“GPU rental prices (H200) end of October?” That Kalshi market is a live read on NVIDIA demand. But prediction markets are free text, so it has no ticker on it. Ticker mapping is now live on Dune. Every Kalshi and Polymarket market on a public company is matched to its ticker. The H200 market now sits under NVDA, one of 1,800+ NVIDIA markets mapped in the past 90 days alone, ready to join to your book. Request a dataset trial:

GPU poor rejoice! You will be able to run Qwen3.8-Next-Flash on a single 24-32 GB GPU + 64GB ddr4/ddr5 + 100GB NVMe/SSD RTX 3090: - 2000 / 2800 tok/s prefill - 64 tok/s decode c1 - 210k fp8 cache Intel Arc B70: - 800 / 1200 prefill tok/s - 35 decode tok/s - 270k fp8 cache

GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs, while cutting queue starvation by 74%, with blocked jobs falling from roughly 10 per day to around 4. That’s the concrete result @SpreeAI saw after fixing their orchestration. When a unified diffusion model requires 80–100 GB of memory, you can’t simply throw workloads at a cluster and expect to use those GPUs efficiently. SPREEAI was dealing with workload fragmentation, ad-hoc submissions, and storage I/O blocking that left expensive GPUs idle. Working with Lambda’s ML engineering team, they implemented MLflow-based experiment orchestration with structured queuing and workload matching. They also connected Lambda’s Prometheus APIs to Grafana for real-time visibility into utilization gaps. The video testimonial covers how they diagnosed the bottlenecks and what the remediation looked like.

GPU prices are climbing again, and the latest TechSpot numbers show how bad things have become. Average GPU prices rose 15% in one month, with Nvidia cards up around 19% and AMD cards about 11%. Some models are now far above official prices: - RTX 5060: around $450, 51% above MSRP - RTX 5060 Ti 16GB: around $730, 70% above MSRP - RTX 5090: nearly $4,900, 145% above MSRP

GPU World: A story competition from Neal Stephenson, Gwern, and me. The AI future is unevenly distributed. What would the world look like with AI broadly diffused, such as if we had one GPU per person? 1000-5000 words, fiction or non-fiction. $100K in total prizes.

GPU fleet unit economics. TLDR: Tough to be bearish on $NBIS or $CRWV right now. Some maths, but it's important to understand. Using $NBIS 1-3yr contract economics: - $20-25M ACV per MW at a payback of 1 year 10 months. - Take $22.5M midpoint ACV/MW at a ~55% cash margin ( $NBIS core AI cloud runs ~50%, $CRWV 59%) and you get ~$12.4M/MW of annual cash flow. (Payback runs against cash flow, not revenue) - 1.83 years of that implies "all-in" capex of ~$20-23M per MW (GPUs included). - Which ties to Nebius' $20-25B capex guide for 2026 against ~1GW. - Over a 4-5 year life that's roughly $50-62M/MW of cumulative cash flow against ~$22M of capex. - NPV is +$20-25M per MW even discounting at 12-15% and IRR near 50%. Then years 1-2 of DCF nearly cover the entire capex on their own. Meaning that the GPU could go to $0 residual after year 2 and the deal basically breaks even. That is insane. So really, the bear case doesn't need slow decay to be wrong. Rather, it needs the contracted years themselves to fail. And those years are effectively walled off rn. - Around 70% of $NBIS deals carry prepayments covering 50-60% of the capex, on take/pay terms. - $CRWV's $104B backlog is take/pay with ~21% of it recognized more than four years out. As we now know, $CRWV signed an A100 contract running into 2029 (a SKU introduced in 2020) "at or above where it was years ago", and CEO told CNBC a batch of H100s coming off an expired contract was re-booked immediately at 95% of the original rate. $NVDA's CFO also said that A100s shipped 6 years ago are still running at full utilization. And $NVDA is now willing to underwrite residual value itself, up to 25% per project in the new financing platforms. I don't think they'd be willing to backstop if they expect GPUs to be worthless in around 3 years or so. For the neoclouds themselves though, depreciation is the biggest cost driver e.g. Nebius D&A was larger than their adj EBITDA last Q. If economic life actually extends to a new base case scenario of 5-6 years rather than the old 3-4 years... Earnings power re-rates higher across $CRWV, $NBIS and even the hyperscalers as ultimate beneficiaries.

GPU compute is becoming more than infrastructure—it is emerging as a financial asset. Join HashKey Capital Insight EP.4 with report author Harper Li and host Trader Joe for a deep dive into GPU pricing, derivatives, financing and RWA. Aug 20 | 5 PM HKT Register: