- Muse Glimmer on one GPU is achievable using 4-bit quantized GGUF formats on 24GB+ VRAM cards
- Total parameter count sits at roughly 29.6B, requiring aggressive quantization for single-card deployment
- DFlash drafters and optimized runtimes push RTX 5090 inference to an average of 233.4 tokens per second
- Context window supports up to 131,072 tokens, enabling long-running agentic coding workflows
- Apache 2.0 license permits commercial use, modification, and redistribution of the model
Muse Glimmer One GPU Hardware Requirements
Running a 30-billion-parameter model on a single graphics card requires careful hardware matching. Muse Glimmer is designed specifically for local deployment on consumer hardware, but the exact configuration determines your inference speed and context capacity. The official model collection provides multiple format options to fit different VRAM tiers.
At full BF16 precision, the model weights alone require approximately 60 GB of memory, which exceeds typical consumer GPU limits. However, Meta has optimized the model for quantized deployment, targeting three primary VRAM tiers: 24GB, 32GB, and 64GB. A 4-bit quantized build compresses the raw parameter footprint to roughly 15 GB, making single-GPU inference practical on high-end consumer cards.
The raw weight size is only the baseline. You must account for the KV cache, context window allocation, and inference runtime overhead when calculating total VRAM usage. A 4-bit model occupying 15 GB of weights may require 18-20 GB total during active inference.
Single GPU VRAM Tier Comparison
| VRAM Tier | Recommended Format | Approx. Weight Size | Practical Context | Best Use Case |
|---|---|---|---|---|
| 24GB | 4-bit Quantized GGUF | ~15 GB | Up to 32K tokens | Coding assistance, short agentic loops |
| 32GB | 4-bit or Mixed Precision | ~15-22 GB | Up to 64K tokens | Long-context coding, multi-step agents |
| 64GB | BF16 or High-Precision GGUF | ~60 GB | Full 131K tokens | Maximum quality, complex workflows |
The RTX 5090 represents the current top-tier consumer option for single-GPU deployment. When paired with the official DFlash drafter mechanism, Meta's own testing demonstrates an average throughput of 233.4 tokens per second, making interactive agentic workflows highly responsive.
Choosing Your Model Format
Selecting the correct model file is the most critical decision for single-GPU deployment. The official Hugging Face collection includes BF16 weights, GGUF quantized builds, ExecuTorch packages, and DFlash drafters. Each format serves a different deployment scenario and hardware profile.
BF16 Original Weights
- Highest fidelity to original training
- Requires ~60 GB memory for weights
- Not suitable for single consumer GPU
- Best for multi-GPU or high-memory server setups
4-Bit Quantized GGUF
- Compressed weight footprint (~15 GB)
- Fits comfortably on 24GB VRAM cards
- Minor quality reduction acceptable for most tasks
- Ideal for local coding and agent workflows
ExecuTorch
- Optimized for edge and mobile deployment
- Streamlined inference pipeline
- Supports AMD and NVIDIA acceleration
- Best for on-device applications
DFlash Drafter
- Speculative decoding acceleration
- Pairs with main model for faster generation
- Achieves 233.4 tok/s on RTX 5090
- Recommended for interactive speed requirements
GGUF Quantization Levels Detailed
| GGUF Format | Bit Depth | Memory Use | Speed | Quality Tradeoff | Single GPU Fit |
|---|---|---|---|---|---|
| Q8_0 | 8-bit | High (~32 GB) | Moderate | Minimal loss | Requires 32GB+ VRAM |
| Q6_K | 6-bit | Medium-High (~24 GB) | Good | Slight degradation | Fits 32GB cards comfortably |
| Q5_K_M | 5-bit | Medium (~20 GB) | Fast | Noticeable but acceptable | Sweet spot for 24GB cards |
| Q4_K_M | 4-bit | Low (~15 GB) | Fastest | Moderate tradeoff | Best for 24GB VRAM baseline |
| Q3_K_M | 3-bit | Lowest (~12 GB) | Very fast | Significant quality loss | Emergency fit for 16GB cards |
For most developers running Muse Glimmer on a single GPU, the Q4_K_M or Q5_K_M GGUF formats provide the best balance of quality, speed, and memory efficiency. Start with Q5_K_M if your VRAM allows, then drop to Q4_K_M if you need more context headroom.
Step-by-Step Single GPU Deployment
Deploying Muse Glimmer on a single GPU involves selecting a runtime, downloading the appropriate model format, and configuring memory allocation. The simplest path uses LM Studio for a graphical interface, while developers seeking more control can use llama.cpp, vLLM, or SGLang directly.
Verify Your GPU and Drivers
Confirm your GPU has at least 24GB of VRAM for quantized deployment. Update to the latest NVIDIA CUDA toolkit or AMD ROCm drivers. Verify that your system has sufficient cooling and power delivery, as sustained 30B inference generates significant thermal load.
Download the Quantized Model
Navigate to the official Hugging Face GGUF repository. Download the Q4_K_M or Q5_K_M file depending on your VRAM tier. The file size ranges from 15 to 22 GB, so ensure you have adequate storage space on a fast SSD for reasonable load times.
Configure Your Inference Runtime
Load the GGUF file in your chosen runtime (LM Studio, llama.cpp, or Ollama). Set the GPU offload layers to maximum so the entire model resides in VRAM. Allocate context length based on your remaining VRAM after model loading, typically 8,192 to 32,768 tokens for a 24GB card.
Run a Benchmark Prompt
Test the deployment with a coding or reasoning prompt. Monitor VRAM usage to ensure you are not hitting out-of-memory errors. Check tokens-per-second throughput to confirm the GPU is properly utilized and not falling back to CPU computation.
Connect to Agent Frameworks
Once basic inference is stable, expose the local model through an API endpoint compatible with your agent scaffold. Configure tool definitions, set appropriate system prompts, and begin testing multi-step agentic workflows.
Before going live with your local deployment, verify that your inference runtime supports the full context window you intend to use. Some GGUF runtimes cap context length below the model's 131K maximum. Check your runtime documentation for context configuration limits.
Coding and Agentic Performance on One GPU
Muse Glimmer is purpose-built for agentic coding workloads. Unlike general-purpose chat models, it excels at multi-step reasoning, function calling, tool use, and failure recovery within extended agent loops. Running these workflows locally on a single GPU keeps proprietary source code and project data entirely on your workstation.
The model's agentic capabilities shine when integrated with a scaffold that manages conversation state, tool execution, and observation feedback. A typical local coding agent workflow involves the model inspecting project files, planning edits, executing changes through development tools, reading test output, and iterating until the task is complete.
Agentic Coding Workflow Stages
| Stage | Model Action | Tool Interaction | VRAM Impact |
|---|---|---|---|
| Planning | Decomposes task into steps | None | Baseline inference |
| File Inspection | Reads relevant source files | File read function | Context grows |
| Code Generation | Produces implementation | Write to file function | KV cache expands |
| Test Execution | Requests test run | Shell command tool | Context stable |
| Error Analysis | Reads failure output | Test output observation | Context grows further |
| Iteration | Plans fix based on error | Repeat edit cycle | Cache management needed |
Long agentic loops accumulate context rapidly. Each tool call, observation, and intermediate result consumes tokens from your context budget. On a 24GB GPU with a 4-bit model, you may be limited to 32K context, which can fill quickly during complex multi-file coding tasks. Implement context pruning or summarization in your agent scaffold.
For developers building private coding assistants, the combination of local inference and tool-calling capabilities means no source code leaves the workstation. The model can read files, execute terminal commands, run test suites, and inspect compiler errors through structured function calls defined by the host application.
Vision and Multimodal on a Single Card
Muse Glimmer integrates a dedicated perception encoder that processes visual inputs alongside text. This multimodal capability enables workflows where the model reasons about screenshots, charts, diagrams, and document images as part of coding and agentic tasks. On a single GPU, the vision encoder shares VRAM with the language model, so visual inputs add to the overall memory footprint.
The most practical multimodal use cases for local deployment include UI debugging through screenshot analysis, chart interpretation during data workflows, and document understanding for codebases with visual documentation. A developer can capture an application screenshot, feed it to the model with text instructions, and receive analysis of visible errors, layout issues, or interface states.
Screenshot Understanding
- UI debugging from captured screenshots
- Error state analysis from visible dialogs
- Layout reasoning for frontend development
- Application state identification
Chart and Document Analysis
- Data extraction from visual charts
- Document page comprehension
- Diagram-to-code workflows
- Mixed text-and-image reasoning
Image inputs consume additional VRAM beyond text tokens. High-resolution screenshots can add significant memory pressure during processing. Resize or crop images before sending them to the model to conserve VRAM on single-GPU setups, especially when running 4-bit quantized weights.
Benchmark Expectations and Hardware Comparison
Understanding expected performance helps you choose the right hardware and set realistic expectations for interactive workflows. Muse Glimmer benchmarks focus on the workloads it is designed for: agentic task completion, coding accuracy, tool-use reliability, and long-running workflow persistence.
Consumer GPU Performance Comparison
| GPU | VRAM | Recommended Format | Est. Speed (tok/s) | Context Headroom | Workflow Suitability |
|---|---|---|---|---|---|
| RTX 5090 | 32GB | Q5_K_M + DFlash | ~233 (with DFlash) | Up to 64K | Full agentic coding |
| RTX 4090 | 24GB | Q4_K_M | ~80-120 | Up to 32K | Coding assistance, short agents |
| RTX 3090 | 24GB | Q4_K_M | ~50-70 | Up to 24K | Basic coding, limited agents |
| AMD Radeon 7900 XTX | 24GB | Q4_K_M (Vulkan) | ~60-90 | Up to 32K | Coding, tool use |
| Mac Studio M3 Ultra | 128GB Unified | BF16 or Q8 | ~40-60 | Full 131K | Maximum quality local |
Speed estimates are based on typical local inference configurations. Actual throughput depends on runtime optimization, context length, batch size, and whether speculative decoding is enabled. The RTX 5090 figure of 233.4 tok/s comes from official Meta testing with the DFlash drafter enabled.
Benchmark Workload Categories
| Workload | What It Measures | Why It Matters for Single GPU |
|---|---|---|
| Agentic Task Completion | Multi-step execution success | Determines if local agents can complete real tasks |
| Coding Accuracy | Code generation correctness | Validates model quality after quantization |
| Tool Use Reliability | Structured function calling | Essential for agent loops on local hardware |
| Long-Running Persistence | Task continuation across steps | Tests context management over extended workflows |
| Failure Recovery | Error response and adaptation | Reduces workflow breakdowns in autonomous mode |
| Local Throughput | Tokens per second on consumer GPU | Determines interactive responsiveness |
Single GPU Deployment Checklist:
- GPU has at least 24GB VRAM with updated drivers
- Downloaded Q4_K_M or Q5_K_M GGUF from official Hugging Face repository
- Inference runtime configured for full GPU offload
- Context length set within VRAM budget after model loading
- Benchmark prompt tested and throughput verified
- Agent scaffold connected with tool definitions configured
- Context pruning strategy implemented for long workflows
FAQ
Q: Can Muse Glimmer run on a single consumer GPU?
Yes. Using 4-bit quantized GGUF formats, Muse Glimmer 30B fits on a single 24GB VRAM GPU. The compressed weights occupy approximately 15 GB, leaving room for context and runtime overhead. Higher VRAM tiers (32GB, 64GB) allow larger context windows or higher-precision formats.
Q: What is the best GPU for running Muse Glimmer locally?
The RTX 5090 with 32GB VRAM represents the top consumer option, achieving 233.4 tokens per second with the DFlash drafter. The RTX 4090 with 24GB VRAM is also viable at lower speeds. AMD Radeon 7900 XTX and Mac Studio with unified memory are supported alternatives.
Q: How much VRAM do I need for Muse Glimmer on one GPU?
The minimum practical VRAM is 24GB using a 4-bit quantized GGUF format. This leaves room for approximately 32K tokens of context. For longer context windows or higher precision, 32GB or 64GB VRAM tiers are recommended by the official model documentation.
Q: Does quantization significantly reduce Muse Glimmer's coding ability?
4-bit quantization introduces a moderate quality tradeoff, but the model retains strong coding and agentic capabilities. For developers who need maximum fidelity, Q5_K_M or Q6_K formats offer a closer approximation of the original BF16 weights while still fitting on a single GPU.
Q: Can I use Muse Glimmer for commercial applications on a single GPU?
Yes. Muse Glimmer is released under the Apache 2.0 license, which permits commercial use, modification, and redistribution. You can build and deploy commercial products using the model on your own hardware without licensing restrictions beyond attribution requirements.