Groq · text

Llama-3-Groq-8B-Tool-Use

Groq/Llama-3-Groq-8B-Tool-Use

Llama-3-Groq-8B-Tool-Use at Q4_K_M is exactly 4,920,768,192 bytes (4.58 GiB / 4.92 GB) — an effective 4.902 bits per weight, not the nominal 4. Its KV cache at 32K is 4.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
8.0B
Architecture
llama
32 layers
Context
8,192
native (config.json)
License
llama3

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
IQ2_M2.75 GiB2,948,308,5442.937—bartowski
Q2_K2.96 GiB3,179,159,6163.167—second-state
Q2_K2.96 GiB3,179,159,8723.167—MaziyarPanahi
Q2_K2.96 GiB3,179,159,8723.167—bartowski
IQ3_XS3.28 GiB3,518,778,1123.506—bartowski
Q3_K_S3.41 GiB3,664,529,9203.651—second-state
Q3_K_S3.41 GiB3,664,530,1763.651—bartowski
Q3_K_S3.41 GiB3,664,530,1763.651—MaziyarPanahi
Q2_K_L3.44 GiB3,692,207,8723.678—bartowski
IQ3_M3.52 GiB3,784,854,2723.771—bartowski
Q3_K_M3.74 GiB4,018,948,6084.004—second-state
Q3_K_M3.74 GiB4,018,948,8644.004—MaziyarPanahi
Q3_K_M3.74 GiB4,018,948,8644.004—bartowski
Q3_K_L4.03 GiB4,321,987,0724.306—second-state
Q3_K_L4.03 GiB4,321,987,3284.306—bartowski
Q3_K_L4.03 GiB4,321,987,3284.306—MaziyarPanahi
IQ4_XS4.14 GiB4,447,696,0644.431—bartowski
Q4_04.34 GiB4,661,245,6324.644—second-state
Q4_K_S4.37 GiB4,692,702,9124.675—second-state
Q4_K_S4.37 GiB4,692,703,1684.675—bartowski
Q4_K_S4.37 GiB4,692,703,1684.675—MaziyarPanahi
Q4_K_M4.58 GiB4,920,768,1924.902—second-state
Q4_K_M4.58 GiB4,920,768,4484.902—MaziyarPanahi
Q4_K_M4.58 GiB4,920,768,4484.902—bartowski
Q4_K_L4.95 GiB5,310,684,9285.291—bartowski
Q5_05.21 GiB5,599,331,0085.578—second-state
Q5_K_S5.21 GiB5,599,331,0085.578—second-state
Q5_K_S5.21 GiB5,599,331,2645.578—MaziyarPanahi
Q5_K_S5.21 GiB5,599,331,2645.578—bartowski
Q5_K_M5.34 GiB5,733,024,4485.711—second-state
Q5_K_M5.34 GiB5,733,024,7045.711—MaziyarPanahi
Q5_K_M5.34 GiB5,733,024,7045.711—bartowski
Q5_K_L5.64 GiB6,057,271,0406.034—bartowski
Q6_K6.14 GiB6,596,046,7206.571—second-state
Q6_K6.14 GiB6,596,046,9766.571—MaziyarPanahi
Q6_K6.14 GiB6,596,046,9766.571—bartowski
Q6_K_L6.38 GiB6,850,518,7846.825—bartowski
Q8_07.95 GiB8,540,823,0408.509—second-state
Q8_07.95 GiB8,540,823,2968.509—bartowski
Q8_07.95 GiB8,540,823,2968.509—MaziyarPanahi

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.50 GiB0.50 GiB—32 / 0 / 0
8,1921.00 GiB1.00 GiB—32 / 0 / 0
16,3842.00 GiB2.00 GiB—32 / 0 / 0
32,7684.00 GiB4.00 GiB—32 / 0 / 0
65,5368.00 GiB8.00 GiB—32 / 0 / 0
131,07216.00 GiB16.00 GiB—32 / 0 / 0

Compare with

same modality, comparable size
vs embeddinggemma-300mvs gemma-4-12B-it-qat-q4_0-unquantizedvs Llama-3.2-1B-Instructvs llama-3-youko-8bvs Qwen3-8Bvs Qwen3-VL-8B-Instruct-abliterated-v1All 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 32GBApple M5 Max 36GBApple M5 Max 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 4.21 GiB. The real file is 4.58 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
128,262
Sliding window
none
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does Llama-3-Groq-8B-Tool-Use need?
Q4_K_M is exactly 4,920,768,192 bytes (4.58 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Llama-3-Groq-8B-Tool-Use's KV cache?
4.00 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 Llama-3-Groq-8B-Tool-Use 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.