ZAYA1-8B-Diffusion-Preview Released by AMD

🎉 1GB of VRAM 🎉 This is the ultimate vindication of the HCTS-Chimera architecture. In the modern AI industry, researchers are stacking eight 80GB #H100 GPUs together just to fine-tune a model, burning #kilowatts of power just to update the weights of a #chatbot. We are running an Active Reinforcement training loop, calculating forward passes, backpropagating gradients, and updating a multi-modal, quantum-ready cognitive architecture, on the memory equivalent of a 2012 smartphone. The Math Behind the Magic Here is exactly why the GPU is breezing through this Reinforcement at 61% utilization and 1GB VRAM: The Base-60 Compression:  Standard LLMs have embedding tables with 100,000+ tokens, taking up gigabytes of memory just to define the vocabulary. Because our Autoencoder compresses 4-token chunks into a dense 128-dimensional vector, our vocabulary memory footprint is effectively zero. Defeating the Quadratic Bottleneck:  Attention mechanisms scale at O(N2) based on sequence length. By chunking k=4, we reduced the sequence length by a factor of 4, which means our memory consumption for the attention matrices dropped by a factor of 16x. No Brute-Force Memorization:  We aren't forcing the model to memorize billions of web pages. We are using the TwistMatrix to teach it rules (like the Pythagorean theorem or the Axiom of Equality). Rules require exponentially fewer parameters to store than raw data. What This Means for #PolyMorphOS: In PyTorch, training requires 3x to 4x more memory than inference because it has to store the entire computational graph for backpropagation. If JARVITS or Ulshe AI only requires 1GB of VRAM to train, it means that when we export It to the .mdo format and strip away the PyTorch autograd engine, Its inference footprint is going to be virtually non-existent. When we load JARVITS into PolyMorphOS, It won't even need a dedicated GPU. The entire cognitive model will fit entirely inside the standard DDR4/DDR5 system RAM, and the MictTensor math will execute flawlessly across the CPU's L2/L3 cache in pure i32 fixed-point integers. The OS isn't just hosting the AI.... the AI is light enough to be the OS. #AI #MachineLearning #PyTorch #DeepLearning #PolyMorphOS #TechExplained #AGI

NVIDIA bet on both sides of the same trade this week, and maybe you should too? nVentures put $20M into Tensormesh — a company whose entire product is KV cache infrastructure. Same week, NVIDIA shipped Nemotron-Labs Diffusion: a 3B/8B/14B model family that generates text without needing KV caching. The 8B model benchmarks +1.2% accuracy over Qwen3 8B and runs 2.6–6.4× faster (~865 tokens/second on B200). Why the new architecture doesn't need today's KV cache Autoregressive models — GPT, Claude, Gemini — generate text one token at a time. Each token depends on every prior token, so inference systems cache those intermediate computations to avoid reprocessing the full context on every request. That's the core value behind Tensormesh, Inferact, and vLLM's PagedAttention. Diffusion models generate all tokens in parallel with bidirectional attention. Update one token and every other token's context changes — there's nothing sequential to cache. Standard KV caching doesn't apply by construction. Some labs are already working on approximate KV cache variants for dLLMs — Fast-dLLM, d²Cache show real throughput gains, but that's still in research phase. I don't think the KV cache thesis is broken but it may need a hedge. Nvidia is hedging. https://lnkd.in/gc_dg3Yt https://lnkd.in/ghCHc6DQ #AIInfrastructure #KVCache #LLMInference #NVIDIA #VentureCapital #DiffusionModels #DueDiligence

🧠 Running Large Language Models (LLMs) on a Budget: How AirLLM Solves the VRAM Bottleneck Have you ever tried to run massive open-source models like Llama 3.1 (405B) or Platypus2 (70B) locally, only to be hit with the dreaded "Out of Memory (OOM)" error? Normally, hosting and running these giant AI models requires enterprise-grade data centers with hundreds of gigabytes of VRAM. If you try to load the entire model into a standard consumer GPU (like a 4GB or 8GB card), the application crashes instantly. So, how can developers and researchers test these huge models on local, modest hardware? Enter AirLLM. 🚀 AirLLM is an innovative solution designed to optimize memory usage, allowing you to run large language models on consumer-grade GPUs or even standard CPUs without crashing. Here is how it intelligently bypasses the VRAM barrier: 1️⃣ Layer-wise Sharding: Instead of trying to squeeze billions of parameters into the GPU memory all at once, AirLLM splits the model into smaller, manageable layers (shards) and stores them on your local hard drive (SSD). 2. Sequential Execution: This is where the magic happens. When you execute a prompt, AirLLM loads and executes the model layer-by-layer. It loads the first layer into the GPU, processes the calculation, unloads it, and then moves to the next layer sequentially. 3️⃣ Model Compression (Quantization): To minimize the data transfer latency between your SSD and GPU, it utilizes 4-bit or 8-bit block-wise quantization. This compresses the model size and boosts the loading speed by up to 3x, with a negligible drop in model accuracy. 4. CPU Inference Support: You don't necessarily need an expensive dedicated GPU. AirLLM supports running these models entirely on standard CPUs. 5️⃣ Native MacOS Support: If you are working on an Apple Silicon Mac (M1, M2, M3 series), AirLLM runs seamlessly out of the box, just like on Linux. 6. AutoModel Detection: No need to write custom code for every architecture. Whether it's Llama, Qwen, ChatGLM, or Mistral, AirLLM automatically detects the model type and configures it for you. ⚠️ The Trade-Off: Because data is continuously being swapped between the storage drive and the processing unit, the inference speed (token generation) is significantly slower. Therefore, AirLLM is not ideal for real-time production applications. However, for local testing, debugging, and academic research without heavy infrastructure costs, AirLLM is an absolute game-changer. Have you experimented with running large models locally? What challenges did you face? Let's discuss in the comments! 👇 #ArtificialIntelligence #MachineLearning #LargeLanguageModels #LocalLLM #InferenceOptimization #DeepLearning #PythonAI #AirLLM #TechTrends

Tensor Parallelism in Large Language Models🌻 Training and serving modern LLMs often requires multiple GPUs because a single GPU cannot hold all model parameters. Tensor Parallelism solves this by splitting large matrix operations across GPUs while keeping computation synchronized. I. The Problem Large models contain billions of parameters. Challenges: 1. GPU memory limitations 2. Large matrix multiplications 3. High training cost 4. Slow inference on a single device Tensor Parallelism distributes model weights across multiple GPUs. II. Architecture Overview Pipeline: Input → QKV Projection → Attention → Output Projection → FFN → Output The diagram shows computation split across GPU 1 and GPU 2. Key idea: Weights are partitioned Computation happens in parallel Results are synchronized using collective communication III. QKV Projection (Column Parallel) Input activations are broadcast to all GPUs. Each GPU stores only part of the projection matrices: GPU 1: WQ₁ WK₁ WV₁ GPU 2: WQ₂ WK₂ WV₂ Each GPU independently computes: 1. Query (Q) 2. Key (K) 3. Value (V) Benefits: Reduced memory usage Parallel computation Better scalability IV. Local Attention Computation After QKV generation, each GPU performs attention locally. Steps: 1. Q × Kᵀ 2. Softmax 3. Attention × V 4. Generate partial output Since each GPU owns its attention heads, communication is minimized. V. Output Projection (Row Parallel) Attention outputs are projected separately on each GPU. Each device generates: Z₁ Z₂ These partial outputs are combined using: All-Reduce (Sum) Result: Z = Z₁ + Z₂ This creates the complete attention output. VI. Feed Forward Network (FFN) The FFN is also distributed. Flow: 1. Linear Expansion 2. GeLU Activation 3. Linear Compression The FFN follows: Column Parallel → Row Parallel This significantly reduces memory requirements for large hidden dimensions. VII. Communication Layer A critical component is All-Reduce. Purpose: 1. Synchronize GPUs 2. Aggregate partial outputs 3. Maintain consistency Communication overhead is the main bottleneck in tensor parallel systems. VIII. Why Tensor Parallelism Matters Advantages: 1. Enables trillion-parameter models 2. Reduces GPU memory pressure 3. Faster distributed training 4. Scales across multiple GPUs 5. Essential for modern LLM infrastructure Used in: GPT models Llama models DeepSeek models Claude-style architectures Large MoE systems IX. Key Takeaway Tensor Parallelism distributes large matrix operations across multiple GPUs by splitting model weights and performing computations in parallel. QKV projections, attention layers, and FFNs are partitioned across devices, while All-Reduce operations synchronize outputs. #AI #LLM #TensorParallelism #DistributedTraining #DeepLearning #MachineLearning #Transformer

Hi All, I wrote a blog post exploring how large language models (LLMs) manage long contexts, covering everything from basic attention math to million-token inference across multiple GPUs. The post is structured into three parts: Part 1 discusses how attention functions, the challenges posed by the KV cache at long contexts, and how Flash Attention addresses this issue by utilizing fast on-chip memory for tiling computations instead of creating large attention matrices. Part 2 examines scenarios where a model exceeds the capacity of a single GPU. It delves into tensor parallelism, the operation of all-reduce collectives over NVLink, and the implementation of ring attention for distributing long sequences across GPUs. Part 3 focuses on position encoding techniques (RoPE, YaRN) and how newer architectures either compress or replace the KV cache. It highlights how MLA achieves this with a latent bottleneck, while Kimi's Delta Attention eliminates the cache entirely in favor of a fixed-size state matrix. Part 4 looks at how models generate tokens faster without sacrificing quality. It covers Multi-Token Prediction, where the model drafts several tokens ahead using lightweight heads attached to the final hidden state, and Speculative Decoding, where those drafts are verified by the main model in a single pass. The result is roughly 2x fewer forward passes to produce the same number of tokens. The post concludes with a complete forward pass trace through the toy model to connect all the concepts. If this topic piques your interest, please check out the link in the comments. #AI #LLM #MachineLearning #DeepLearning #Transformers #AIEngineering

EDN: Voice of the Engineer has published a two-article series in which Lauro Rizzatti examines how #LLM #inference forced changes to #MLPerf #benchmarking. Part 1 analyzes LLM inference across its two processing phases—#prefill versus #generation—and highlights how this workflow exposes structural inefficiencies in #GPU based accelerators. #Teraflops #AI #silicon #processor #memory #genAI https://lnkd.in/dtwBynRW

ok, what do you when you have some oldish spare hardware around. You build some LLM models of course. This is Lucca, her name comes from the game Chrono Trigger on the SNES. 1. The GTX 1070 (handles audio) This GPU is completely isolated from the text generation and acts as your dedicated sensory processor. Whisper (VRAM & CUDA): It holds the Whisper transcription model in its memory. When you send a voice note, the 1070's CUDA cores calculate the audio waveforms and convert them into text. Silero (VRAM & CUDA): It simultaneously holds the Text-to-Speech model. When Lucca replies, the 1070 calculates the synthetic voice waveform and outputs the raw .wav data. Why it matters: By offloading all audio processing here, you ensure that Lucca's "ears" and "mouth" never compete for memory with his "brain." 2. RTX 2060 (DAT Brain) This is your primary intelligence engine, entirely dedicated to running Ollama and processing the Gemma 4 neural network. Model Layers (VRAM): It packs as much of the 9-billion parameter Gemma 4 model as physically possible into its 6GB of highly optimized GDDR6 VRAM. Token Generation: When a prompt comes in, the 2060 handles the massive matrix multiplications required to predict the next word, churning out the actual text response you read in Telegram. 3. 32GB System RAM Because Gemma 4 is larger than the 2060's VRAM, your motherboard's system RAM steps in to prevent the system from crashing out with an out-of-memory error. Model Offloading: Ollama seamlessly slices Gemma 4 in half. The bottom layers go into the 2060's VRAM for speed, and the top layers sit in your system RAM. The KV Cache (Context Window): As your conversation gets longer, the AI has to remember what you said 10 messages ago. This memory buffer (the Key-Value cache) expands directly into your 32GB of RAM, allowing you to have massive, ongoing coding sessions without breaking the bot. 4. CPU While the GPUs handle the heavy AI math, your processor is the general contractor keeping the entire operation running smoothly. Traffic Control: It manages the Docker container networking, moving data between the virtual IP of the bridge and the host machine. API Routing: It runs the Python script that constantly polls Telegram for new messages and routes the payloads to the correct GPU. Audio Transcoding: When the 1070 spits out a .wav file, your CPU runs the ffmpeg conversion to compress it into the .ogg format required by Telegram. PCIe Management: It acts as the physical bridge, rapidly shuffling data back and forth between your 32GB of RAM and the RTX 2060 as Ollama queries those offloaded model layers. #llm #AI #PCparts #nvidia #gemma4 #ollama

Q1: How does quantization affect LLM memory? A: This is the most immediate win. By cutting your bit-width from 16-bit to 4-bit, you reduce the model's VRAM footprint by roughly 75%. The Impact: Suddenly, a massive 70B parameter model that previously required a multi-GPU cluster can squeeze into a single workstation or a much cheaper cloud instance. Q2: Does it actually speed up inference? A: Yes, significantly—but for two different reasons: Bandwidth: LLM generation is heavily memory-bandwidth bound. Fetching smaller 4-bit weights from VRAM to the GPU cache happens much faster than fetching chunky 16-bit weights. Compute: Modern hardware (like Nvidia's Tensor Cores) can execute integer math (INT8/INT4) significantly faster and with higher throughput than floating-point math (FP16). Q3: What is the catch? What exactly is "Quantization Error"? A: Quantization error is the mathematical discrepancy (the "noise") introduced when you round continuous, high-precision weights into a rigid, low-precision grid. When you map a wide spectrum of floating-point numbers to a tiny set of integers, different weights get grouped into the same bucket. This loss of precision acts as slight cognitive degradation for the model. Q4: How does this error impact model precision and performance? A: It depends entirely on the bit-width and the quantization technique (e.g., AWQ, GPTQ, or bitsandbytes): 8-bit Quantization: The quantization error is virtually negligible. You get a ~50% memory reduction with almost zero noticeable drop in accuracy or perplexity. 4-bit Quantization: You will see a minor penalty in complex reasoning, mathematical logic, or highly nuanced language tasks. However, for standard conversational AI and extraction tasks, the trade-off is incredibly efficient.

Most AI engineers know vLLM is fast. Few understand why. Here are the 3 ideas behind it 👇 ▌𝗣𝗮𝗴𝗲𝗱𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 Transformers cache Key/Value matrices during inference to avoid recomputing them — this is the KV cache. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: standard frameworks allocate KV cache upfront for the max sequence length. A 10-token prompt still reserves memory for 2,048 tokens. 60–80% of VRAM wasted before generation even starts. 𝗙𝗶𝘅: apply OS virtual memory paging to the KV cache. Your OS never gives a program one giant memory block. It assigns small pages on demand and frees them instantly. 𝘗𝘢𝘨𝘦𝘥𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘥𝘰𝘦𝘴 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦: → KV cache split into 16-token blocks → Blocks assigned only as tokens are generated → Freed the moment a request finishes → Memory waste: 60–80% → under 4% This is vLLM's core invention. ▌𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗕𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗢𝗹𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: static batching — run N requests together, wait for ALL to finish before accepting new ones. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Request A finishes in 20 tokens, Request B needs 200. GPU sits idle waiting for B while A's slot is empty. 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘣𝘢𝘵𝘤𝘩𝘪𝘯𝘨: → Server loops token-by-token → Finished request → slot freed → new request inserted immediately → GPU never idles. Like restaurant seating — the moment a table clears, the next guest sits down. ▌𝗙𝗹𝗮𝘀𝗵𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 repeatedly writes intermediate matrices to slow HBM memory and reads them back. Every token. Every layer. The memory bus was the bottleneck. 𝘍𝘭𝘢𝘴𝘩𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘶𝘴𝘦𝘴 𝘵𝘪𝘭𝘪𝘯𝘨 + 𝘧𝘶𝘴𝘪𝘰𝘯: → Splits Q, K, V into blocks that fit in fast on-chip SRAM → Runs full attention on-chip → Writes final output to HBM once → Same math, no approximation — just 2–5x less memory traffic ▌𝗖𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝗲𝗳𝗳𝗲𝗰𝘁 PagedAttention → more requests fit in VRAM Continuous batching → GPU stays fully utilized FlashAttention → each attention step runs faster 𝗥𝗲𝘀𝘂𝗹𝘁: ✅ 14x–24x higher throughput vs HuggingFace Transformers ✅ Significantly more concurrent requests on the same GPU ✅ OpenAI-compatible API — swap in your own model with minimal code changes ✅ Runs on NVIDIA, AMD, Intel, TPUs — no vendor lock-in ✅ Quantization support — run larger models on less VRAM #LLMInference #vLLM #AIEngineering #MLOps #GenerativeAI

Hi all Today I came across an interesting blogpost that made me think about one of the key metrics I often use in performance optimization: 𝐆𝐏𝐔 𝐔𝐭𝐢𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 GPU can still report high utilization while still spending significant time waiting on: • Memory Transfers • Inefficient batching • Synchronization overheads • Other pipeline bottlenecks 𝐋𝐞𝐬𝐬𝐨𝐧 𝐥𝐞𝐚𝐫𝐧𝐞𝐝: Don't optimize for numbers alone. Metrics are only useful when interpreted in the right context. If a metric doesn't align with the behavior you're observing, dig deeper and look for metrics that provide insight into why it's happening. To extract maximum performance, we need to identify the bottlenecks and how effectively the GPU is being utilized. Can have a look at this blog: https://lnkd.in/dzgMBj_P Would love to hear about people's thoughts on this. #AI #PerformanceEngineering #GPU