Bielik 11B V2.2 Instruct GGUF — Hardware Requirements & GPU Compatibility
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Bielik 11B V2.2 Instruct GGUF is a 11B-parameter open language model from speakleash. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 7.32 GB of VRAM — see which GPUs and Macs can run it below.
424 downloads
19 likesSpecifications
- Publisher
- speakleash
- Parameters
- 11B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,128
- Release Date
- 2024-10-22
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Bielik 11B V2.2 Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 7.3 GB | 13.6 GB | 6.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 8.6 GB | 14.8 GB | 7.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 9.8 GB | 16.1 GB | 9.07 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 11.7 GB | 18.0 GB | 11.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Bielik 11B V2.2 Instruct GGUF?
Q4_K_M · 7.3 GBBielik 11B V2.2 Instruct GGUF (Q4_K_M) requires 7.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 33K context window can add up to 6.3 GB, bringing total usage to 13.6 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Which Devices Can Run Bielik 11B V2.2 Instruct GGUF?
Q4_K_M · 7.3 GB33 devices with unified memory can run Bielik 11B V2.2 Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Bielik 11B V2.2 Instruct GGUF need?
Bielik 11B V2.2 Instruct GGUF requires 7.3 GB of VRAM at Q4_K_M, or 11.7 GB at Q8_0. Full 33K context adds up to 6.3 GB (13.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 11B × 4.8 bits ÷ 8 = 6.6 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M7.3 GBQ4_K_M + full context13.6 GB- What's the best quantization for Bielik 11B V2.2 Instruct GGUF?
For Bielik 11B V2.2 Instruct GGUF, Q4_K_M (7.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (8.6 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★7.3 GBQ5_K_M8.6 GBQ6_K9.8 GBQ8_011.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Bielik 11B V2.2 Instruct GGUF on a Mac?
Bielik 11B V2.2 Instruct GGUF requires at least 7.3 GB at Q4_K_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Bielik 11B V2.2 Instruct GGUF locally?
Yes — Bielik 11B V2.2 Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 7.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Bielik 11B V2.2 Instruct GGUF?
At Q4_K_M, Bielik 11B V2.2 Instruct GGUF can reach ~398 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~90 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 7.3 × 0.55 = ~398 tok/s
Estimated speed at Q4_K_M (7.3 GB)
~398 tok/s~90 tok/s~298 tok/s~246 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Bielik 11B V2.2 Instruct GGUF?
At Q4_K_M, the download is about 6.60 GB. The full-precision Q8_0 version is 11.00 GB.
- Which GPUs can run Bielik 11B V2.2 Instruct GGUF?
35 consumer GPUs can run Bielik 11B V2.2 Instruct GGUF at Q4_K_M (7.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 27 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Bielik 11B V2.2 Instruct GGUF?
33 devices with unified memory can run Bielik 11B V2.2 Instruct GGUF at Q4_K_M (7.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.