 GB/token ≈ 4.5 tok/s ceiling — before any overhead

That’s a computed ceiling, not a benchmark, and real-world numbers land at 70–95% of ceiling on well-configured systems — call it 3 tok/s on a good day. A dense 27B at Q4 (~16GB) ceilings around 12 tok/s. Reading speed is 7–10 tok/s, so dense models on this rig range from “barely tolerable” to “walk away from the keyboard.”

The rule the $35 rig proves: cheap VRAM capacity is only useful when paired with models that read a small fraction of their weights per token. In 2026 that’s a real and growing category — Qwen3.6-35B-A3B, gpt-oss-20b, Gemma 4 26B-A4B — which is exactly why this rig is more interesting today than it would have been in 2023, when everything worth running was dense.

What it costs to replicate in the US

You won’t find a $35 garage rig on demand, but the cards themselves are genuinely cheap on eBay as of October 2026:

• Single tested cards: $25–$62 asking, with at least one recent sale at $25
• Lot of 10 (tested): $170 — about $17 per card
• Lot of 24: $499.99 — about $21 per card

Nine cards from a lot runs $155–$190, or roughly $3 per GB of VRAM. A used RTX 3060 12GB is $260–$296 ($22–$25/GB) and a used RTX 3090 is $1,399–$1,450 ($58–$60/GB). On capacity per dollar, nothing touches the mining cards.

But cards aren’t a rig. You still need a mining frame, nine x1 risers, a motherboard with enough slots, a CPU, RAM, storage, and a PSU (or two) that can feed nine 6-pin connectors — and if you’re not buying a complete retired rig like the Avito find, sourcing all of that pushes the project well past the cards-only number. The realistic play is the same one the Reddit buyer made: find a complete retired mining rig locally (Facebook Marketplace and Craigslist still have them) and pay scrap prices, because to everyone except LLM hobbyists, that’s what it is.

One number that surprises people: power is not the dealbreaker at US rates. Nine cards at 120W TDP is a 1,080W ceiling — under a standard 15A/1,800W circuit — and at $0.12/kWh that’s about $0.13 per hour flat-out. In practice layer-split inference keeps most cards waiting most of the time, so sustained draw sits well below TDP. The real cost is that you’re paying any watts at all for 30 tok/s when a 350W RTX 3090 delivers 107 tok/s — per token generated, the old cards burn several times the energy. Our 24/7 home AI server power math covers how to think about idle draw if you plan to leave it running.

The 2026 driver cliff is the real expiration date

This is the part the viral coverage mostly skipped. Pascal is at the end of its software life:

• NVIDIA’s driver roadmap: the 580 branch is the last to support Maxwell, Pascal, and Volta. The Linux 590 beta already drops GTX 10-series cards. Security updates for the 580 branch are planned through October 2028, but no new features or optimizations.
• CUDA 13.0 removed Pascal entirely — offline compilation and library support for pre-Turing architectures are gone from the toolkit. Any inference project that moves to a CUDA 13-only build drops these cards automatically, without ever announcing a “deprecation.”
• Ollama and llama.cpp still work — both shipped CUDA 12 build paths that explicitly include compute capability 6.1 as of their mid-August 2026 releases. But that’s a status, not a promise. The practical move is to pin your driver and stop auto-updating:

$ sudo apt-mark hold nvidia-driver-580
nvidia-driver-580 set on hold.

There’s also a known Pascal-specific failure you’ll hit sooner or later: flash attention. llama.cpp’s flash_attn_ext_f16 kernel has shipped builds with no device code for arch 610, which fails with CUDA kernel flash_attn_ext_f16 has no device code compatible with CUDA arch 610. The fix is to run without flash attention (drop -fa in llama.cpp; Ollama handles the fallback automatically on these cards) and accept the KV-cache memory penalty. On a 54GB rig you have the room; on a single 6GB card you don’t.

If you want pooled-VRAM capacity without adopting 2017 hardware on hospice care, the honest alternatives are a used RTX 3090 — still the value king — or two cards on one board, where at least the silicon has a supported future. And check what your target model actually needs in our VRAM calculator before buying anything; a lot of readers discover their model of choice fits in hardware they already own.

Who should actually build this

Build it if you can get a complete rig near scrap price, your electricity is cheap or free, you’re comfortable on Linux with headless GPUs (remember: no display outputs — you need iGPU or SSH), and the appeal is the project itself. Running a 21GB MoE fully in VRAM for under $200 in cards is legitimately the cheapest way to hold a model that size in 2026, and 30 tok/s is above reading speed. As a dedicated box serving one MoE model to your home network, it works.

Skip it if you want speed (a 3090 is 3.5× faster on the same model), you want dense-model quality (the 4.5 tok/s ceiling on 70B is physics, not configuration), you plan to run it past 2028 (driver EOL), or you’d be buying parts piecemeal at retail — at that point you’re assembling a worse computer for 3090 money. Distributed-VRAM schemes have the same shape every time, and we reached the same verdict on EXO’s pooled-3090 pitch: pooling adds capacity, never speed.

It’s also worth saying plainly: a single P106-100 is not a starter GPU. One card is a 6GB Pascal with no display outputs and a dying driver stack — a used RTX 3060 12GB at $260 is the actual floor for a supported local AI card, and here’s what 6GB honestly runs if you’re stuck there. The P106 story only works at nine cards, where capacity crosses a threshold that single consumer cards can’t reach for the money.

What to actually buy

Prices as of October 2026, all verified in the comparison above:

Your situation	The move	Price	Where
Want the project — capacity for pennies, eyes open	9× P106-100 (54GB pooled)	~$17–$21/card in lots	Check price
Want the cheapest supported CUDA card	Used RTX 3060 12GB	$260–$296	Check price
Want this exact model at 107 tok/s, one card, no drama	Used RTX 3090 24GB	$1,399–$1,450	Check price
Undecided — test a 35B MoE on real hardware first	Rented RTX 3090	from $0.07/hr	Vast.ai

FAQ

Can nine P106-100s run a 70B model? It loads — 42.5GB at Q4_K_M fits in 54GB — but dense 70B reads all 42.5GB per token, and at 192 GB/s per card the ceiling is ~4.5 tok/s. Loading isn’t the same as running. Stick to MoE models with small active parameter counts.

Why is a mining rig suddenly viable for AI when it wasn’t in 2023? Small-active MoE models. In 2023 the good local models were dense, and dense models need bandwidth the P106 doesn’t have. A 2026 MoE like Qwen3.6-35B-A3B reads ~1.9GB per token instead of 21GB, which puts usable speed within reach of 2017 bandwidth.

Do P106-100 cards work in Ollama out of the box? Yes, as of October 2026 — compute capability 6.1 is still in Ollama’s and llama.cpp’s CUDA 12 build paths. Pin your NVIDIA driver to the 580 branch (the last to support Pascal) and expect flash attention to be unavailable on these cards.

What about gaming or image generation on P106s? No display outputs, no Tensor Cores, and 6GB per card that can’t be pooled for a single diffusion model’s activations. These are LLM-inference capacity cards and nothing else.

Is the $35 price realistic? That was one lucky local pickup of a complete rig, bought untested. The repeatable number is eBay lots at $17–$21 per card, plus whatever a frame, risers, board, and PSU cost you — which is why finding a complete retired rig locally is the only version of this that stays cheap.

Recommended Gear

• NVIDIA P106-100 6GB (mining card) — only in multiples, only for MoE inference, only at scrap prices
• Used RTX 3060 12GB — the real floor for a supported single-card setup
• Used RTX 3090 24GB — 936 GB/s and 24GB; the card this site keeps recommending because the math keeps landing on it

Sources

• Reddit user pays $35 for a crypto mining rig left in a garage and gets 54GB of VRAM at 65 cents per GB — Tom’s Hardware
• Retired nine-GPU mining rig bought for $35 provides 54GB VRAM for local LLM inference — Guru3D
• An Old GPU Mining Rig With 9 GPUs Sporting 54GB VRAM Is An LLM User’s Dream — Wccftech
• Reddit user runs 35B model on 54GB VRAM from $35 crypto rig — Hyper.ai
• P106-100: specs and benchmarks — Technical City
• GV-NP106D5-6G specification — Gigabyte
• NVIDIA GPU Driver Changes: Maxwell, Pascal, and Volta Unsupported After Release 580 Series — Guru3D
• NVIDIA Is Ending Driver Support For GeForce GTX 1080 Ti And These Other GPUs — HotHardware
• Pascal GPUs after CUDA 13 — Local AI Master
• CUDA kernel flash_attn_ext_f16 has no device code compatible with CUDA arch 610 — llama.cpp issue #7055
• P106-100 listings — eBay

Last updated October 15, 2026. Prices and specs change; verify current rates before purchasing.

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