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LM Studio vs MTPLX

LM Studio does support MTP now: version 0.4.14 made MTP speculative decoding a stable feature for models that ship with multi-token prediction heads. So the choice is less about whether you get MTP and more about scope. LM Studio is a general desktop app for Mac, Windows and Linux with a big model browser; MTPLX is a Mac-only runtime built entirely around MTP on a short list of models, with its own speed and correctness claims.

Does LM Studio support MTP?

Yes. LM Studio's 0.4.14 changelog calls it a "Stable release of MTP Speculative Decoding" and says it works with models that include built-in MTP heads, so you need to download an MTP-capable model for it to do anything. Before that, LM Studio's speculative decoding meant loading a second, smaller draft model from the same family, which costs memory. With MTP heads, the draft lives inside the main checkpoint, which is the same idea MTPLX is built on.

Why you might still want MTPLX on a Mac

MTPLX (Apache-2.0, Apple Silicon M1 or newer, macOS 14+) does nothing but this, and it publishes its own tuned packs for Qwen 3.8, Qwen 3.6, Ternary Bonsai 2, Gemma 4 and MiMo. The vendor reports Qwen 3.6 27B at 81.74 tokens per second on an M5 Max, 2.69 times plain decode, and says every release checks the fast path against the plain path token by token. Neither tool's MTP speed has been measured by AIDiveForge, so treat those as MTPLX's claims.

Its server speaks both the OpenAI and Anthropic APIs (/v1/messages), which helps if you point Claude Code at a local model. LM Studio's local API and Python and TypeScript SDKs are the more established route for other apps. MTPLX is also one developer's project; LM Studio is a company product (Element Labs).

Price and license

Running local models costs nothing in either. LM Studio's app is closed source and free under its own terms, with optional cloud plans: Bionic+ at $20 a month and Pro at $100 a month for US-hosted open models. MTPLX is open source with no paid tier, installed from a DMG, Homebrew or pip.

LM Studio: Pick LM Studio if you want one app to browse, chat with and serve many models on Mac, Windows or Linux, with MTP turned on where a model supports it.

MTPLX: Pick MTPLX if you're on an Apple Silicon Mac, run its supported Qwen or Gemma packs for coding agents, and want an open-source runtime tuned only for MTP speed.

Facts checked Oct 18, 2026 against 7 sources

Which one should you try?

LM Studio

Free

Best for People who want a desktop app for downloading and chatting with local models

Visit LM Studio See full review

MTPLX

Free

Best for MacBook Pro / Mac Mini users running 27B+ models locally

Visit MTPLX See full review

Field notes

All field notes →

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

LM Studio

LM Studio

LM Studio is a free desktop app for local models with a headless server (llmster), local APIs and SDKs. We ran llmster on a CPU-only server and measured install, load time and offline behavior.

MTPLX

MTPLX

The vendor states a 2.24× decode speedup on Qwen3-27B running on an M5 Max MacBook Pro, achieved by using the model's own built-in MTP heads as the drafter — no second model loaded, no external checkpoint to maintain. Acceptance is handled via Leviathan–Chen rejection sampling with a residual (p − q)+ correction, verified bit-exact against single-token autoregressive output. It serves an OpenAI- and Anthropic-compatible API, so downstream tooling like Claude Code, Cline, or the openai-python SDK connects without shims. The wall appears immediately if you leave Apple Silicon: the runtime is explicitly Apple Silicon only, and the custom Metal kernels have no CUDA path.

AttributeLM StudioMTPLX
PricingFreeFree
PriceFree to run models locally; optional cloud plans: Bionic+ $20/mo, Pro $100/mo—
Pricing checkedOct 16, 2026Not checked yet
Free trialNot statedNot stated
Open sourceNoYes
Has APIYesYes
Self-hosted optionYesYes
PlatformsmacOS (Intel and Apple Silicon), Windows, Linux (x64 and ARM64)macOS 14+ on Apple Silicon (M1 or newer)
Released2023-052025
Pros
  • A desktop app and a headless server (llmster), so you can chat with models or serve them to your apps.
  • Local APIs plus Python and TypeScript SDKs.
  • Works offline: with its update and extension hosts blocked, it still loaded and served the model.
  • Used 2 GB of memory with our 3B model, a little less than Ollama's 2.2 GB.
  • Leviathan–Chen rejection sampling with residual correction produces bit-exact output at temperature > 0, so agent workflows that depend on non-greedy sampling get the correct distribution instead of a silent approximation that drifts results unpredictably.
  • The drafter lives inside the target checkpoint's own MTP heads, which means no second model in memory — on a MacBook with 64–128 GB unified memory, that headroom stays available for context or parallel sessions rather than a dedicated draft model.
  • OpenAI- and Anthropic-compatible API endpoints with streaming SSE, so tools like Claude Code, Cline, Continue, and the openai-python SDK connect without a translation layer or custom adapter.
  • The vendor reports 2.24× decode speed on Qwen3-27B at temperature 0.6/top_p 0.95 on an M5 Max — meaning you get more tokens per second without switching to a smaller model or lowering temperature to approximate greedy.
  • Apache-2.0 license with no cloud tier or usage telemetry mentioned in the docs, which means inference stays entirely on local hardware — no prompt data leaves the machine.
Cons
  • Slow cold load: lms load took 44.6 s, about 34 s of it before the engine started, with nothing in the logs to explain the wait.
  • Closed source and free under LM Studio's terms; only the lms CLI is open source (MIT).
  • Ollama generated text up to 1.38x faster on the same model and cores.
  • The runtime is Apple Silicon only, with custom Metal kernels and no CUDA path: the moment your deployment target is a Linux server, a cloud VM, or a Windows workstation, this tool is not an option and teams move to vLLM or llama.cpp instead.
  • MTP speculative decoding requires models that ship with native MTP heads in their checkpoint — models without those heads get no speedup and fall back to standard autoregressive decode, which means the 2.24× figure applies only to a specific subset of supported architectures.
  • The project is at v0.1.0-preview.1 and built by a single developer: production teams that need an SLA-backed issue resolution path, a security response process, or a multi-maintainer commit history will hit that wall before they finish the proof-of-concept.

Made up your mind?

LM Studio

Free

Best for People who want a desktop app for downloading and chatting with local models

Visit LM Studio See full review

MTPLX

Free

Best for MacBook Pro / Mac Mini users running 27B+ models locally

Visit MTPLX See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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