01-ai · text

Yi-1.5-34B

01-ai/Yi-1.5-34B

Yi-1.5-34B at Q4_K_M is exactly 20,658,711,104 bytes (19.24 GiB / 20.66 GB) — an effective 4.806 bits per weight, not the nominal 4. Its KV cache at 32K is 7.50 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
34.4B
Architecture
llama
60 layers
Context
4,096
native (config.json)
License
apache-2.0

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
IQ1_S6.98 GiB7,498,980,0961.744—bartowski
IQ1_M7.62 GiB8,176,786,1761.902—bartowski
IQ2_XXS8.67 GiB9,306,462,9762.165—bartowski
IQ2_XS9.60 GiB10,306,542,3362.398—bartowski
IQ2_S10.14 GiB10,891,021,0562.534—bartowski
IQ2_M10.98 GiB11,794,762,4962.744—bartowski
Q2_K11.94 GiB12,825,233,9842.984—mradermacher
Q2_K11.94 GiB12,825,234,1762.984—bartowski
IQ3_XXS12.42 GiB13,333,875,4563.102—bartowski
IQ3_XS13.26 GiB14,234,319,4243.311—mradermacher
IQ3_XS13.26 GiB14,234,319,6163.311—bartowski
Q3_K_S13.93 GiB14,960,294,4643.480—mradermacher
Q3_K_S13.93 GiB14,960,294,6563.480—bartowski
IQ3_S13.99 GiB15,018,785,3443.494—mradermacher
IQ3_S13.99 GiB15,018,785,5363.494—bartowski
IQ3_M14.50 GiB15,564,700,2243.621—mradermacher
IQ3_M14.50 GiB15,564,700,4163.621—bartowski
Q3_K_M15.51 GiB16,654,924,3523.874—mradermacher
Q3_K_M15.51 GiB16,654,924,5443.874—bartowski
Q3_K_L16.89 GiB18,139,445,8244.220—mradermacher
Q3_K_L16.89 GiB18,139,446,0164.220—bartowski
IQ4_XS17.21 GiB18,475,051,7764.298—bartowski
IQ4_XS17.36 GiB18,635,614,7844.335—mradermacher
IQ4_NL18.18 GiB19,521,579,7764.541—bartowski
Q4_K_S18.25 GiB19,598,649,9204.559—mradermacher
Q4_K_S18.25 GiB19,598,650,1124.559—bartowski
Q4_K_M19.24 GiB20,658,711,1044.806—mradermacher
Q4_K_M19.24 GiB20,658,711,2964.806—bartowski
Q5_K_S22.08 GiB23,707,691,5845.515—mradermacher
Q5_K_S22.08 GiB23,707,691,7765.515—bartowski
Q5_K_M22.65 GiB24,321,845,8245.658—mradermacher
Q5_K_M22.65 GiB24,321,846,0165.658—bartowski
Q6_K26.28 GiB28,213,926,4646.564—mradermacher
Q6_K26.28 GiB28,213,926,6566.564—bartowski
Q8_034.03 GiB36,542,282,3048.501—mradermacher
Q8_034.03 GiB36,542,282,4968.501—bartowski
F325 shards128.11 GiB137,557,179,45632.000—bartowski

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.94 GiB0.94 GiB—60 / 0 / 0
8,1921.88 GiB1.88 GiB—60 / 0 / 0
16,3843.75 GiB3.75 GiB—60 / 0 / 0
32,7687.50 GiB7.50 GiB—60 / 0 / 0
65,53615.00 GiB15.00 GiB—60 / 0 / 0
131,07230.00 GiB30.00 GiB—60 / 0 / 0

Compare with

same modality, comparable size
vs Qwen3-Coder-30B-A3B-Instructvs Qwen3.6-27Bvs Qwen3.8-27Bvs gemma-4-12B-it-qat-q4_0-unquantizedvs gemma-4-E4B-itvs Qwen3-30B-A3B-Thinking-2507All alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 18.02 GiB. The real file is 19.24 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
60
Attention heads
56
KV heads
8
Head dim
128
Hidden size
7168
Vocab
64,000
Sliding window
none
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does Yi-1.5-34B need?
Q4_K_M is exactly 20,658,711,104 bytes (19.24 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Yi-1.5-34B's KV cache?
7.50 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 Yi-1.5-34B 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.