Muse Glimmer vs Needle2: Choosing Between a 30B Edge Agent and a 14MB Embedded Agent
"Run the agent on the device" used to mean one thing: a cloud call you wished you could avoid. In August 2026, two open-weight releases made it a real architectural decision — at two wildly different scales. Meta's Muse Glimmer is a 30-billion-parameter agentic model distilled for always-on local workflows on consumer hardware. Cactus Compute's Needle 2 is a 45-million-parameter tool-calling model that ships as a single 14MB binary and runs a full session in about 28MB of RAM.
Both are Apache 2.0. Both are built for agents. Both run without a network. But they are not competitors so much as two answers to two different questions: "How much reasoning can I keep on my own machine?" versus "How small can an agent get and still reliably act?" This comparison walks through the specs, the hardware each one actually needs, the use cases each is built for, and the caveats worth knowing before you commit a design to either.
Quick Verdict
| Question | Muse Glimmer | Needle 2 |
|---|
| What is it? | 30B-parameter open agentic model from Meta, distilled from a larger teacher | 45M-parameter open tool-calling model from Cactus Compute |
| Model footprint | Tens of GB on disk; runs with 4-bit quantization | Single 14MB binary; ~28MB peak session RAM |
| Target hardware | Mac or PC with a single consumer GPU | Phones, Raspberry Pi, wearables, VR headsets, even ESP32-class microcontrollers |
| Core strengths | Multi-step reasoning, coding, multimodal input, failure recovery, long-horizon tasks | Tool calling, structured extraction, device control, embeddings — with grammar-constrained output |
| License | Apache 2.0 | Apache 2.0 |
| Best for | A capable local "thinking" agent on a workstation or laptop | An always-on "acting" layer inside constrained devices |
| Biggest caveat | Needs real GPU memory; not a phone-class model | Deliberately narrow: it acts on declared tools, it does not chat or reason broadly |
Short version: if your device has a consumer GPU and you need an agent that can reason, write code, and recover from errors, Muse Glimmer is the relevant class. If your device is a $50 board, a budget phone, or a wearable and you need reliable function calls in kilobytes of headroom, Needle 2 is built for exactly that. Many real systems will want both.
Model Size and Architecture
The parameter counts differ by nearly three orders of magnitude, and that gap drives everything else.
Muse Glimmer is a 30B-parameter model from Meta Superintelligence Labs, trained with logit distillation from a much larger teacher (Muse Spark), then mid-trained on long-context, agent-heavy data and post-trained with supervised fine-tuning plus on-policy distillation and reinforcement learning across general, reasoning, coding, and agentic domains. It ships with a lightweight DFlash-based "drafter" network for speculative decoding, and Meta validated that its compression introduces minimal degradation on agentic tasks across 15 benchmarks. A dedicated perception encoder gives it interleaved text-and-image input, so an agent can read screenshots, charts, and documents. Optimized integrations for llama.cpp, MLX, and ExecuTorch are landing around release.
Needle 2 is a 45M-parameter Simple Attention Network: a Hadamard MLP in place of the standard FFN, grouped-query attention, engram key-value memory, and multi-lane hyper-connections, compressed to CQ2-bit with Cactus Quants and baked into its own engine (the design and ablations are in the project's arXiv paper). The result is the entire model as one 14MB binary — no runtime, no downloads, no network. Every decode step is constrained by a byte-level grammar compiled from your declared schemas, so tool calls come back as structured data that parses, every time. A learned confidence head scores each response so you can gate actions on a threshold.
One honest framing: Muse Glimmer is a small foundation model. Needle 2 is a purpose-built actuator that happens to be a language model. Comparing benchmark scores across the two is mostly meaningless; compare them against the job.
Hardware Requirements
This is where the decision is usually made for you.
Muse Glimmer targets consumer workstations: a Mac or PC with a single consumer GPU. At 30B parameters, even at 4-bit quantization you should plan on roughly 18–24GB of available memory for comfortable inference, which means an Apple Silicon Mac with unified memory or a PC with a 24GB-class GPU. It is "edge" in the sense of
your hardware instead of a datacenter — not in the sense of a phone.
Needle 2 targets genuinely constrained hardware. Published numbers: 500 tokens/sec decode on a Raspberry Pi 5, 400–1,500 tokens/sec on VR devices like the Meta Quest 3S and Apple Vision Pro, and 300–700 tokens/sec on sub-$200 phones such as Samsung's A-Series. Peak session RAM stays near 28MB because context is a 256-token sliding window with tools pinned as KV sinks — memory stays flat no matter how long the session runs. It reaches microcontrollers like the ESP32-P4, and community reports have it on an ESP32-S3 in about 11MB. Supported targets include ARM64, x86-64, ARMv7, RISC-V, and WebAssembly across Apple, Windows, Linux, Android, and Raspberry Pi.
If your device budget is measured in megabytes, Muse Glimmer is not an option and that is not a criticism — it was never the goal. If your device budget includes a GPU, Needle 2 will run, but you would be leaving most of the reasoning capability you paid for on the table.
Use Cases
Where Muse Glimmer fits:
- Local personal agents that manage schedules, draft messages, and organize files with deep access to personal context
- Local coding agents: Meta evaluates it on SWE-Bench-style full-task completion, plus DeepSearch QA, MCP-Atlas, and τ-Bench
- LLM-as-a-judge evaluation runs you want off cloud APIs
- Multimodal agents that read screenshots and documents alongside conversation
- Long-horizon workflows where the model must sustain a plan, call tools with precise schemas, and recover when a call fails instead of halting
Where Needle 2 fits:
- Smart home control: "dim the bedroom and lock up" becomes two ordered tool calls, executed offline with no hub round trip
- Phones and wearables: an assistant that acts on the device — make an album from last weekend's photos, dim the screen, find the lease in your files
- Robots and small devices: nuanced instructions mapped to sequences of executable moves
- Structured extraction on-device: declare a shape, hand over messy text, get typed fields back — invoices, bookings, notifications
- Local embeddings for search, matching, and routing; a built-in retrieval head can render only the top five tools from a large declared catalogue per turn
The pattern: Muse Glimmer is for tasks where the hard part is
deciding what to do. Needle 2 is for tasks where the hard part is
doing it reliably with nothing.
Self-Hosting and Deployment
Both models are genuinely open — Apache 2.0 weights, no API keys, no usage metering — which matters if you are shipping a product rather than a demo.
Muse Glimmer distributes through
Hugging Face with
developer documentation, and is built to work with the tooling developers already run: llama.cpp, MLX, and ExecuTorch integrations mean you can go from download to a working local agent in minutes on supported setups. Deployment looks like any modern local-LLM stack: pick a runtime, pull weights, quantize, serve.
Needle 2 takes a different shape: the model is baked into its own engine binary, so there is no separate runtime to install at all. Source, engine, and training code live at
github.com/cactus-compute/needle, with a Python package (
pip install cactus-needle) for quick integration. You declare tools by decorating functions — the signature gives argument types, the docstring is the tool description — and
run() completes the loop: the model picks a call, Needle executes your function, feeds the result back, and returns the answer. The strict contract is the point: text in, JSON out, grammar-constrained so malformed calls are structurally impossible.
Worth knowing: Cactus has since released Needle 3 (8–29MB, an "intelligence ladder" of depths from 2 to 20 layers) on its site, so evaluate the current generation before pinning a version — the line is moving fast.
Data Privacy
Both models share the fundamental privacy property of on-device inference: user data never leaves the hardware, works with no internet connection, and sends nothing to a vendor API. For products handling health-adjacent data, home automation state, or personal files, that removes an entire class of compliance and trust problems.
The differences are at the margins. Muse Glimmer's positioning is the always-on
personal agent — the one with deep access to your messages, schedule, and files — so Meta's framing emphasizes keeping that context on your Mac or PC rather than in a cloud session. Needle 2's positioning is the
embedded actuator inside other people's devices: confidence-gated execution means low-confidence commands can be escalated (to a human, or to a bigger model) rather than guessed at, which is a safety property as much as a privacy one.
One caveat for both: on-device inference protects data in transit and at the vendor, but the device itself becomes the trust boundary. A compromised handset or workstation leaks the same data whether the model is 45M or 30B parameters.
Honest Caveats
- Different leagues, by design. Do not benchmark these against each other. Muse Glimmer would be absurd on a microcontroller; Needle 2 will not write your code review. If a comparison you read ranks one above the other on a single score, check what workload was measured.
- Muse Glimmer's hardware floor is real. "Runs on consumer hardware" means a GPU with serious memory. If your target is a phone, tablet, or fanless appliance, it is the wrong tool today.
- Needle 2 is deliberately narrow. Its 256-token sliding window and grammar-constrained decoding are features for reliability, but they mean no long conversations, no open-ended chat, and no broad reasoning. It trades general capacity to beat models 10x its size specifically on mobile tool calls. Treat it as a component, not a brain.
- Both are young. Muse Glimmer shipped in August 2026 with runtime integrations still landing; Needle's line has already moved from the original Needle to Needle 2 to Needle 3 within months. Pin versions, and replay your own workloads against each release.
- Vendor benchmarks are vendor benchmarks. Meta's agentic evals and Cactus's tool-call comparisons are published by the teams that built the models. Before committing a product design, run your own tasks, your own prompts, and your own failure cases.
A Practical Two-Tier Pattern
For many products the right answer is both. A common emerging pattern: Needle 2 (or its successor) runs on the device as the always-on actuator — parsing intent, calling local tools, extracting structured data, embedding for local search — and escalates anything above its confidence threshold or beyond its reasoning depth to a larger model. That larger model can be Muse Glimmer on the user's own workstation or home server for privacy-sensitive workloads, or a cloud model when privacy allows. You get sub-second local action for the 90% of requests that are simple commands, and real reasoning for the 10% that need it, without paying cloud latency — or cloud trust — for every interaction.
Official Resources
Muse Glimmer (Meta)
Needle 2 (Cactus Compute)
FAQ
Can Muse Glimmer run on a phone?
Not realistically today. It is a 30B-parameter model; even quantized it expects a consumer-GPU-class memory budget. For phones, Needle 2 is the model in this pair actually designed for that constraint.
Can Needle 2 replace a general assistant?
No, and it does not try to. It is trained for tool calling, device use, structured extraction, and embeddings, with a 256-token context window. Open-ended conversation and multi-step reasoning are explicitly traded away for size, speed, and reliability.
Which is better for a privacy-first product?
Both keep inference fully on-device under Apache 2.0. The deciding factor is hardware, not privacy posture: workstations and laptops point to Muse Glimmer; phones, wearables, and microcontrollers point to Needle 2.
Do I have to choose just one?
No. A two-tier design — a tiny on-device actuator escalating to a larger local or cloud model — is a legitimate architecture, and both models' confidence-gating and tool-routing features support it.
What about Needle 3?
Cactus Compute has released a newer generation (8–29MB, selectable depths from 2 to 20 layers). If you are starting fresh, evaluate the current release on the official site; the Needle 2 numbers here remain a valid reference for the 14MB class.
Are these production-ready?
Both are real releases with weights, docs, and working integrations — and both are months old with fast-moving successors. Prototype against your actual workloads, pin your versions, and re-validate on each release before shipping.
Final Verdict
Muse Glimmer and Needle 2 mark the two ends of the on-device agent spectrum in 2026. Muse Glimmer brings near-frontier agentic reasoning — tool use, coding, multimodal understanding, failure recovery — down to a single consumer GPU, under an open license. Needle 2 proves that reliable tool calling and structured extraction fit in 14MB and 28MB of RAM, on hardware that costs less than dinner. The choice is rarely between them: it is about which tier of your system each one owns. Start from your device's memory budget and your task's reasoning depth, and the answer usually picks itself.