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Found 6 articles in AI Efficiency
Distill LLM for Efficiency: Cut Costs & Boost Performance
01AI Efficiency
Aug 31, 202610 min read

Distill LLM for Efficiency: Cut Costs & Boost Performance

Your AI inference bill is likely escalating, but buying more GPUs isn't the only answer. Discover how LLM model distillation allows you to create highly efficient, task-specific models that perform comparably to their larger counterparts at a fraction of the cost and latency.

KE
Krapton Engineering
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Master LLM Inference Optimization Techniques: Cut Costs & Latency
02AI Efficiency
Aug 23, 202611 min read

Master LLM Inference Optimization Techniques: Cut Costs & Latency

Facing soaring AI bills and slow model responses? Discover advanced LLM inference optimization techniques like speculative decoding, continuous batching, and paged attention to drastically reduce costs and latency without buying more hardware. Learn how to get more from your existing AI infrastructure.

KE
Krapton Engineering
Read →
Optimize LLM Inference Throughput: Cut Costs & Boost AI Responsiveness
03AI Efficiency
Aug 18, 202610 min read

Optimize LLM Inference Throughput: Cut Costs & Boost AI Responsiveness

High LLM inference costs and slow response times are major hurdles for production AI. Discover how continuous batching and PagedAttention can dramatically optimize LLM inference throughput, reduce GPU idle time, and deliver faster, more cost-effective AI applications without buying new hardware. Learn practical strategies from Krapton's engineering team.

KE
Krapton Engineering
Read →
Optimize LLM Inference with Quantization: Cut Costs, Boost Speed
04AI Efficiency
Aug 16, 202611 min read

Optimize LLM Inference with Quantization: Cut Costs, Boost Speed

High inference costs and latency are major roadblocks for AI adoption. Discover how LLM quantization techniques like GPTQ, AWQ, and GGUF can drastically reduce your operational expenses and improve model response times without sacrificing critical accuracy. Learn when and how to implement these powerful optimizations.

KE
Krapton Engineering
Read →
Unlock Cheaper LLM Fine-Tuning with Parameter-Efficient Methods
05AI Efficiency
Aug 14, 202612 min read

Unlock Cheaper LLM Fine-Tuning with Parameter-Efficient Methods

Struggling with high VRAM requirements and slow training times for large language models? Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA and QLoRA offer a powerful solution to adapt LLMs to specific tasks with significantly less computational overhead and hardware, making advanced AI accessible.

KE
Krapton Engineering
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Run LLMs on Less VRAM: Boost Performance, Cut Costs
06AI Efficiency
Aug 12, 202610 min read

Run LLMs on Less VRAM: Boost Performance, Cut Costs

Struggling with high VRAM requirements for your LLMs? This guide dives into practical, battle-tested techniques like quantization (4-bit, 8-bit) and Parameter-Efficient Fine-Tuning (QLoRA) to significantly reduce memory footprint and inference costs, enabling powerful AI deployments on constrained hardware.

KE
Krapton Engineering
Read →
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