Local AI Image Generation on a 64GB NVIDIA CMP 170HX
Sep 26, 2026 · 6 min read
In the last post I unlocked a pair of NVIDIA CMP 170HX cards to their full 64GB of memory. Language models were the first job. This post covers the second: generating images locally with one of the largest open image models available, at full precision, on a single card.
The full bf16 version of Qwen Image 2512 is about 40GB. On a typical 24GB consumer card you have two options: run a quantized (compressed) version of the model, or offload parts of it to system RAM and accept a large speed penalty. With 64GB of VRAM, neither is necessary. The whole model, the text encoder, and the VAE fit on the GPU at once.
The Setup
The Lightning LoRA is what makes this practical for everyday use. It lets the model produce a finished image in 4 steps instead of the usual 30 or more, and it is designed to run at CFG 1, which also removes the second pass that higher CFG values require.
That is roughly 55GiB resident on the card during generation, with about 9GiB to spare. Usage stayed around this level across all six images. Nothing spills into system memory.
The Results
Six prompts, each chosen to test something different: text rendering, fine detail, perspective, crowds, reflections, and difficult lighting. Every image below is straight out of the model with no editing or upscaling.
01Text Rendering
Prompt
A vintage-style enamel sign mounted on a brick wall in warm late-afternoon light, hand-painted lettering reading "RECLAIMED NODE - SECOND LIFE FOR SERIOUS HARDWARE" in cream and deep green, with a smaller line beneath reading "Est. 2026". Slight weathering and chipped paint at the edges, soft rim light from a low sun, shallow depth of field, photorealistic, shot on an 85mm lens.
02Macro Hardware Detail
Prompt
Extreme macro photograph of a data center GPU with a large gold-plated heatsink and dense rows of copper fins, lit by a single hard side light that rakes across the metal and reveals fine machining marks. Deep shadows, tiny dust particles catching the light, cool blue accent glow from an LED on the board, shallow depth of field, ultra-detailed, photorealistic.
03Data Center Aisle
Prompt
A vast, quiet data center aisle stretching into the distance, rows of black server racks with thousands of tiny green and blue status lights, cold blue light from the ceiling and warm amber light spilling from an open door at the far end, light haze in the air, polished concrete floor with soft reflections. Symmetrical one-point perspective, cinematic, photorealistic, high dynamic range.
04Neon Night Market
Prompt
A bustling night market in a rain-soaked neon city street, packed with hundreds of people under colorful umbrellas, food stalls with steam rising, hand-lettered signs in several languages, reflections of pink and teal neon in puddles, layers of depth from foreground to distant skyscrapers. Cinematic, richly detailed, photorealistic, shot on a 35mm lens.
05Mountain Lake Sunrise
Prompt
A mountain lake at sunrise with perfectly still water mirroring snow-capped peaks, warm orange light striking the highest summits while the valley below stays in cool blue shadow, thin mist hovering over the water, a lone wooden dock in the foreground. Crisp detail, high dynamic range, photorealistic, landscape photography.
06Lighthouse After a Storm
Prompt
A lone lighthouse keeper's cottage on a rocky coastline at golden hour, just after a storm. Low sun breaking through a gap in dark, heavy clouds, sending sharp god rays across the scene. Warm amber light rakes across wet stone and grass, creating long soft shadows and glowing rim light on the edges of the roof and rocks. Puddles and tidal pools on the rocks reflect the fiery orange and deep magenta sky. Cool blue shadows in the foreground contrast with the warm highlights. Atmospheric mist rising off the ocean, light scattering through it. Cinematic composition, high dynamic range, rich contrast, photorealistic, shot on a 50mm lens.
The 64GB of memory is what makes this setup work. The full-precision model runs entirely in VRAM, with no quantization and no offloading to system RAM, on a card that was sold as a mining-only product.
Follow-up: I ran the same six prompts and seeds on a Mac Studio with an M5 Ultra. See the Mac Studio vs. CMP 170HX head-to-head.
Next: I put four of these cards in one server for 256GB of VRAM. See the quad CMP 170HX build and first LLM benchmarks.