#llm-inference
11 posts tagged with #llm-inference
Every article below is hand-written, technically reviewed, and focused on llm-inference. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning RTX 4060 Ti vs RTX 4070 for Local LLM Inference in 2026
I'd pick the RTX 4060 Ti if you're running sub-13B models solo on a tight budget, and the RTX 4070 if VRAM headroom and generation speed actually matter to your workflow. The $150 price gap is real, but so is the performance cliff you hit at 16GB models.
AI and Machine Learning Groq vs Together AI 2026: Which Inference API Is Actually Faster?
I'd pick Groq when raw token throughput is the make-or-break metric — it's still the fastest hosted inference I've tested at under $1/M tokens for Llama 3. I'd pick Together AI when model variety, fine-tuning, or multimodal pipelines matter more than milliseconds.
AI and Machine Learning LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16 [2026]
The practitioner's guide to choosing between Q4_K_M, Q5_K_S, Q8_0, and FP16 quantization for local LLMs — with real perplexity numbers, throughput benchmarks, and per-use-case recommendations.
![GGUF vs GPTQ vs EXL2: LLM Quantization Compared [2026]](https://img.kunalganglani.com/images/vzekdneq/production/34e429bea58e21d5119b5baad6b9efe44974be77-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning GGUF vs GPTQ vs EXL2: LLM Quantization Compared [2026]
A head-to-head comparison of GGUF, GPTQ, and EXL2 quantization formats with real quality, speed, and VRAM trade-offs — updated for the 2026 Hugging Face acquisition of ggml.ai.
AI and Machine Learning Apple M4 Max vs M5 Max for Local AI in 2026: Which Wins?
The M5 Max wins for serious local AI workloads in 2026, offering ~40% more neural engine throughput and a larger memory ceiling. The M4 Max remains the smart buy for budget-conscious developers who don't need cutting-edge inference speed.
AI and Machine Learning Mac Studio M4 Max vs RTX 4090 PC: Best Local AI Rig in 2026?
The Mac Studio M4 Max wins for plug-and-play local LLM work with massive unified memory; the RTX 4090 PC wins for raw CUDA throughput and flexibility. Your budget, workflow, and model size determine which is worth every dollar.
AI and Machine Learning Raspberry Pi 5 vs Jetson Orin Nano 2026: Which Edge AI Board Wins?
The Jetson Orin Nano wins for serious edge AI workloads with its dedicated GPU and CUDA ecosystem, while the Raspberry Pi 5 wins for cost-sensitive prototyping, general computing, and hobbyist projects. Neither is universally better — it depends entirely on whether you need inferencing horsepower or affordability.
Developer Tools vLLM vs Ollama 2026: Production Power or Developer Ease?
vLLM wins for high-throughput production deployments where every token/second counts; Ollama wins for local developer workflows where setup speed and portability matter most. Pick wrong and you'll either over-engineer a side project or under-power a real API.
AI and Machine Learning RTX 4090 vs RX 7900 XTX for Local LLMs in 2026: Which 24GB GPU Wins?
The RTX 4090 wins for serious local LLM inference thanks to superior CUDA ecosystem support and faster throughput; the RX 7900 XTX wins on price-per-GB for budget-conscious builders willing to navigate ROCm. Your choice hinges almost entirely on ecosystem tolerance and how much you value plug-and-play setup.
AI and Machine Learning Apple Silicon vs NVIDIA GPU for Local LLMs in 2026: Which Wins?
NVIDIA wins on raw throughput and ecosystem depth for serious multi-GPU workloads; Apple Silicon wins on memory bandwidth per dollar and zero-friction local inference for solo developers. Your budget and batch size decide the rest.
AI and Machine Learning Apple M4 vs M4 Max for Local LLMs in 2026: Which Should You Buy?
The M4 Max wins for serious local LLM work thanks to its unified memory ceiling and bandwidth advantage; the base M4 wins for portability and budget-conscious inference on smaller models. Here's exactly where the line falls.