Streamlining LLM Inference for Lightweight Deployments

Transformer-Lite High-efficiency Deployment of Large Language Models on Mobile Phone GPUs The Large Language Model (LLM) is widely employed for tasks such as intelligent assistants, text summarization, translation, and multi-modality on mobile phones. However, the current methods for on-device LLM deployment maintain slow inference speed, which causes poor user experience. To facilitate high-efficiency LLM deployment on device GPUs, we propose four optimization techniques: (a) a symbolic expression-based approach to support dynamic shape model inference; (b) operator optimizations and execution priority setting to enhance inference speed and reduce phone lagging; (c) an FP4 quantization method termed M0E4 to reduce dequantization overhead; (d) a sub-tensor-based technique to eliminate the need for copying KV cache after LLM inference. Furthermore, we implement these methods in our mobile inference engine, Transformer-Lite, which is compatible with both Qualcomm and MTK processors. We evaluated Transformer-Lite's performance using LLMs with varied architectures and parameters ranging from 2B to 14B. Specifically, we achieved prefill and decoding speeds of 121 token/s and 14 token/s for ChatGLM2 6B, and 330 token/s and 30 token/s for smaller Gemma 2B, respectively. Compared with CPU-based FastLLM and GPU-based MLC-LLM, our engine attains over 10x speedup for the prefill speed and 2~3x speedup for the decoding speed.

If you’re an AI engineer trying to optimize your LLMs for inference, here’s a quick guide for you 👇 Efficient inference isn’t just about faster hardware, it’s a multi-layered design problem. From how you compress prompts to how your memory is managed across GPUs, everything impacts latency, throughput, and cost. Here’s a structured taxonomy of inference-time optimizations for LLMs: 1. Data-Level Optimization Reduce redundant tokens and unnecessary output computation. → Input Compression:  - Prompt Pruning, remove irrelevant history or system tokens  - Prompt Summarization, use model-generated summaries as input  - Soft Prompt Compression, encode static context using embeddings  - RAG, replace long prompts with retrieved documents plus compact queries → Output Organization:  - Pre-structure output to reduce decoding time and minimize sampling steps 2. Model-Level Optimization (a) Efficient Structure Design → Efficient FFN Design, use gated or sparsely-activated FFNs (e.g., SwiGLU) → Efficient Attention, FlashAttention, linear attention, or sliding window for long context → Transformer Alternates, e.g., Mamba, Reformer for memory-efficient decoding → Multi/Group-Query Attention, share keys/values across heads to reduce KV cache size → Low-Complexity Attention, replace full softmax with approximations (e.g., Linformer) (b) Model Compression → Quantization:  - Post-Training, no retraining needed  - Quantization-Aware Training, better accuracy, especially <8-bit → Sparsification:  - Weight Pruning, Sparse Attention → Structure Optimization:  - Neural Architecture Search, Structure Factorization → Knowledge Distillation:  - White-box, student learns internal states  - Black-box, student mimics output logits → Dynamic Inference, adaptive early exits or skipping blocks based on input complexity 3. System-Level Optimization (a) Inference Engine → Graph & Operator Optimization, use ONNX, TensorRT, BetterTransformer for op fusion → Speculative Decoding, use a smaller model to draft tokens, validate with full model → Memory Management, KV cache reuse, paging strategies (e.g., PagedAttention in vLLM) (b) Serving System → Batching, group requests with similar lengths for throughput gains → Scheduling, token-level preemption (e.g., TGI, vLLM schedulers) → Distributed Systems, use tensor, pipeline, or model parallelism to scale across GPUs My Two Cents 🫰 → Always benchmark end-to-end latency, not just token decode speed → For production, 8-bit or 4-bit quantized models with MQA and PagedAttention give the best price/performance → If using long context (>64k), consider sliding attention plus RAG, not full dense memory → Use speculative decoding and batching for chat applications with high concurrency → LLM inference is a systems problem. Optimizing it requires thinking holistically, from tokens to tensors to threads. Image inspo: A Survey on Efficient Inference for Large Language Models ---- Follow me (Aishwarya Srinivasan) for more AI insights!

What if your LLM could reuse work and respond 5-10× faster? That’s exactly what LMCache delivers. What is LMCache? It’s the open-source “KV cache layer” for LLMs — designed to store and reuse key/value caches across queries, sessions and even engines. Built for high-volume, long-context systems. Evaluations show up to 15× throughput improvements when paired with engines like vLLM. Why This Matters Right Now Latency kills UX. Every extra millisecond waits hit adoption. LMCache slashes response time by re-using caches. GPU cycles cost money. Re-computation means wasted resources. LMCache allows reuse across workloads, reducing GPU load. Context & multi-round workflows are exploding. RAG systems, agent pipelines, conversational contexts — LMCache fits them all. It’s production-ready and open-source. No black-box: you can inspect, integrate, extend. Typical Use Cases: -Agentic systems that make multi-turn decisions -RAG pipelines that reuse retrievalable contexts -Long-form applications (document processing + summarization) -Multi-engine inference clusters / cloud-scale deployments Plug into your engine and enable KV-cache reuse across queries & threads. If you’re building LLM-based systems for scale, this isn’t one more library — it’s a fundamental architecture upgrade. Mark this: The future of LLM inference isn’t just bigger models — it’s smarter reuse.

70% Smaller LLMs With Zero Accuracy Loss: Introducing DFloat11 Compression ... 👉 Why This Matters Large language models are hitting hardware limits: - Lossy quantization (8-bit/4-bit) reduces model size but alters outputs, risking accuracy drops in reasoning, coding, and niche tasks - Traditional lossless compression works for storage but fails during GPU inference due to serial decoding bottlenecks 👉 What Changed The DFloat11 framework achieves: - 30% size reduction for models like Llama-3, Qwen, and Gemma - Bit-for-bit identical outputs compared to original BFloat16 models - Efficient GPU inference via parallel decompression, avoiding CPU offloading delays The Core Insight: BFloat16’s exponent values are highly repetitive. By applying entropy coding (shorter codes for frequent patterns), DFloat11 compresses exponents while keeping signs/mantissas intact. 👉 Technical Breakthroughs 1️⃣ GPU-friendly decompression: - Splits large lookup tables into SRAM-sized chunks for fast access - Coordinates 1,000s of threads to decode variable-length codes in parallel 2️⃣ Transformer-block-level processing: - Batches weight decompression to maximize GPU utilization - Adds minimal latency (amortized over large batches) 👉 Real-World Impact - 1.9–38.8x faster than CPU-offloaded inference - Enables 5.3–13x longer context windows by freeing GPU memory - Runs 810GB models (e.g., Llama-3.1-405B) on 8x80GB GPUs – previously impossible Validation: - Identical accuracy on MMLU, TruthfulQA, and perplexity benchmarks - 100% weight reconstruction accuracy post-decompression 👉 Why It’s a Big Deal DFloat11 removes the “compromise mindset” in LLM deployment. Engineers no longer need to choose between model size, accuracy, and hardware costs – all three improve simultaneously.

No one really explains how llama.cpp works under the hood. For deploying LLMs on Edge or CPU, most guides stop at “use llama.cpp”, but they don't explain what’s happening under the hood. ✅ So I decided to fix that. I spent hours digging through the codebase, PRs, and community threads, and turned it all into a single, clear sequence diagram showing how it really works. My goal was to see what's happening, to understand each component, from loading up an LLM Checkpoint, up to generating the first token. Why is this important? 1️⃣ Frontier LLMs are built for high-compute environments. 2️⃣ But small language models (SLMs) are catching up, some even matching larger LLMs on key tasks. This means that with the appropriate toolkit, anyone could optimize and run them locally on their consumer Hardware, CPUs, or GPUs, and Edge devices. Having your own GPT-5 level LLM running on a CPU is impossible. But running Gemma 3, Llama 3.2, Phi-4, or Nemotron (3B–12B) is totally doable. In this deep dive, I cover: > GGML - the ML Tensor Library and how it parses LLM checkpoints. > GGUF - the format for storing quantized LLM models and Quantization types. > The high-level architecture of how everything fits together. > Source code overlays and sequence diagrams. Key points to know: 1/ llama.cpp is a pure C++ inference engine for LLMs, cross-platform (x64, ARM64, x86) 2/ GGML + GGUF + llama.cpp form a complete, deployable edge stack 3/ You can run modern LLMs with minimal dependencies and full control. 📌 Find the deep dive link in the first comment. It’s everything you need to understand the stack, not just use it. Enjoy!

[Dash//Stack] Goodbye 5-second awkward pauses, hello 1.5-second snappy replies!💥 The engineering challenge with LLM deployment isn't just about model quality anymore -- it's about making them fast enough for real applications. Meet Arctic Ulysses: A new inference engine that offers some genuine technical breakthroughs. 👉 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Even with vLLM and other optimizations, getting first token response times under 500ms has been nearly impossible with larger models (7B+) without extreme hardware requirements. 👉 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • Reimplemented attention mechanisms specifically for inference • Intelligent speculative decoding using smaller draft models • Hardware-aware memory optimization for KV cache 👉 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 75% latency reduction for first response token compared to vLLM with Mistral 7B (116ms vs 460ms) What makes Arctic Ulysses technically noteworthy isn't just raw performance, but how it fits into data workflows: 1️⃣ The architecture doesn't just optimize for academic benchmarks, but for metrics that actually impact user experience 2️⃣ It solves the governance challenge that's blocking many enterprise LLM deployments - maintaining the same security and access controls from your data layer through to your AI applications 3️⃣ The quantization and attention mechanism optimizations enable running 7B+ parameter models with interactive latencies without specialized hardware 𝘍𝘰𝘳 𝘢𝘯𝘺𝘰𝘯𝘦 𝘸𝘩𝘰'𝘴 𝘣𝘦𝘦𝘯 𝘧𝘳𝘶𝘴𝘵𝘳𝘢𝘵𝘦𝘥 𝘸𝘢𝘪𝘵𝘪𝘯𝘨 𝘧𝘰𝘳 𝘈𝘐 𝘵𝘰 𝘳𝘦𝘴𝘱𝘰𝘯𝘥, 𝘵𝘩𝘪𝘴 𝘮𝘢𝘬𝘦𝘴 𝘵𝘩𝘦 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘢 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯 𝘵𝘩𝘢𝘵 𝘧𝘭𝘰𝘸𝘴 𝘯𝘢𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘷𝘦𝘳𝘴𝘶𝘴 𝘰𝘯𝘦 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶'𝘳𝘦 𝘤𝘰𝘯𝘴𝘵𝘢𝘯𝘵𝘭𝘺 𝘸𝘢𝘪𝘵𝘪𝘯𝘨.  The engineering deep dive (link in comments👇) explains how its attention implementation achieved up to 3x performance gains over traditional approaches. 𝗜𝘁'𝘀 𝘄𝗼𝗿𝘁𝗵 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗲𝘃𝗲𝗻 𝗶𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗻𝗼𝘁 𝘂𝘀𝗶𝗻𝗴 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲! 👏 Huge shoutout & major kudos to the Snowflake's AI research team behind this: Samyam Rajbhandari Aurick Qiao Yuxiong He Mert Hidayetoğlu Jeff Rasley _________________________________ For those implementing LLMs in production systems, what's been your biggest inference performance bottleneck? Oh, and ... 𝙥𝙞𝙥 𝙞𝙣𝙨𝙩𝙖𝙡𝙡 𝙖𝙧𝙘𝙩𝙞𝙘-𝙞𝙣𝙛𝙚𝙧𝙚𝙣𝙘𝙚[𝙫𝙡𝙡𝙢] to get started! Let's learn together! Dash DesAI #AI #Latency #Inference #Optimizations #GenerativeAI #Snowflake

Most teams deploy LLMs with default settings and wonder why inference costs $50K/month. The optimization stack exists. Most engineers don't know the layers. Here's the full inference optimization hierarchy: LAYER 1: Serving architecture Before touching a single kernel, get your serving right. vLLM (74K ⭐): PagedAttention, continuous batching. https://lnkd.in/eeT_HM2B SGLang (25K ⭐): structured generation + RadixAttention. Faster for constrained outputs. https://lnkd.in/eKK7sxdf LAYER 2: Quantization Shrink the model without killing accuracy. llama.cpp (92K ⭐): GGUF quantization. Run 70B on consumer hardware. https://lnkd.in/eJrUg_qd Unsloth (50K ⭐): QLoRA fine-tuning at 70% less VRAM. https://lnkd.in/gJZtH4Y4 This layer alone can cut your GPU bill in half. LAYER 3: Attention + caching How much are you spending on redundant prefill? Flash Attention (21K ⭐): memory-efficient, IO-aware. Non-negotiable. https://lnkd.in/eYkuRuxC LMCache (1.5K ⭐): KV cache sharing. Eliminates it entirely. github.com/LMCache/LMCache LAYER 4: Hardware-specific acceleration Match your optimization to your silicon. TensorRT-LLM: purpose-built for NVIDIA GPUs. Kernel fusion, in-flight batching. https://lnkd.in/ekuFuDAP MLX: native framework for Apple Silicon. Inference without CUDA. github.com/ml-explore/mlx LAYER 5: Custom kernels Where the real differentiation lives. LeetCUDA (9K ⭐): 200+ CUDA kernels. Tensor Cores, HGEMM. https://lnkd.in/eUfgpwW6 llm.c (28K ⭐): Karpathy's raw C/CUDA. The fundamentals. github.com/karpathy/llm.c Engineers who write custom kernels command $200K+ at NVIDIA, Meta, and Google. LAYER 6: Distributed inference When one node isn't enough. NVIDIA Dynamo: multi-node orchestration. Disaggregated serving. https://lnkd.in/etBGNtjk exo (39K ⭐): distributed inference across consumer devices. github.com/exo-explore/exo 6 layers. Each one multiplies the savings from the layer above. Most teams stop at Layer 1. The ones running inference profitably reach Layer 5. Which layer is your team stuck at? 👇 💾 Bookmark this. Your next inference bill will thank you.

You're optimizing LLM inference. These are your options: ➢ KV cache management. Every token generation reuses the key-value pairs from previous tokens. If you're not caching them, you're recomputing everything. If your cache is growing unbounded, you're OOMing. Techniques like paged attention (vLLM) or sliding window help. ➢ Batching. One request at a time wastes GPU cycles. Continuous batching lets you pack multiple requests together. This is where you get real throughput gains — but it adds latency to individual requests. ➢ Quantization. Run in FP16 instead of FP32. Or INT8. Or INT4. You're trading precision for speed and memory. For most applications, the quality loss is negligible. These three levers interact. More batching means more memory pressure on KV cache. More quantization means you can fit bigger batches. Understand the tradeoffs. There's no free lunch.

📝 Announcing QuickSilver, a runtime-only, token-level framework that accelerates LLM inference by exploiting semantic redundancy through halting, memory skipping, token fusion, and precision adaptation -- without retraining or architectural changes. 🔹 "𝐐𝐮𝐢𝐜𝐤𝐒𝐢𝐥𝐯𝐞𝐫 — 𝐒𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐋𝐋𝐌 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐓𝐨𝐤𝐞𝐧 𝐇𝐚𝐥𝐭𝐢𝐧𝐠, 𝐊𝐕 𝐒𝐤𝐢𝐩𝐩𝐢𝐧𝐠, 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐓𝐨𝐤𝐞𝐧 𝐅𝐮𝐬𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐌𝐚𝐭𝐫𝐲𝐨𝐬𝐡𝐤𝐚 𝐐𝐮𝐚𝐧𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧" 🔹 In collaboration with Manipal University Jaipur, Vellore Institute of Technology, National Institute of Technology Silchar, Harrisburg University of Science and Technology, Meta, Indian Institute of Science Education & Research (IISER), Kolkata, Birla Institute of Technology and Science, Pilani Goa. 🔹 Paper: https://lnkd.in/gpZQKMmP ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐐𝐮𝐢𝐜𝐤𝐒𝐢𝐥𝐯𝐞𝐫’𝐬 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 🧠 𝑫𝒚𝒏𝒂𝒎𝒊𝒄 𝑻𝒐𝒌𝒆𝒏 𝑯𝒂𝒍𝒕𝒊𝒏𝒈 & 𝑲𝑽 𝑪𝒂𝒄𝒉𝒆 𝑺𝒌𝒊𝒑𝒑𝒊𝒏𝒈: Halts forward computation for converged tokens using L2 representational drift and suppresses attention KV cache updates, achieving fine-grained compute savings without architectural change. 🔗 𝑪𝒐𝒏𝒕𝒆𝒙𝒕𝒖𝒂𝒍 𝑻𝒐𝒌𝒆𝒏 𝑭𝒖𝒔𝒊𝒐𝒏: Merges semantically redundant tokens based on hidden state similarity, reducing sequence length dynamically while preserving syntax and semantics through proximity-constrained averaging. ⚙️ 𝑨𝒅𝒂𝒑𝒕𝒊𝒗𝒆 𝑴𝒂𝒕𝒓𝒚𝒐𝒔𝒉𝒌𝒂 𝑸𝒖𝒂𝒏𝒕𝒊𝒛𝒂𝒕𝒊𝒐𝒏: Allocates per-token bit-width (2/4/8-bit) based on entropy computed mid-network, scaling memory and compute to token uncertainty for efficient precision adaptation. ✍🏼 Authors: Danush Khanna, Aditya Kumar Guru, Srivarshinee S, Zidan Ahmed, Rubhav Bahirwani, Meetu Malhotra, Vinija Jain, Aman Chadha, Dr. Amitava Das, Kripabandhu Ghosh

🚀 𝗜𝗻𝘀𝗶𝗱𝗲 vLLM: 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗳𝗼𝗿 𝗟𝗟𝗠 𝘀𝗲𝗿𝘃𝗶𝗻𝗴 vLLM Is my favorite inference engine for self-hosting LLMs. It feels snappier because its design keeps GPUs busy and memory tidy. Here are the parts that matter when you’re shipping real apps. 🔩 𝗖𝗼𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 𝗶𝗱𝗲𝗮𝘀  • 𝗣𝗮𝗴𝗲𝗱𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 treats the KV cache like virtual memory: fixed-size pages that can be allocated, compacted, and reused—less copying/fragmentation and higher GPU utilization under bursty traffic.  • 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗯𝗮𝘁𝗰𝗵𝗶𝗻𝗴 admits new requests at token boundaries so GPUs don’t idle for the slowest prompt; throughput rises without hurting p50/p95 latency.  • 𝗣𝗿𝗲𝗳𝗶𝘅 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 shares overlapping headers (system prompts, RAG/tool preambles) to cut repeat compute and speed time-to-first-token.  • 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗸𝗲𝗿𝗻𝗲𝗹𝘀 & graphs reduce launch overhead; prefill/decode paths are tuned for chats and long contexts. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻  • Tensor & pipeline parallelism split weights/layers across GPUs so larger models fit and tokens stay in lockstep.  • Multi-node scheduling preserves batching/paging across machines—scale out without giving up efficiency.  • One-model-per-process keeps blast radius small; run many vLLM servers and route via a gateway. 🧰 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿-𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝘀𝗲𝗿𝘃𝗶𝗻𝗴  • 𝗢𝗽𝗲𝗻𝗔𝗜-𝘀𝘁𝘆𝗹𝗲 𝗲𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀 (chat/completions/embeddings) ease migrations.  • 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗯𝘂𝗳𝗳𝗲𝘁 (INT8/INT4, GPTQ/AWQ/AutoRound, FP8) trades tiny quality for big cost/latency wins.  • 𝗖𝗿𝗼𝘀𝘀-𝘃𝗲𝗻𝗱𝗼𝗿 𝗯𝗮𝗰𝗸𝗲𝗻𝗱𝘀 keep options open across accelerators and clouds.  • Streaming first with SSE for faster perceived latency. 💡 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀  • Lower $/token via better GPU saturation.  • Tighter tail latency keeps SLOs green.  • Operational simplicity—paging, caching, batching reduce custom CUDA and brittle schedulers. ⚙️ 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘁𝗶𝗽𝘀 • Keep prompts DRY so prefix caching hits often. • Use shorter max_tokens + streaming; request more if needed. • Right-size KV blocks and batch sizes to traffic shape. • Measure prefill vs decode throughput; long contexts are often prefill-bound. 🧪 𝗪𝗵𝗲𝗿𝗲 𝘃𝗟𝗟𝗠 𝘀𝗵𝗶𝗻𝗲𝘀 • Agent platforms with many short turns. • RAG APIs with shared system prompts. • Consumer chat with unpredictable spikes. • Enterprise multi-tenant backends needing strong isolation. 🔮 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 vLLM’s speed comes from the combo of paged KV memory, continuous batching, smart caching, and lean kernels—turning GPUs into well-fed token factories with speed, cost control, and predictability. Aleksa Gordić’s deep-dive blog is the clearest explanation of the vLLM engine I’ve seen 👉 https://lnkd.in/gRgiC_45  🔗 #vLLM #LLM #SelfHosting #AIInfrastructure #Inference #GPU #CUDA #SystemsDesign #AIAgents #Latency #Throughput #Quantization #KVCache #PagedAttention