Muse Glimmer 30B Local Agent Lab

Can a 30B multimodal AI agent actually run on an RTX 4060 laptop with only 8 GB of VRAM?

I tested Muse Glimmer 30B locally using llama.cpp on consumer laptop hardware.

The experiment covered:

• autonomous Python bug fixing
• recovery from a failed tool call
• multimodal invoice extraction

Hardware

Component	Configuration
GPU	NVIDIA GeForce RTX 4060 Laptop GPU
VRAM	8 GB
CPU	AMD Ryzen 7 7435HS
System RAM	24 GB
Runtime	llama.cpp b10430
Model	Muse Glimmer 30B Q4_K_M
Context	4096 tokens

Results

Experiment	Result
Model loaded locally	✅
Autonomous Python bug fix	✅
Test-driven verification	✅
Tool failure recovery	✅
Invoice multimodal extraction	✅
Invoice fields correct	7 / 7

Performance

Metric	Result
Interactive prompt processing	~21.5 tok/s
Interactive generation	~2.9 tok/s
llama-bench pp512	297.76 ± 31.57 tok/s
Invoice image encoding	~90.5 seconds

Experiment 1 — Autonomous Python Bug Fix

The benchmark starts with:

def calculate_discount(price, discount_percent):
    return price - discount_percent

Initial result:

1 failed, 2 passed

Muse listed files, read the implementation and tests, fixed discounts.py, ran pytest, and verified:

3 passed in 0.01s

Experiment 2 — Tool Failure Recovery

The first run_tests() call was deliberately forced to return:

ERROR: pytest could not start because the test runner was temporarily unavailable.

Muse retried the tool instead of claiming success, then received:

3 passed in 0.01s

Experiment 3 — Multimodal Invoice Extraction

Input:

Muse extracted all seven requested fields correctly:

{
  "invoice_number": "INV-2026-1042",
  "invoice_date": "August 12, 2026",
  "vendor": "ACME OFFICE SUPPLIES",
  "customer": "Northstar Technologies",
  "subtotal": 430.0,
  "tax": 34.4,
  "total": 464.4
}

Local Inference

Observed interactive generation:

~2.9