CompactAI-O/cAI-Prism-K50
Viewer ⢠Updated ⢠100 ⢠54 ⢠3
poe
wop
AI & ML interests
AI research
AGI
Recent Activity
liked a dataset about 2 hours ago
CompactAI-O/cAI-Prism-K50 liked a Space about 8 hours ago
Pacific-i64/Dense-400M updated a collection about 9 hours ago
Cool stuffOrganizations
liked a
dataset about 2 hours ago
liked a
Space about 8 hours ago
updated a
collection about 9 hours ago
liked a
model about 9 hours ago
updated a
model about 9 hours ago
published a
model about 9 hours ago
liked a
Space 5 days ago
liked a
model 5 days ago
liked a
Space 6 days ago
updated a
Space 8 days ago
updated a
collection 8 days ago
upvoted a collection 8 days ago
liked a
Space 8 days ago
updated a
Space 8 days ago
published a
Space 8 days ago
reactedto reaperdoesntknow's post with đ 10 days ago
Post
3266
We present a methodology for training small language models on CPU at FP32 precision
that achieves capability-per-dollar efficiency orders of magnitude beyond GPU-based training.
Across15modelsspanningfournovelarchitecturefamiliesâMixtureofAttentions(MoA),cross-
architecture fusion (Qemma), swarm intelligence (SAGI), and metric-space causal language
models (DiscoverLM)âtotal compute cost was $24 on a single AMD EPYC 9454P proces-
sor. We introduce seven methodological pillars: (1) FP32 precision preservation, with exper-
iments demonstrating 5,810Ăsingle-operation error and 23,225Ăcompounding error ratio for
FP16 at network depth; (2) sparse cognitive architectures where 0.02â7% of parameters activate
per token, matching CPU branching rather than GPU SIMD; (3) developmental curriculum
training progressing from language to logic to transfer to depth; (4) continuous belt-fed data
ingestion eliminating truncation waste; (5) hardware-native optimization for AMD Zen 4 via
AOCL/OpenMP/NUMA-aware allocation; (6) self-regulating thermodynamic governance with
emergent temperature measurement grounded in L2-star discrepancy; and (7) open-standard
compute (AVX2 SIMD at FP32) free of proprietary vendor dependency. We argue that transformers were designed for GPU hardware rather than mathematical optimality, and that architecture designed for geometric correctnessâmetric-space attention, triangle inequality enforcement, sparse expert routingânaturally favor CPU execution. For sub-2B parameter models, CPU training produces more capable models at a fraction of the cost.
that achieves capability-per-dollar efficiency orders of magnitude beyond GPU-based training.
Across15modelsspanningfournovelarchitecturefamiliesâMixtureofAttentions(MoA),cross-
architecture fusion (Qemma), swarm intelligence (SAGI), and metric-space causal language
models (DiscoverLM)âtotal compute cost was $24 on a single AMD EPYC 9454P proces-
sor. We introduce seven methodological pillars: (1) FP32 precision preservation, with exper-
iments demonstrating 5,810Ăsingle-operation error and 23,225Ăcompounding error ratio for
FP16 at network depth; (2) sparse cognitive architectures where 0.02â7% of parameters activate
per token, matching CPU branching rather than GPU SIMD; (3) developmental curriculum
training progressing from language to logic to transfer to depth; (4) continuous belt-fed data
ingestion eliminating truncation waste; (5) hardware-native optimization for AMD Zen 4 via
AOCL/OpenMP/NUMA-aware allocation; (6) self-regulating thermodynamic governance with
emergent temperature measurement grounded in L2-star discrepancy; and (7) open-standard
compute (AVX2 SIMD at FP32) free of proprietary vendor dependency. We argue that transformers were designed for GPU hardware rather than mathematical optimality, and that architecture designed for geometric correctnessâmetric-space attention, triangle inequality enforcement, sparse expert routingânaturally favor CPU execution. For sub-2B parameter models, CPU training produces more capable models at a fraction of the cost.
- 6 replies
liked a
model 11 days ago
liked a
Space 12 days ago
liked 2
models 12 days ago
