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Bielik-11B-v2.3-Instruct

speakleash/Bielik-11B-v2.3-Instruct

Bielik-11B-v2.3-Instruct at Q4_K_M is exactly 6,724,050,496 bytes (6.26 GiB / 6.72 GB) — an effective 4.816 bits per weight, not the nominal 4. Its KV cache at 32K is 6.25 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
11.2B
Architecture
llama
50 layers
Context
32,768
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q4_K_M6.26 GiB6,724,050,4964.816speakleash
Q5_K_M7.36 GiB7,907,040,8325.664speakleash
Q6_K8.53 GiB9,163,968,0646.564speakleash
Q8_02 shards22.34 GiB23,984,366,17617.180speakleash

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.78 GiB0.78 GiB50 / 0 / 0
8,1921.56 GiB1.56 GiB50 / 0 / 0
16,3843.13 GiB3.13 GiB50 / 0 / 0
32,7686.25 GiB6.25 GiB50 / 0 / 0
65,53612.50 GiB12.50 GiB50 / 0 / 0
131,07225.00 GiB25.00 GiB50 / 0 / 0

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 5.85 GiB. The real file is 6.26 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
50
Attention heads
32
KV heads
8
Head dim
128
Hidden size
4096
Vocab
32,128
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Bielik-11B-v2.3-Instruct need?
Q4_K_M is exactly 6,724,050,496 bytes (6.26 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Bielik-11B-v2.3-Instruct's KV cache?
6.25 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of Bielik-11B-v2.3-Instruct should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.