Bielik-11B v3.0 Instruct FP8 Dynamic
An FP8-quantized build of Bielik-11B v3.0 Instruct, designed to run on vLLM or SGLang with roughly 50% less GPU memory than the BF16 original. Weights and activations are both quantized dynamically. Multilingual, with Polish as the primary target language plus 31 other European languages.
If you have a 4090 or an H100 and need a Polish-capable instruction model that actually fits in VRAM, this is a practical pick. The 50% memory saving is real and the Apache-2.0 license removes commercial friction. The hard blocker is the Ada/Hopper GPU requirement — anything older won't run it at all. The 4096-token context is a genuine limitation for document-heavy workloads, so hedge if that matters to you.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.30/10. License is explicitly apache-2.0 in the HF metadata and the row reflects that correctly. Parameter count, vendor, family, and FP8 quantization details all match the card. The row is honest about the Ada/Hopper GPU requirement, the 4096 context limit, and weak community traction — exactly the operator-grade framing runlocalai requires. Best use case is appropriately sharp (Polish instruction following on constrained GPUs). Brand fit is solid but slightly niche given the GPU compute-capability requirement narrows the audience.
Flags: - Context length of 4096 is stated in the vLLM example but the underlying Bielik-11B-v3.0 base may support more — worth double-checking the base model's actual max context - Slightly niche: FP8 requires Ada/Hopper, which excludes most hobbyist local-AI readers
Overview
An FP8-quantized build of Bielik-11B v3.0 Instruct, designed to run on vLLM or SGLang with roughly 50% less GPU memory than the BF16 original. Weights and activations are both quantized dynamically. Multilingual, with Polish as the primary target language plus 31 other European languages.
Strengths
- ~50% VRAM reduction vs BF16 baseline via FP8 dynamic quantization
- Optimized for Polish; covers 32 European languages total
- Apache-2.0 license — commercial use allowed
- Designed for vLLM and SGLang, straightforward production deployment
Weaknesses
- Requires Nvidia GPU with compute capability ≥ 8.9 (Ada Lovelace / Hopper only — RTX 4000 series or H100)
- 4096-token context is short; many competing 11B models offer 8k–128k
- FP8 quantization carries some quality loss compared to the original BF16 weights
- Low community traction — 8k downloads and 6 likes suggest limited real-world validation
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 6.1 GB | 8 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Bielik-11B v3.0 Instruct FP8 Dynamic.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Bielik-11B v3.0 Instruct FP8 Dynamic?
Can I use Bielik-11B v3.0 Instruct FP8 Dynamic commercially?
What's the context length of Bielik-11B v3.0 Instruct FP8 Dynamic?
Source: huggingface.co/speakleash/Bielik-11B-v3.0-Instruct-FP8-Dynamic
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Bielik-11B v3.0 Instruct FP8 Dynamic runs on your specific hardware before committing money.