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DeepScaleR-1.5B-Preview

agentica-org/DeepScaleR-1.5B-Preview

DeepScaleR-1.5B-Preview at Q4_K_M is exactly 1,117,322,144 bytes (1.04 GiB / 1.12 GB) — an effective 5.030 bits per weight, not the nominal 4. Its KV cache at 32K is 0.88 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
1.8B
Architecture
qwen2
28 layers
Context
131,072
native (config.json)
License
mit

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q2_K0.70 GiB752,881,5683.389bartowski
IQ3_XXS0.72 GiB769,071,0083.462bartowski
IQ3_XS0.77 GiB831,977,8883.745bartowski
Q3_K_S0.80 GiB861,223,3283.877bartowski
IQ3_M0.82 GiB876,942,7523.948bartowski
Q3_K_M0.86 GiB924,457,3764.162bartowski
Q3_K_L0.91 GiB980,441,5044.414bartowski
Q2_K_L0.91 GiB980,785,5684.415bartowski
IQ4_XS0.95 GiB1,019,712,4164.590bartowski
IQ4_NL0.99 GiB1,067,604,8964.806bartowski
Q4_01.00 GiB1,068,809,1204.811bartowski
Q4_K_S1.00 GiB1,071,586,2084.824bartowski
Q4_K_M1.04 GiB1,117,322,1445.030bartowski
Q4_11.08 GiB1,162,701,7285.234bartowski
Q5_K_S1.17 GiB1,259,174,8165.668bartowski
Q5_K_M1.20 GiB1,285,495,7125.787bartowski
Q4_K_L1.20 GiB1,290,529,1845.810bartowski
Q5_K_L1.33 GiB1,429,531,0406.435bartowski
Q6_K1.36 GiB1,464,180,1286.591bartowski
Q6_K_L1.47 GiB1,577,220,5127.100bartowski
Q8_01.76 GiB1,894,533,5368.529bartowski
F163.32 GiB3,560,417,69616.028bartowski
F326.63 GiB7,114,303,58432.027bartowski

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.11 GiB0.11 GiB28 / 0 / 0
8,1920.22 GiB0.22 GiB28 / 0 / 0
16,3840.44 GiB0.44 GiB28 / 0 / 0
32,7680.88 GiB0.88 GiB28 / 0 / 0
65,5361.75 GiB1.75 GiB28 / 0 / 0
131,0723.50 GiB3.50 GiB28 / 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 0.93 GiB. The real file is 1.04 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
28
Attention heads
12
KV heads
2
Head dim
128
Hidden size
1536
Vocab
151,936
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window
false

Questions people ask

How much VRAM does DeepScaleR-1.5B-Preview need?
Q4_K_M is exactly 1,117,322,144 bytes (1.04 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is DeepScaleR-1.5B-Preview's KV cache?
0.88 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 DeepScaleR-1.5B-Preview 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.