openchat · text

openchat-3.5-1210

openchat/openchat-3.5-1210

openchat-3.5-1210 at Q4_K_M is exactly 4,368,450,656 bytes (4.07 GiB / 4.37 GB) — an effective 4.826 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
7.2B
Architecture
llama
32 layers
Context
8,192
native (config.json)
License
apache-2.0

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
Q2_K2.87 GiB3,083,107,5523.406—TheBloke
Q3_K_S2.95 GiB3,164,577,8243.496—TheBloke
Q3_K_M3.28 GiB3,518,996,5123.888—TheBloke
Q3_K_L3.56 GiB3,822,034,9764.222—TheBloke
Q4_03.83 GiB4,108,928,0964.539—TheBloke
Q4_K_S3.86 GiB4,140,385,3764.574—TheBloke
Q4_K_M4.07 GiB4,368,450,6564.826—TheBloke
Q5_K_S4.65 GiB4,997,728,3525.521—TheBloke
Q5_04.65 GiB4,997,728,3525.521—TheBloke
Q5_K_M4.78 GiB5,131,421,7925.669—TheBloke
Q6_K5.53 GiB5,942,078,6246.564—TheBloke
Q8_07.17 GiB7,695,875,1048.502—TheBloke

KV cache by context

unresolved

This model declares a 4,096-token sliding window, but we could not establish which layers use it. Its architecture publishes the layout as a per-layer array inside the model file rather than as a period in config.json, and we have not yet ingested that array.

A flat context × layers × heads figure would be substantially too high, so we are not showing one. This is tracked as a known gap rather than filled with a guess.

Compare with

same modality, comparable size
vs gemma-4-12B-it-qat-q4_0-unquantizedvs gemma-4-E4B-itvs Qwen3-30B-A3B-Thinking-2507vs Qwen3-4Bvs Qwen3-8Bvs Llama-3.2-1B-InstructAll 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 3.79 GiB. The real file is 4.07 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
32
Attention heads
32
KV heads
8
Head dim
128
Hidden size
4096
Vocab
32,002
Sliding window
4096
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does openchat-3.5-1210 need?
Q4_K_M is exactly 4,368,450,656 bytes (4.07 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of openchat-3.5-1210 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.