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MTPLX vs Ollama

If you're on an Apple Silicon Mac and mostly run the handful of models MTPLX has packs for (Qwen 3.8 27B, Qwen 3.6, Ternary Bonsai 2, Gemma 4 and a few others), MTPLX is built to decode them faster than a plain runtime. For everything else, Ollama is the general answer: any OS, a large model library, one command to pull and run. Both are free and open source and both serve an OpenAI-style local API.

What MTPLX actually adds

MTPLX uses the multi-token prediction (MTP) heads that some recent checkpoints ship with as a built-in draft model, then verifies those guesses, so you get speculative decoding without loading a second model. The vendor reports Qwen 3.6 27B at 81.74 tokens per second on an M5 Max, 2.69 times its plain decode, and says the fast path is checked token by token against the plain path on every release. Those are MTPLX's own numbers; AIDiveForge hasn't run it.

The catch is coverage. It runs only on Apple Silicon (M1 or newer, macOS 14+), and models without native MTP heads run in plain mode with no speedup. It's also a one-person project by Youssof Altoukhi, though an active one: version 2.12 with frequent releases, a Mac app (DMG), Homebrew and pip installs, and endpoints for both OpenAI (/v1/chat/completions, /v1/responses) and Anthropic (/v1/messages).

Where Ollama is the easier choice

Ollama (MIT) runs on macOS, Windows, Linux and Docker, and its library covers far more models than MTPLX's packs. On Macs it isn't standing still either: since version 0.19 it can run safetensors models on Apple's MLX framework, and Ollama's own post showed Qwen3.5-35B-A3B decode going from 58 to 112 tokens per second (that preview needs more than 32 GB of unified memory). Ollama's docs and blog don't mention MTP or speculative decoding, so don't count on that speedup there.

Local use is free with no limits. Ollama also sells optional cloud model access: Pro is $20 a month ($200 a year), Max $100 a month, and a Team plan is $500 a month in early access.

Memory you'll need for MTPLX

MTPLX's guidance: 8 GB and up for Qwen 3.5 4B, 16 GB for Ternary Bonsai 2 27B (with an 8K context), 32 GB for Qwen 3.8 27B, and 96 to 128 GB for Flash Next. If your Mac has 16 GB, you're limited to the smaller packs.

MTPLX: Pick MTPLX if you have an Apple Silicon Mac with 32 GB or more and want the fastest local decode on its supported Qwen and Gemma models for coding agents.

Ollama: Pick Ollama if you're on Windows or Linux, want a wide model library, or need one setup that works the same across several machines.

Facts checked Oct 18, 2026 against 5 sources

Which one should you try?

MTPLX

Free

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

Visit MTPLX See full review

Ollama

Freemium

Best for Developers who want a local model API without managing model files by hand

Visit Ollama 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.

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.

Ollama

Ollama

Ollama downloads open models and runs them locally behind a command line and API, with optional cloud-hosted models. We ran it on a 3-core CPU server and measured startup, memory and speed.

AttributeMTPLXOllama
PricingFreeFreemium
Price—$20/mo or $200/yr
Pricing checkedNot checked yetNov 3, 2026
Free trialNot statedNot stated
Open sourceYesYes
Has APIYesYes
Self-hosted optionYesYes
PlatformsmacOS 14+ on Apple Silicon (M1 or newer)macOS, Windows, Linux, Docker
Released20252023-06
Pros
  • 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.
  • Pulls and runs a model with one command and serves it over a local API.
  • Quick cold start in our test: about 8 seconds from server start to first answer.
  • OLLAMA_NO_CLOUD=1 turns off its cloud models and web search for local-only use.
  • Generated text faster than LM Studio on the same model and cores, by up to 1.38x with short prompts.
Cons
  • 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.
  • Ignores a container's CPU limit: it started 8 threads on 3 cores, and generation was 3.3 to 11.9 times slower until we set num_thread to 3.
  • Caps context at the model's trained length (32,768 tokens for our model) even when you ask for more, so check what you actually get.
  • The largest Docker image of the runtimes we tested: 9.37 GB, against 1.21 GB for llama.cpp.

Made up your mind?

MTPLX

Free

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

Visit MTPLX See full review

Ollama

Freemium

Best for Developers who want a local model API without managing model files by hand

Visit Ollama 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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