2026 Comparison

vMLX vs LM Studio

The complete comparison for local AI on Mac

Summary Verdict

vMLX is 224x faster at 100K context, has 20+ built-in agentic tools, and the only 5-layer caching stack for local AI on Mac. LM Studio has broader platform support (Windows, Linux) and a larger community. Both are free to use.

Feature Comparison

Feature vMLX LM Studio
Cold PP/s at 100K 154,121 tok/s 686 tok/s
Warm TTFT at 2.5K 0.05s9.7x faster 0.49s
KV Cache Paged multi-context Single-slot (evicts on switch)
KV Cache Quantization q4 / q8 None
Prefix Caching Yes No
Persistent Disk Cache Yes No
Continuous Batching Up to 256 sequences Limited
Agentic Tools (MCP) 20+ built-in None
API Endpoints 7 1
Vision Models + Full Cache Yes No
Mamba / SSM Support Yes (BatchMambaCache) No
Speculative Decoding Yes No
Platform macOS (Apple Silicon) macOS, Windows, Linux
Price Free Free Pro: $7.99/mo
Framework MLX (Apple-native) llama.cpp

Speed Comparison

Benchmarked on Apple M3 Ultra with Llama 3.2 3B (4-bit). Time-to-first-token (TTFT) measures how long you wait after pressing Enter before the model starts responding. Lower is better.

Cold TTFT (first request, no cache)

Context Length vMLX LM Studio Speedup
2.5K tokens 0.05s 0.49s 9.7x
10K tokens 0.13s 3.3s 25x
50K tokens 0.41s 42s 102x
100K tokens 0.65s 131s 201x

Prompt Processing Speed (tokens/sec)

Context Length vMLX LM Studio Speedup
2.5K tokens 50,040 4,928 10x
10K tokens 76,923 3,012 26x
50K tokens 121,951 1,179 103x
100K tokens 154,121 686 224x

Benchmark: Llama 3.2 3B Q4, M3 Ultra, macOS Tahoe. Cold TTFT = app restart, no cached state. vMLX uses paged KV cache with q8 quantization. LM Studio uses default llama.cpp settings.

Why vMLX Is Faster

The speed difference comes down to architecture. vMLX uses a purpose-built 5-layer caching stack on Apple's MLX framework. LM Studio uses llama.cpp with a single-slot KV cache that evicts state whenever you switch conversations.

vMLX: 5-Layer Caching Stack

1
Prefix Caching
Reuses previously computed KV states for shared prompt prefixes. System prompts and repeated conversation history are processed once, not re-computed every turn.
vMLX only
2
Paged Multi-Context KV Cache
Multiple conversations stay cached in memory simultaneously. Switch between chats without evicting cached state — no re-processing when you return to a previous conversation.
vMLX only
3
KV Cache Quantization (q4/q8)
Compresses cached KV states at the storage boundary. q8 saves ~2x memory, q4 saves ~4x — enabling longer contexts and more cached conversations in the same RAM.
vMLX only
4
Continuous Batching (256 sequences)
Processes up to 256 concurrent inference requests in a single batch. API consumers get low-latency responses even under load.
vMLX only
5
Persistent Disk Cache
Saves computed prompt caches to disk. Restart the app or reboot your Mac — cached state loads instantly without re-processing.
vMLX only

LM Studio: Single-Slot KV Cache

LM Studio's llama.cpp backend maintains a single KV cache slot. When you switch conversations, the cached state is evicted and the entire prompt must be re-processed from scratch. There is no prefix caching, no cache quantization, no persistent disk cache, and no multi-context paging. At long contexts (50K–100K tokens), this means waiting 40–130+ seconds for the model to start responding.

Agentic Tools

vMLX is the only local AI app with built-in agentic coding tools through native MCP (Model Context Protocol) integration. Models can autonomously read, write, and edit files, execute shell commands, search the web, and run multi-step workflows — all locally. LM Studio has no built-in tool support.

File I/O
read, write, edit, copy, move, delete, list directories
Code Search
grep (regex search), glob (pattern matching)
Shell
Execute arbitrary shell commands with configurable working directory
Web Search
DuckDuckGo, Brave Search integration
URL Fetch
Fetch and parse web page content
Git
status, diff, log, show — built-in version control
Utilities
Clipboard read/write, current date/time, timezone

Configure tool iterations, tool-choice modes, and working directories for complex multi-step agentic workflows. All tools run locally with zero cloud dependency.

When to Choose LM Studio

LM Studio is a solid app and the right choice in certain scenarios. Here is where it has an edge:

LM Studio Advantages

  • Cross-platform support — LM Studio runs on macOS, Windows, and Linux. vMLX is macOS-only (Apple Silicon).
  • Larger community — LM Studio has been around longer and has a larger user base, more tutorials, and broader community support.
  • llama.cpp ecosystem — LM Studio benefits from the extensive llama.cpp ecosystem with GGUF model compatibility across many platforms.
  • More established — LM Studio is a mature product with a longer track record and wider name recognition.

If you are on a Mac with Apple Silicon and want the fastest local AI experience with agentic capabilities, vMLX is the clear choice. If you need Windows or Linux support, or prefer a larger community, LM Studio is a good option.

Try vMLX — It's Free

224x faster. 20+ agentic tools. 5-layer caching. Zero cloud dependency.

Download vMLX

Free · macOS 26+ · Apple Silicon (M1 or later) · Code-signed & notarized