---
author: "Umesh Malik"
canonical: "https://umesh-malik.com/blog/tag/llm-inference"
description: "Explore articles tagged with LLM Inference by Umesh Malik — AI Engineer, LLM & GenAI Developer. Learn LLM Inference best practices, practical tips, and in-depth guides."
title: "Umesh Malik's Blog - LLM Inference Articles | LLM Inference Tutorials"
tokens: 563
generator: "scripts/generate-page-markdown.mjs"
---

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# LLM Inference

4 articles

 [![Qwen3.8 27B VRAM budget: FP8 weights plus KV cache at 262K context on a single GPU](https://umesh-malik.com/blog/qwen3-8-27b-vram-kv-cache-math-cover.png)

LLM Engineering • Aug 15, 2026

### Qwen3.8 27B VRAM: how to fit 262K context in 16 GiB, not 64

Qwen3.8 27B VRAM math: 25.9 GiB of FP8 weights plus 16 GiB of KV cache at 262,144 tokens, not 64. The arithmetic, and where a 48 GB card breaks.

9 min read

Read more →](https://umesh-malik.com/blog/qwen3-8-27b-vram-kv-cache-math)

 [![How a paravirtualized Metal device reporting Apple GPU family 5 makes llama.cpp disable its simdgroup matrix kernels, and the throughput that returns when the guest reports family 9](https://umesh-malik.com/blog/fix-slow-llm-inference-macos-vms-cover.png)

LLM Engineering • Aug 11, 2026

### Fix slow LLM inference in macOS VMs: 12.6 → 207 tok/s

LLM inference in macOS VMs collapses to 12.63 tok/s because the guest reports GPU family 5 and llama.cpp drops its matrix kernels. The check, and its limits.

9 min read

Read more →](https://umesh-malik.com/blog/fix-slow-llm-inference-macos-vms)

 [![Cover showing the vLLM VRAM budget split into model weights, runtime overhead and KV cache, with the KV cache block math that converts free VRAM into concurrent sequences](https://umesh-malik.com/blog/vllm-throughput-tuning-flags-cover.png)

LLM Engineering • Aug 8, 2026

### vLLM throughput tuning: configure these four flags, not a bigger GPU

vLLM throughput tuning starts with KV cache blocks, not a bigger GPU. The four flags that decide your tokens/sec, and the one that quietly backfires.

10 min read

Read more →](https://umesh-malik.com/blog/vllm-throughput-tuning-flags)

 [![Layer-by-layer LLM inference streaming one transformer layer at a time from disk onto a 4GB GPU](https://umesh-malik.com/blog/run-70b-llm-on-4gb-gpu-airllm-cover.png)

LLM Engineering • Aug 4, 2026

### Run 70B LLM on 4GB GPU: AirLLM's Real Tradeoff

Run 70B LLM on 4GB GPU hardware with AirLLM's layer-by-layer inference. The VRAM math is real — you just pay for it in disk bandwidth. The honest tradeoff.

11 min read

Read more →](https://umesh-malik.com/blog/run-70b-llm-on-4gb-gpu-airllm)