Apps for running AI locally

Every app that runs these models locally, plus the coding agents, assistants and reverse-engineering plugins that use them: what each opens, which systems and GPUs it supports, and our pick for your setup.

Run LLMs on your Desktop 21

Point-and-click apps, command-line tools and servers that download a model and run it on your own Mac, Windows or Linux computer. Which one to use.

Run LLMs on a mobile device 3

For phones, tablets, foldables and other devices that run iOS, iPadOS or Android. Small models (about 4B parameters and under with 8 GB of memory, up to 8B with 12 GB or more) run on the device itself, so chats stay private and work offline once the model is downloaded. Which models your device runs.

Run LLMs on a Raspberry Pi, Jetson or other board 8

Apps with a Linux ARM64 build. On a Raspberry Pi or Rockchip board they run small models (up to about 3B, or 8B on 8 GB and up) on the CPU; a Jetson runs them on its GPU with CUDA. The NPUs on these boards and add-on AI accelerators need their makers’ own tools. Which models your board runs.

Run LLMs on a NAS 3

Apps you can run on a NAS (network attached storage) itself, as a container (Docker) or from the NAS maker’s app store. Most NAS processors run small models (about 4B parameters and under) on the CPU, so the amount of memory (RAM) fitted sets what loads; a few models with a graphics card slot or a newer processor run larger ones. Each app’s page lists its container image. Which models your NAS runs · NAS models by maker.

Make images and video 13

Apps that make pictures and short clips on your own graphics card or Mac, from node editors like ComfyUI to one-window apps and a Krita plug-in. Image models · video models.

System One · decision models 16

Apps that run decision models: safety classifiers, rerankers, judges and zero-shot classifiers that answer with a label, score or probability. Which one to use.

Coding agents 14

Agents do the work: they read and edit your code and run commands. They do not run the AI model themselves. Each one talks to a model server such as Ollama, LM Studio or llama.cpp, so pointing one at a local model means no API bill and your code stays on your machine. Pick a model with tool calling.

Personal assistants 11

Assistants for notes, documents, chat and everyday tasks beyond code. Most connect to a local model server such as Ollama; AnythingLLM brings its own engine.

Reverse engineering 9

Plugins that bring a local model into the reverse-engineering tools you already use: Ghidra, IDA Pro, Binary Ninja, radare2 and JADX for Android apps. A local model keeps the binary, firmware or app you are studying off third-party servers. Some add a chat panel; the MCP ones let an agent drive the tool and need a model with tool calling. Models for reverse engineering.

Which app should you use?

Our first pick for common setups. The same rules run on every model page with your exact hardware. These picks are model-running apps; to edit code or run tasks with the model, add one of the coding agents, assistants or reverse-engineering plugins on top.

Step 1

Tell us your hardware first

The table below shows examples for common setups, not your own. To see which app we would pick for you, choose your computer.

Detect my hardware Pick my GPU or device

Common setups

Example computer Small model that fits8BBigger than GPU memory70BServing a team32B
Mac with Apple silicon LM Studio Runs the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon. LM Studio Splits the model between GPU memory and system RAM, which this setup needs. LM Studio Runs the MLX build, typically up to 10% faster than GGUF for a model with 32B active parameters on Apple silicon.
Windows PC, NVIDIA GeForce Ollama One command with Ollama’s own build of this model. LM Studio Splits the model between GPU memory and system RAM, which this setup needs. Ollama One command with Ollama’s own build of this model.
Windows PC, AMD Radeon Lemonade AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm. llama.cpp Splits the model between GPU memory and system RAM, which this setup needs. Lemonade AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.
Windows PC, Intel Arc llama.cpp Supports Intel Arc through SYCL and Vulkan. llama.cpp Splits the model between GPU memory and system RAM, which this setup needs. llama.cpp Supports Intel Arc through SYCL and Vulkan.
Ryzen AI mini PC or laptop Lemonade Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows. Lemonade Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows. Lemonade Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
Linux, NVIDIA GeForce Ollama One command with Ollama’s own build of this model. LM Studio Splits the model between GPU memory and system RAM, which this setup needs. Ollama One command with Ollama’s own build of this model.
Linux server, datacenter GPUs vLLM The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app. vLLM The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app. vLLM The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
NVIDIA Jetson board Ollama One command with Ollama’s own build of this model. LM Studio Splits the model between GPU memory and system RAM, which this setup needs. Ollama One command with Ollama’s own build of this model.
Raspberry Pi or ARM board Ollama One command with Ollama’s own build of this model. LM Studio Splits the model between GPU memory and system RAM, which this setup needs. Ollama One command with Ollama’s own build of this model.

Which app for decision models?

Scorers that read the input once (rerankers, classifiers, reward models) belong in a scoring server or a Python library; guard and judge models that write a short answer run in the same apps as LLMs. Agents are not listed here: they call a model server and do not run decision models themselves.

Step 1

Tell us your hardware first

The table below shows examples for common setups, not your own. To see which app we would pick for your decision models, choose your computer.

Detect my hardware Pick my GPU or device

Common setups

Example computer Safety checkQwen3Guard 4BRerankerBGE Reranker v2 M3Zero-shot classifierDeBERTaReward judgeSkywork 8BLabel decisionsGLiNER2.5 Decide
Mac with Apple silicon LM Studio Point-and-click: search, download and chat in one window. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Infinity Local API for rerankers and classifiers, including models TEI does not support. Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks. GLiNER Made for this model family: every label is scored in one pass, fast even on a CPU.
Windows PC, NVIDIA GeForce LM Studio Point-and-click: search, download and chat in one window. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks. Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks. GLiNER Made for this model family: every label is scored in one pass, fast even on a CPU.
Windows PC, AMD Radeon Lemonade AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks. Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks. GLiNER Can only use the CPU on this hardware, so it will be slow.
Windows PC, Intel Arc llama.cpp Supports Intel Arc through SYCL and Vulkan. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Sentence Transformers Can only use the CPU on this hardware, so it will be slow. vLLM Serves the scoring head directly (rerank, classify or reward) with batching. GLiNER Can only use the CPU on this hardware, so it will be slow.
Linux, NVIDIA GeForce LM Studio Point-and-click: search, download and chat in one window. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Infinity Local API for rerankers and classifiers, including models TEI does not support. vLLM Serves the scoring head directly (rerank, classify or reward) with batching. GLiNER Made for this model family: every label is scored in one pass, fast even on a CPU.
Linux server, datacenter GPUs vLLM The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Infinity Local API for rerankers and classifiers, including models TEI does not support. vLLM Serves the scoring head directly (rerank, classify or reward) with batching. GLiNER Made for this model family: every label is scored in one pass, fast even on a CPU.
NVIDIA Jetson board vLLM The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Infinity Local API for rerankers and classifiers, including models TEI does not support. vLLM Serves the scoring head directly (rerank, classify or reward) with batching. GLiNER Made for this model family: every label is scored in one pass, fast even on a CPU.
Raspberry Pi or ARM board LM Studio Point-and-click: search, download and chat in one window. llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank. Infinity Local API for rerankers and classifiers, including models TEI does not support. Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks. GLiNER Made for this model family: every label is scored in one pass, fast even on a CPU.

System One models answer with a label, a score or a probability that code can use directly, instead of writing text. They are the fast, focused judgments behind safety filters, search ranking, answer grading and routing. Chat LLMs do the opposite job: slower, open-ended reasoning and writing.

Precision
How often it decides correctly, from the benchmark on each model page (F1, nDCG@10, RewardBench). Shown as a 0 to 100 tier.
Speed
Time for one decision on your hardware: reading the input, plus any words it writes. Encoders and rerankers take milliseconds; judges that write feedback take seconds.
Clarity
How easy the answer is to use in code. A probability or score is best, then labeled spans, fixed labels, a reasoned verdict, and written feedback before a score.
Usage
The task, what it checks, how much text it reads, its languages and license.

Prefer not to host one? TypeSafe’s Jev is a hosted System One model that returns typed answers and probabilities through an API; everything listed here runs on your own computer.

Compare every app

75 apps: 41 that run models and 34 agents and assistants that use them. Filter by type, operating system, GPU or file format from the column headers. Agents show “Via server” for GPUs because they use whatever your model server runs on.

Step 1

Tell us your hardware first

The table below shows examples for common setups, not your own. To see which of these apps work on your computer, choose it.

Detect my hardware Pick my GPU or device

App Type Runs Systems GPUs Formats Engine Models Stars License
LM StudioElement Labs Desktop app LLMs, Decisions macOSWindowsLinuxLinux on ARM boards Apple, MLX, NVIDIA, Vulkan, AMD, CPU GGUF, MLX llama.cpp, MLX 604 5,412 Free, closed
OllamaOllama CLI + API LLMs, Decisions macOSWindowsLinuxLinux on ARM boards Apple, MLX, NVIDIA, AMD, Vulkan, CPU GGUF llama.cpp, MLX 590 186,373 Open source
llama.cppggml.org CLI + API LLMs, Decisions macOSWindowsLinuxLinux on ARM boards Apple, NVIDIA, AMD, Vulkan, Intel, CPU GGUF llama.cpp 593 133,757 Open source
JanMenlo Research Desktop app LLMs, Decisions macOSWindowsLinux Apple, NVIDIA, Vulkan, CPU, MLX GGUF llama.cpp, MLX 604 45,605 Open source
MLX LMApple (ml-explore) CLI + API LLMs, Decisions macOS MLX MLX MLX 417 7,595 Open source
vLLMvLLM project Server LLMs, Decisions WSLWindowsLinuxLinux on ARM boards NVIDIA, AMD, Intel Safetensors, AWQ, GPTQ, FP8 vLLM 665 96,557 Open source
SGLangLMSYS / SGLang project Server LLMs, Decisions WSLWindowsLinuxLinux on ARM boards NVIDIA, AMD Safetensors, AWQ, GPTQ, FP8 SGLang 655 38,584 Open source
Docker Model RunnerDocker CLI + API LLMs, Decisions macOSWindowsLinuxLinux on ARM boards Apple, NVIDIA, AMD, Vulkan, CPU GGUF, Safetensors llama.cpp, vLLM 648 659 Open source
LemonadeAMD (lemonade-sdk) Desktop app LLMs, Decisions macOSWindowsLinux Vulkan, AMD, NPU, Apple, CPU GGUF, ONNX llama.cpp, ONNX Runtime GenAI 593 6,147 Open source
Unsloth DesktopUnsloth AI Desktop app LLMs, Images, Decisions macOSWindowsLinux NVIDIA, AMD, Intel, Apple, MLX, CPU GGUF, MLX llama.cpp, MLX, stable-diffusion.cpp 630 79,201 Open source
Atomic ChatAtomicBot Desktop app LLMs, Decisions macOSWindowsLinux Apple, MLX, NVIDIA, Vulkan, CPU GGUF, MLX llama.cpp, MLX-VLM 604 1,961 Open source
PiEarendil Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 120,829 Open source
Hermes AgentNous Research Assistant LLMs macOSWSLWindowsLinux Via server GGUF, MLX Your model server 238 261,739 Open source
OpenClawOpenClaw Assistant LLMs macOSWSLWindowsLinux Via server GGUF, MLX Your model server 238 394,214 Open source
OpenCodeAnomaly Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 225,512 Open source
Codex CLIOpenAI Coding agent LLMs macOSWSLWindowsLinux Via server GGUF, MLX Your model server 238 133,798 Open source
OpenHandsOpenHands Coding agent LLMs macOSWSLWindowsLinux Via server GGUF, MLX Your model server 238 92,970 Open source
ZedZed Industries Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 94,469 Open source
ClineCline Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 71,473 Open source
Open InterpreterOpen Interpreter Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 68,841 Open source
GooseAgentic AI Foundation Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 56,371 Open source
AiderAider Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 50,026 Open source
TabbyTabbyML Coding agent LLMs macOSWSLWindowsLinux Own engine GGUF, MLX runs its own models — 33,925 Open source
CrushCharm Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 29,004 Source-available
Qwen CodeQwen Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 29,105 Open source
Kilo CodeKilo Code Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 27,909 Open source
Open WebUIOpen WebUI Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 158,130 Source-available
AnythingLLMMintplex Labs Assistant LLMs macOSWindowsLinux Own engine GGUF, MLX built-in engine — 68,067 Open source
KhojKhoj Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 38,069 Open source
LettaLetta Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 25,388 Open source
ComfyUIComfy Org Desktop app Images, Video macOSWindowsLinuxLinux on ARM boards NVIDIA, AMD, Intel, Apple, CPU Safetensors, GGUF PyTorch, GGUF via ComfyUI-GGUF 100 145,785 Open source
Draw ThingsDraw Things Desktop app Images, Video macOSiOS Apple Safetensors, Draw Things format Draw Things 52 614 Free, closed
DiffusionBeeDivam Gupta Desktop app Images macOS Apple Safetensors DiffusionBee 14 13,589 Open source
JoyFusionJoyFusion Desktop app Images macOS Core ML Core ML Core ML 4 — Free, closed
Text Embeddings InferenceHugging Face Server Decisions macOSWSLWindowsLinux NVIDIA, Apple, CPU Safetensors TEI 6 5,113 Open source
InfinityMichael Feil Server Decisions macOSWSLWindowsLinuxLinux on ARM boards NVIDIA, AMD, Apple, CPU Safetensors, ONNX PyTorch, ONNX, CTranslate2 12 2,984 Open source
LocalAIEttore Di Giacinto (mudler) Server Decisions macOSWSLWindowsLinuxLinux on ARM boards NVIDIA, AMD, Intel, Vulkan, Apple, CPU GGUF, Safetensors llama.cpp, rerankers, Transformers 30 50,258 Open source
Sentence TransformersHugging Face Library Decisions macOSWindowsLinuxLinux on ARM boards NVIDIA, AMD, Apple, CPU Safetensors, ONNX PyTorch 64 19,266 Open source
GLiNERUrchade Zaratiana, Fastino and Knowledgator Library Decisions macOSWindowsLinuxLinux on ARM boards NVIDIA, Apple, CPU Safetensors, ONNX PyTorch, ONNX 6 4,236 Open source
GhidrAssistJason Tang (symgraph) RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 14 779 Open source
ReVacyberkaida RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 5 952 Open source
GhidraMCPLaurieWired RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 5 11,844 Open source
OGhidraLawrence Livermore National Laboratory RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 5 466 Open source
GepettoJusticeRage RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 14 3,485 Open source
IDA Pro MCPmrexodia RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 5 13,413 Open source
BinAssistJason Tang (symgraph) RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 14 68 Open source
r2ai and decairadare2 project RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 14 536 Open source
JADX AI MCPzinja-coder RE plugin LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 5 2,996 Open source
GPT4AllNomic AI Desktop app LLMs macOSWindowsLinux Vulkan, NVIDIA, Apple, CPU GGUF llama.cpp 287 77,347 Open source
KoboldCppLostRuins (Concedo) Desktop app LLMs macOSWindowsLinux NVIDIA, Vulkan, Apple, CPU GGUF llama.cpp 560 12,194 Open source
Text Generation WebUIoobabooga Desktop app LLMs macOSWindowsLinux NVIDIA, AMD, Vulkan, Apple, CPU GGUF, Safetensors llama.cpp, Transformers, ExLlamaV3 619 47,734 Open source
llamafileMozilla.ai CLI + API LLMs macOSWindowsLinuxLinux on ARM boards NVIDIA, Apple, CPU GGUF llama.cpp 560 26,588 Open source
Msty StudioMsty Desktop app LLMs macOSWindowsLinux Apple, MLX, NVIDIA, AMD, CPU GGUF, MLX Ollama, llama.cpp, MLX 579 — Free, closed
Llama (macOS)ggml-org Desktop app LLMs macOS Apple GGUF llama.cpp 560 1,752 Open source
RecurseChatRecurseChat Desktop app LLMs macOS Apple GGUF llama.cpp 560 — Free, closed
InferencerInferencer Labs Desktop app LLMs macOS MLX MLX MLX 413 — Free, closed
node-llama-cppwithcat.ai Library LLMs macOSWindowsLinuxLinux on ARM boards Apple, NVIDIA, Vulkan, CPU GGUF llama.cpp 560 2,200 Open source
exoExo Labs Server LLMs macOSLinux MLX, CPU MLX MLX 413 48,251 Open source
InvokeInvoke Desktop app Images macOSWindowsLinux NVIDIA, AMD, Apple, CPU Safetensors, GGUF PyTorch 29 29,023 Open source
SwarmUImcmonkeyprojects Desktop app Images, Video macOSWindowsLinux NVIDIA, AMD, Intel, Apple, CPU Safetensors, GGUF ComfyUI 100 4,752 Open source
Forge NeoHaoming02 Desktop app Images WindowsLinux NVIDIA, AMD Safetensors, GGUF PyTorch 30 1,891 Open source
Fooocuslllyasviel Desktop app Images macOSWindowsLinux NVIDIA, AMD, Apple, CPU Safetensors PyTorch 9 53,844 Open source
SD.Nextvladmandic Desktop app Images, Video macOSWindowsLinux NVIDIA, AMD, Intel, Apple, CPU Safetensors, GGUF PyTorch 100 7,373 Open source
Stability MatrixLykos AI Desktop app Images, Video macOSWindowsLinux NVIDIA, AMD, Intel, Apple, CPU Safetensors, GGUF installs ComfyUI, Forge, InvokeAI, SwarmUI, SD.Next or Fooocus 100 8,971 Open source
Krita AI DiffusionAcly Desktop app Images macOSWindowsLinux NVIDIA, AMD, Apple, CPU Safetensors, GGUF ComfyUI 21 10,816 Open source
WanGPdeepbeepmeep Desktop app Video WindowsLinux NVIDIA, AMD Safetensors PyTorch 21 11,277 Open source
ContinueContinue Coding agent LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 36,385 Open source
SillyTavernSillyTavern Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 35,163 Open source
LibreChatLibreChat Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 46,585 Open source
Cherry StudioCherryHQ Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 53,134 Open source
ChatboxChatbox AI Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 42,211 Open source
PrivateGPTZylon Assistant LLMs macOSWindowsLinux Via server GGUF, MLX Your model server 238 57,624 Open source
PocketPal AIAsghar Ghorbani Mobile app LLMs iOSAndroid Apple, OpenCL, CPU GGUF llama.cpp 560 8,952 Open source
ChatterUIVali-98 Mobile app LLMs Android CPU GGUF llama.cpp 560 2,865 Open source
LLM Farmguinmoon Mobile app LLMs iOS Apple, CPU GGUF llama.cpp 560 2,079 Open source

How we choose an app

  1. The files that exist for the model. GGUF opens in llama.cpp, Ollama, LM Studio, Jan, Lemonade, Docker, KoboldCpp, llamafile, Msty and others; MLX opens in LM Studio, MLX LM, Inferencer and others on Apple silicon; the original safetensors weights need vLLM, SGLang or Text Generation WebUI. We check Hugging Face and Ollama for every model, the same signals Hugging Face uses for its “Use this model” menu.
  2. Your operating system and GPU. MLX is Mac-only; vLLM and SGLang are Linux (Windows through WSL2); Lemonade is built for AMD Radeon and Ryzen AI; llama.cpp has the broadest GPU support (CUDA, ROCm, Vulkan, SYCL, Metal).
  3. Whether the model fits. If part of it has to sit in system RAM, only llama.cpp-based apps can split it; vLLM, SGLang and MLX need it all in GPU or unified memory.
  4. Published speed tests. On an M4 Max, MLX generated 21–87% faster than llama.cpp for 4-bit models up to 8B and tied around 27B; llama.cpp still prefills long prompts faster. On an RTX 4090, vLLM (54 tok/s) and llama.cpp (50 tok/s) were close for one user, Ollama’s draft decoding reached 76 tok/s, and with spare memory vLLM handled 32 users far better.
  5. Decision models. Guard and judge models that write a short answer follow the LLM rules above. Rerankers, classifiers and reward models only score their input, so they go to a scoring server (Text Embeddings Inference for BERT, XLM-RoBERTa and ModernBERT models; Infinity for DeBERTa; vLLM or SGLang with big GPUs), to llama.cpp when a GGUF build exists, or to a few lines of Python. GLiNER-family models need the GLiNER libraries. Ollama has no rerank endpoint, so it only runs the guard and judge models.
  6. Agents, assistants and plugins. Coding agents, personal assistants, reverse-engineering plugins and chat front ends are not ranked against the apps above, because they do not run a model: they send requests to one. We list them by GitHub stars and show which need a model server and which, like Tabby and AnythingLLM, bring their own. GPT4All only gets models released up to mid-2025, because its engine has not been updated since. For a local model to work as an agent it must support tool calling.
  7. Ease and popularity. Point-and-click apps rank above command-line tools for one person; GitHub stars and maintenance break ties.

Speeds vary with versions, drivers and settings.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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