Vllm

Llama 4 local deployment is practical if you match the model to the hardware: run Scout quantized for workstation experiments, use vLLM or SGLang on H100/H200 servers for API serving, and treat Maverick as a multi-GPU or heavily quantized model. Quick answer: what hardware can actually run Llama 4 locally? Llama 4 local deployment is the process of running Meta’s Llama 4 Scout or Llama 4 Maverick weights on hardware you control, from a 24 GB VRAM workstation to an 8xH100 server. Scout is the easier target because it has 17B active parameters, 16 experts, and 109B total parameters; Maverick also activates 17B parameters but has 128 experts and about 400B total parameters. In practice, a quantized Scout build can be useful on one high-end consumer GPU, while production Scout and most Maverick deployments belong on H100, H200, or dual 48 GB workstation hardware. The main mistake is assuming active parameters define memory use. Mixture-of-experts lowers compute per token, but disk, VRAM, and sharding still care about the full model. The takeaway: choose Scout for local iteration and Maverick only when your hardware budget is explicit. ...

If you want Llama 4 in production, start by matching hardware, concurrency, and context requirements before model size. In most teams, Scout is the first stable bet: faster startup, cheaper memory, and smoother local iteration, while Maverick becomes the right move when you need the bigger context and reasoning headroom under higher traffic. The path that works is not “which product is better,” it is “which constraint profile is cheaper to satisfy this quarter.” ...

GLM-5.1 is a 744B parameter MoE model with 40B active tokens, and it is best deployed for SWE-Bench Pro workloads when you match stack, quantization, and API behavior to your latency and tool-call requirements. This guide gives practical production defaults for vLLM, SGLang, and Ascend, with a DeepSeek-V3.1 baseline comparison and a live-check workflow you can apply in less than a day. What makes GLM-5.1 deployment hard in SWE-Bench Pro workflows? GLM-5.1 is designed for long-horizon coding work, and SWE-Bench Pro is exactly that: 1,865 tasks with enterprise-grade difficulty, split across public/held-out/commercial sets, so the first-turn success rate is only part of the story. In deployment terms, GLM-5.1 is not just a large model; it is an orchestration surface where token routing, tool-calling behavior, request queue depth, and prefill-recompute tradeoffs decide whether you can sustain coding sessions. On the Hugging Face leaderboards, GLM-5.1 reports around 58.4 on SWE-Bench Pro and is positioned above multiple high-end competitors, but a bad parser setting or poor precision choice can erase that advantage under real call patterns. The same 1,865-task pressure that drives benchmark score also magnifies edge cases like malformed JSON, stale routes, and silent retries. The key operational lesson is that tool-loop reliability beats single-shot token quality, because SWE-Bench chains typically fail on orchestration before they fail on first-pass reasoning. The takeaway: for SWE-Bench Pro, deployment engineering decides production quality more than raw model score. ...

대부분의 llama-stack vs Ollama vs vLLM 비교 글은 핵심을 놓칩니다. 이 세 가지 도구는 서로 경쟁하는 게 아닙니다. llama-stack은 오케스트레이션 API 레이어이고, Ollama와 vLLM은 추론 엔진입니다. 올바른 질문은 “무엇을 선택할까?“가 아니라 “어떻게 조합할까?“입니다. 2026년 권장 스택은 셋 모두를 사용합니다. What Is Each Tool? (Clearing Up the Confusion) llama-stack, Ollama, vLLM은 로컬 LLM 생태계에서 각각 다른 레이어를 담당하는 도구입니다. llama-stack은 Meta가 2026년 4월 8일에 릴리스한 OpenAI 호환 API 서버로, Ollama·vLLM·Fireworks 같은 여러 추론 제공자를 플러그인 방식으로 연결하는 오케스트레이션 레이어입니다. Ollama는 개발자 로컬 환경에 최적화된 추론 엔진으로, 한 줄 명령어(ollama run llama4)로 모델을 실행할 수 있습니다. vLLM은 PagedAttention 알고리즘을 기반으로 한 프로덕션 급 추론 엔진으로, GPU 서버 배포에 최적화되어 있습니다. ...

Devstral Small 2 is a 24B-parameter coding model from Mistral AI that scores 68% on SWE-bench Verified and runs on a single 24GB GPU or a Mac M-series with 32GB unified memory — making it the first cloud-grade coding agent most developers can realistically self-host. This guide covers three setup paths: Ollama for beginners, vLLM for production teams, and llama.cpp for CPU-only or low-VRAM machines. What Is Devstral Small 2? Devstral Small 2 is Mistral AI’s open-weight coding specialist, released December 10, 2025 under the Apache 2.0 license. With 24 billion parameters and a 256K-token context window, it achieves 68.0% on SWE-bench Verified — a real-world benchmark measuring a model’s ability to resolve open GitHub issues autonomously. That puts it on par with models up to five times its parameter count, including closed-source proprietary systems. Because it ships under Apache 2.0, you can run it locally with no API fees, no data leaving your machine, and no usage restrictions — even in commercial projects. The model is fine-tuned specifically on agentic coding workflows: reading multi-file codebases, writing patches, running tool calls, and self-correcting from test failures. Devstral Small 2 outperforms Qwen 3 Coder Flash (30B) despite being a smaller model, and its larger sibling Devstral 2 (123B) hits 72.2%, compared to Claude Sonnet 4.5’s 77.2% — at up to 7x lower cost per coding task. For teams or individuals who need a capable coding agent without cloud dependency, Devstral Small 2 is the most practical choice available today. ...

The right answer depends entirely on your scale: Ollama is the fastest path from zero to running a local LLM (2 minutes, zero config), LM Studio is the best option if you’re on integrated graphics or want a GUI, and vLLM is the only serious choice once you need to serve more than one user concurrently — it delivers up to 16x higher throughput than Ollama under load. Why Developers Are Moving from Cloud APIs to Local Inference Local LLM deployment is not a niche experiment anymore. The market is projected to grow 42% in 2026 as developers calculate the real cost of API calls at scale and start weighing data privacy risks. When you’re running a coding assistant for a team of 30 engineers, sending every keystroke completion to OpenAI adds up fast — both financially and contractually. The shift is also driven by model quality: open-weight models like Llama 3.3, Mistral, and Devstral have closed most of the capability gap with commercial frontier models for code-heavy workloads. In 2025–2026, Ollama adoption alone grew 300% by developer survey data (JetBrains AI Pulse), making it the default entry point for local inference. But adoption data also shows a clear pattern: 80% of developers start with Ollama for experimentation, then hit a scaling wall when they try to share the instance with their team. That’s the moment the “which stack” question becomes urgent. ...

Choosing between vLLM and Ollama for serving LLMs in production is not a matter of which tool is “better” — it is a matter of which tool solves the problem you actually have. vLLM serves 18.4 million Docker pulls and 2.79 million weekly PyPI downloads from teams running high-throughput inference APIs on GPU clusters. Ollama serves 126 million Docker pulls and 169,569 GitHub stars from developers running models locally on laptops and workstations. They overlap in capability but diverge sharply in architecture, performance characteristics, and production fitness. This guide compares them directly — with benchmarks, cost data, and a decision framework — so you can pick the right tool for your actual workload, not the one with more GitHub stars. ...