GB300
A high-performance computing hardware platform product introduced by NVIDIA, requiring high-precision automated testing and assembly processes.
Timeline (last 90 days)
According to a post on X, a single GB300 rack is enough to carry all the traffic of DSV4.1 Flash on OpenRouter.
GB300 is delivered as complete racks and targets large-scale training and high-throughput inference scenarios.
SGLang admitted that its earlier comparison placing GB300 measured results side by side with DeepInfer's B300 data was unfair, and after retesting said performance had matched the DeepInfer benchmark, with decoding throughput improving 7.4% on a 4-GB300 configuration.
The LoRA+GRPO recipe summarized in the Latent Space AI news digest: trained with verl, using vLLM for rollout, running 150 steps on 16 GB300s, raising AIME-2024 mean@8 from 0.56 to 0.85.
vLLM provides day-0 support for NVIDIA Vera Rubin NVL72, reporting 7.8x throughput per GPU versus GB200 NVL72 (AgentX) and 3.7x VLM throughput versus GB300 (MLPerf v6.1), with locality domains tuned for MoE routing.
Beam's reinforcement learning phase used 1,500 GB300 chips running continuously for four weeks, generating over 100 million rollouts, with a maximum context length of 256K.
The automated assembly of GB300 test trays belongs to a high-precision, low-volume scenario where the batch size is insufficient to amortize the cost of fully customized automation, and the precision requirements exceed the capability boundaries of traditional teach programming.
NVIDIA published an engineering practice record titled "The Machines that Make the Machines," describing how robots are taught to assemble GB300 test trays.
The GB300 is an NVIDIA hardware product for high-performance computing.
Jalapeño has a rated power consumption of 700W with measured sustained power consumption not exceeding 550W; by comparison, the GB200 is rated at 1200W and the GB300 at 1400W.
4 more timeline events
All Facts (12)
In the DeepSeek R1 670B (MXFP4 precision) test, Jalapeño achieved approximately 1.7× throughput per watt (19,641 vs. GB300's 11,781), and end-to-end latency was reduced from 5.99 seconds to 1.65 seconds.
60%UnverifiedAccording to a post on X, a single GB300 rack is enough to carry all the traffic of DSV4.1 Flash on OpenRouter.
50%UnverifiedGB300 is delivered as complete racks and targets large-scale training and high-throughput inference scenarios.
50%UnverifiedSGLang admitted that its earlier comparison placing GB300 measured results side by side with DeepInfer's B300 data was unfair, and after retesting said performance had matched the DeepInfer benchmark, with decoding throughput improving 7.4% on a 4-GB300 configuration.
50%UnverifiedThe LoRA+GRPO recipe summarized in the Latent Space AI news digest: trained with verl, using vLLM for rollout, running 150 steps on 16 GB300s, raising AIME-2024 mean@8 from 0.56 to 0.85.
50%UnverifiedBeam's reinforcement learning phase used 1,500 GB300 chips running continuously for four weeks, generating over 100 million rollouts, with a maximum context length of 256K.
50%UnverifiedThe automated assembly of GB300 test trays belongs to a high-precision, low-volume scenario where the batch size is insufficient to amortize the cost of fully customized automation, and the precision requirements exceed the capability boundaries of traditional teach programming.
50%UnverifiedNVIDIA published an engineering practice record titled "The Machines that Make the Machines," describing how robots are taught to assemble GB300 test trays.
50%UnverifiedThe GB300 is an NVIDIA hardware product for high-performance computing.
50%UnverifiedJalapeño has a rated power consumption of 700W with measured sustained power consumption not exceeding 550W; by comparison, the GB200 is rated at 1200W and the GB300 at 1400W.
50%UnverifiedOpenAI's dual-rack system has a total power consumption of approximately 160 kilowatts, roughly equivalent to the power level of a dual-chassis GB300 rack
50%UnverifiedThe GB300 is NVIDIA's data center GPU based on the Blackwell architecture.
50%