VibeThinker-3B-Q4_K_M-GGUF
VibeThinker-3B-Q4_K_M-GGUF is a 3-billion-parameter language model quantized to GGUF format for efficient local deployment. It is optimized for running on consumer GPUs (e.g., RTX 3080 10GB) via llama.cpp and supports reasoning, math, code generation, and instruction-following tasks. The model is MIT-licensed, non-gated, and maintained as a community quantization of the original WeiboAI/VibeThinker-3B.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | semparuthiveeran |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 78.2k |
| Likes | 3 |
| Last updated | 2026-06-17 |
| Source | semparuthiveeran/VibeThinker-3B-Q4_K_M-GGUF |
What VibeThinker-3B-Q4_K_M-GGUF is
This is a GGUF-quantized derivative of WeiboAI/VibeThinker-3B, converted via llama.cpp and ggml.ai's GGUF-my-repo. The Q4_K_M quantization scheme reduces model size for local inference. It is compatible with llama.cpp (CLI and server modes), supports context lengths up to 2048 tokens (as shown in example invocations), and is tagged for math, code, reasoning, GPQA, and instruction-following use cases. The model card does not disclose full parameter count, training data, or base model architecture details.
Run VibeThinker-3B-Q4_K_M-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="semparuthiveeran/VibeThinker-3B-Q4_K_M-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: Q4_K_M quantization of 3B model typically requires ~2–3 GB VRAM for inference at context length 2048. Card states optimization for RTX 3080 (10 GB), suggesting headroom for larger context or batch processing. CPU-only inference possible but slow. Verify actual VRAM usage for your workload; quantization and batch size affect memory footprint.
Not stated in card. LoRA or QLoRA fine-tuning feasibility on this quantized model is unknown. If fine-tuning is required, consider starting from the original WeiboAI/VibeThinker-3B in full precision, then re-quantize. GGUF-format models may have limited fine-tuning tooling support compared to HuggingFace-native formats.
When to avoid it — and what to weigh
- High-Throughput Production Inference — 3B parameters and quantization are trade-offs for latency and quality. If you need throughput comparable to 7B+ models or multi-tenant SaaS, consider larger models or managed endpoints.
- Complex Long-Context Tasks — Card examples show 2048 context length. Complex RAG, long document summarization, or multi-turn conversations with large context windows may degrade quality.
- Proprietary or Commercial Support Requirements — This is a community quantization. No SLA, commercial support, or liability from the quantizer (semparuthiveeran). Ensure your team can debug and maintain llama.cpp deployments independently.
- Uncertain Model Provenance or Licensing — The original WeiboAI/VibeThinker-3B card is not fully referenced in this excerpt. Verify the base model's training data, licensing restrictions, and compliance with your use case before production deployment.
License & commercial use
MIT license. Permissive OSI-approved license allowing modification, redistribution, and commercial use with attribution.
MIT license permits commercial use. However, this is a community quantization of WeiboAI/VibeThinker-3B. Verify the original model's license, training data restrictions, and any usage terms from WeiboAI. Ensure compliance with any downstream obligations before commercializing. No warranty or indemnification from the quantizer.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No explicit security statement in card. Self-hosted deployment eliminates cloud-provider data transit concerns but shifts responsibility to your infrastructure. llama.cpp is widely used but conduct your own review of binary integrity and supply-chain risk. Quantized models reduce attack surface by limiting full-precision weights on-disk. No information on prompt-injection hardening, jailbreak resistance, or adversarial robustness.
Alternatives to consider
Ollama + Mistral 7B or Llama 2 7B
Larger, well-maintained models with broader compatibility and better documentation. Trade-off: higher VRAM (~5–6 GB for 7B quantized), slower inference on RTX 3080.
LM Studio (UI + pre-packaged GGUF models)
Reduces deployment friction for non-engineers. Offers curated, tested GGUF quantizations. Trade-off: less control, potential licensing ambiguity on bundled models.
OpenAI API or Anthropic Claude (managed)
Cloud-hosted, no hardware maintenance, strong support. Trade-off: higher latency, data egress, recurring costs, no offline capability.
Ship VibeThinker-3B-Q4_K_M-GGUF with senior software developers
VibeThinker-3B offers fast, privacy-first inference on consumer hardware. Start with llama.cpp, verify base-model compliance, and evaluate against your context-length and quality requirements.
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VibeThinker-3B-Q4_K_M-GGUF FAQ
Can I use this model commercially?
What GPU do I need?
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What is the context window, and is it sufficient?
Custom software development services
Adopting VibeThinker-3B-Q4_K_M-GGUF is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Deploy Local AI?
VibeThinker-3B offers fast, privacy-first inference on consumer hardware. Start with llama.cpp, verify base-model compliance, and evaluate against your context-length and quality requirements.