NVIDIA

NVIDIA CMP 170HX 64 GB HBM2e (Modified, Ex-Mining)

NVIDIA CMP 170HX 64 GB HBM2e (Modified, Ex-Mining)

Běžná cena €1.600,00 EUR
Běžná cena Výprodejová cena €1.600,00 EUR
Sleva Vyprodáno
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PCIe interface
Modified · Ex-Mining · 64 GB HBM2e

NVIDIA CMP 170HX 64 GB HBM2e — cheap VRAM for resident inference

A GA100-based CMP 170HX with its memory expanded to 64 GB of HBM2e. At €1,600 that is roughly €25 per gigabyte of high-bandwidth VRAM — around a fifth of what large-VRAM professional cards cost. It is modified hardware with real limits, and we spell them out below before you buy.

64 GB
HBM2e VRAM
GA100
Ampere GPU
~25 €/GB
VRAM cost
PCIe 1.0
×4 or ×16 link
Read this first

What this card is — and is not

The CMP 170HX was sold by NVIDIA as a dedicated mining card: a cut-down GA100 with its PCIe link deliberately locked to PCIe 1.0 and most display and compute functions restricted. These units have been modified after the fact to carry 64 GB of HBM2e. That makes them an unusually cheap way to hold a large model entirely in high-bandwidth memory — and a poor choice for anything that depends on fast host transfers, multi-GPU scaling or training.

This is modified hardware operating outside its intended configuration. Compute is partially fused off compared with a full A100, so treat VRAM capacity — not throughput — as the reason to buy. Individual cards vary: some are more fully unlocked than others, and we cannot promise which you will receive. If you need guaranteed, predictable compute, buy an RTX PRO 6000 or a proper datacenter card instead and we will happily quote one.
The PCIe limit

Why the ×16 option costs €150 more

Both versions are locked to PCIe 1.0 signalling — that part cannot be undone. What differs is lane count, and it changes host-to-card bandwidth by 4×:

PCIe 1.0 ×4 — €1,600 ~1 GB/s · filling 64 GB takes roughly a minute
PCIe 1.0 ×16 — €1,750 ~4 GB/s · filling 64 GB takes roughly 15 seconds
Once weights are resident in VRAM, inference runs from HBM2e and the PCIe link barely matters. The link speed dominates model load time, host↔device streaming, and any multi-GPU communication. If you load a model once and serve it for hours, ×4 is fine. If you swap models often, stream data continuously, or plan to split a model across cards, pay the €150.
Both options come from the same batch of cards — the ×16 conversion is work we carry out in-house before dispatch, which is what the €150 covers. Allow a little extra time on ×16 orders for the conversion and re-testing.
Specifications

Technical data

GPU NVIDIA GA100 — Ampere
Memory 64 GB HBM2e (modified — not a stock configuration)
Compute Partially fused off vs A100 — capacity-oriented, not throughput-oriented
Interface PCIe 1.0 ×4 or PCIe 1.0 ×16 (select above)
Host bandwidth ~1 GB/s (×4) · ~4 GB/s (×16)
Display outputs None — compute only
Cooling Passive — requires chassis airflow
Condition Used, ex-mining — tested before dispatch
Warranty — card 6 months
Warranty — modified VRAM 14 days
Best for

Where it fits

  • Serving one large model that stays resident in VRAM — load once, run for hours.
  • Experimenting with big models on a budget, where 64 GB for €1,600 is the whole point.
  • Batch inference jobs that are VRAM-bound rather than transfer-bound.
  • Learning and development on large-model workflows without datacenter-GPU spend.

Where it does not fit

  • Training or fine-tuning — PCIe 1.0 and reduced compute both work against you.
  • Multi-GPU tensor-parallel setups — the interconnect is far too slow.
  • Workloads that stream data continuously from host memory or disk.
  • Anything needing display output, or a card you can rely on for years of production duty.
Questions

FAQ

Why is the VRAM warranty only 14 days?

Because the 64 GB memory configuration is an aftermarket modification the hardware was never designed for. We test every card before dispatch, but we will not pretend a modified memory subsystem carries the same long-term guarantee as a factory part. The card itself is covered for 6 months; the modified VRAM for 14 days. Test it properly as soon as it arrives.

Will my framework see all 64 GB?

In our testing the full capacity is addressable, which is the entire reason to buy this card. Compute capability is a different matter — it is partially restricted versus a real A100, and cards vary between units. Tell us your intended workload before ordering and we will give you a straight answer about whether this is the right purchase.

Can I get a fully unlocked card?

Some units are less restricted than others, but it is genuinely a lottery and we will not sell you a promise we cannot keep. Order on the basis of 64 GB of VRAM at a low price; treat anything beyond that as a bonus.

Can I run several in one machine?

You can physically, and each card keeps its own 64 GB, so independent jobs per card work. What does not work well is splitting a single model across cards — PCIe 1.0 makes tensor-parallel communication the bottleneck by a wide margin.

Does it need special cooling?

Yes. It is a passive card with no fan of its own and expects a server chassis with a proper front-to-back airflow path. It will overheat in a normal desktop case.

How does this compare with buying a professional card?

On VRAM price nothing comes close — roughly €25/GB here against about €141/GB for a 96 GB RTX PRO 6000. On reliability, compute, warranty, PCIe bandwidth and resale, the professional card wins on every count. Pick based on which of those matters to you.

Not sure this is the right card for your workload? Tell us the model and how you intend to serve it and we will say honestly whether this or a Kentino AI build fits better.
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