Hacked Nvidia 4090 GPU driver to enable P2P
A hacker has modified Nvidia’s open-source Linux GPU driver to enable peer‑to‑peer (P2P) memory access over PCIe on RTX 40‑series cards, letting multiple consumer GPUs share VRAM directly without bouncing data through system RAM. Commenters see this as a way to make multi‑GPU AI and compute workloads on 4090‑class hardware much more practical, partially eroding the artificial gap to expensive datacenter parts that offer NVLink and officially supported P2P. The change also reignites debate over Nvidia’s feature locking and market segmentation, the likelihood of future firmware crackdowns, and the broader push to “govern compute” as AI hardware becomes more powerful.
What the hack enables (P2P on RTX 40xx)
- Patch to Nvidia’s open-source kernel driver on Linux enables GPU-to-GPU peer-to-peer (P2P) over PCIe on RTX 4090 and likely most 40xx cards.
- P2P here means one GPU can read/write another GPU’s VRAM directly over PCIe, avoiding a bounce through system RAM and CPU.
- This uses resizable BAR / large BAR to map full VRAM into the shared PCIe address space; IOMMU currently must be disabled.
- It is explicitly not NVLink; bandwidth is much lower than data-center parts with NVLink, but still a significant gain over going via RAM.
Technical limits and performance considerations
- PCIe 4.0 on 4090s caps bandwidth; P2P over PCIe is ~2× slower than 3090 NVLink and far slower than A100/H100 NVLink, but still useful.
- Especially relevant for multi-GPU ML workloads (tensor/model parallelism, KV-cache sharding), less so for gaming or simple data-parallel tasks.
- For many inference workloads, 2×4090 with P2P can easily beat a single workstation/datacenter card; training remains more constrained.
- There’s debate over whether multi-GPU P2P is mainly about adding effective VRAM capacity vs. raw compute.
Nvidia product segmentation and “locked” features
- Many commenters see this as another example of consumer cards shipping with hardware capabilities that are disabled in software for segmentation, pushing “serious” users to expensive A/H-series cards.
- Others argue vendors have economic reasons: one silicon, multiple SKUs lowers manufacturing cost and recoups large R&D by price discrimination.
- Some think the omission of P2P on 40xx may be an oversight; others expect future architectures/firmware to hard-disable such features once hacks appear.
Hardware / build implications
- P2P makes multi-4090 rigs (4–6 GPUs) more attractive for local LLMs and other compute workloads; discussion of lane counts, PCIe switches, large BAR, and NVLink tradeoffs.
- 6-GPU designs (e.g., 6×24GB = 144GB VRAM) are attractive but awkward for some libraries that expect 4 or 8 GPUs; some frameworks (like tinygrad) claim to support uneven splits.
- There is interest in whether similar techniques can extend to 3090s and older GPUs; bandwidth, PCIe topology, and power cost are major constraints.
Legal, business, and future-lockdown concerns
- Some worry Nvidia could respond via driver or firmware updates, or eventually through on-die e-fuses, making such hacks impossible.
- Others argue visible hacks help regulators see what’s possible and may shape antitrust or “right to compute” debates.
Broader AI governance tangent
- Large subthread debates “compute governance”: restricting access to powerful hardware to mitigate hypothetical AGI existential risk.
- One side sees strict control (including extreme enforcement scenarios) as potentially justified; the other sees it as dystopian, unfalsifiable fear-mongering and a route to state/mega-corp control of AI.
- There is no consensus; the only agreement is that AI’s dual-use nature makes regulation contentious.