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VECTRA by Valley Semiconductor
We built an AI processor that beats Jetson Orin on Qwen 27B.
VECTRA is a programmable AI processor running Qwen 27B on our FPGA prototype, bit for bit against our 4-bit reference. It reached 7.9 tokens/sec in short-context decode, ahead of a Jetson AGX Orin in our tests. Built for robots and autonomous systems.
Hi Product Hunt, I'm Jack, founder of Valley Semiconductor.
Go to valleysemiconductor.com, ask a question, and watch a 27B language model answer from our own processor design running on an FPGA. No GPU involved.
7.9 tokens/sec on Qwen 27B in short-context decode, about 5.5 in live chat
4.2x faster in eight days, 533 ms to 126 ms per token
Faster than Jetson AGX Orin in our short-context tests, compared with 7.4 tokens/sec in MAXN mode and 5.9 tokens/sec in 50W mode
Every output matches our 4-bit reference bit for bit
Qwen 27B, Llama 3.1 8B and OLMoE on one FPGA image
For a 27B model, every token means moving about 13.5 GB of weights through memory. VECTRA is built around that one fact. The ASIC is designed for about 12 tokens/sec at 16 W, and Goodman Technologies has signed a letter of support to evaluate it on their autonomous ground vehicle.
It's one FPGA answering one question at a time, so you may wait a moment, or you can watch a recorded run. If you build robots, drones or autonomous vehicles, tell me what you'd run on it.
About VECTRA by Valley Semiconductor on Product Hunt
“We built an AI processor that beats Jetson Orin on Qwen 27B.”
VECTRA by Valley Semiconductor was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #136 on the daily leaderboard. VECTRA is a programmable AI processor running Qwen 27B on our FPGA prototype, bit for bit against our 4-bit reference. It reached 7.9 tokens/sec in short-context decode, ahead of a Jetson AGX Orin in our tests. Built for robots and autonomous systems.
On the analytics side, VECTRA by Valley Semiconductor competes within Robots, Hardware and Artificial Intelligence — topics that collectively have 505.6k followers on Product Hunt. The dashboard above tracks how VECTRA by Valley Semiconductor performed against the three products that launched closest to it on the same day.
Who hunted VECTRA by Valley Semiconductor?
VECTRA by Valley Semiconductor was hunted by Jack Al-Kahwati. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of VECTRA by Valley Semiconductor including community comment highlights and product details, visit the product overview.
Jack Al-Kahwati
Hi Product Hunt, I'm Jack, founder of Valley Semiconductor.
Go to valleysemiconductor.com, ask a question, and watch a 27B language model answer from our own processor design running on an FPGA. No GPU involved.
7.9 tokens/sec on Qwen 27B in short-context decode, about 5.5 in live chat
4.2x faster in eight days, 533 ms to 126 ms per token
Faster than Jetson AGX Orin in our short-context tests, compared with 7.4 tokens/sec in MAXN mode and 5.9 tokens/sec in 50W mode
Every output matches our 4-bit reference bit for bit
Qwen 27B, Llama 3.1 8B and OLMoE on one FPGA image
For a 27B model, every token means moving about 13.5 GB of weights through memory. VECTRA is built around that one fact. The ASIC is designed for about 12 tokens/sec at 16 W, and Goodman Technologies has signed a letter of support to evaluate it on their autonomous ground vehicle.
It's one FPGA answering one question at a time, so you may wait a moment, or you can watch a recorded run. If you build robots, drones or autonomous vehicles, tell me what you'd run on it.