AI
LocalAI Calc
VRAM + cost per token
Meta · Dense · GQA

Llama 3.1 8B Instruct: VRAM by quantization and context

8.03B parameters, 32 layers, 8 KV heads. Here is what that means in gigabytes.

8.03B parameters 4.8 GB at Q4_K_M 32 layers 8 KV heads
Architecture
Parameters8.03BVendor model card
Layers32config.json
Hidden size4096config.json
Attention heads32config.json
KV heads8config.json (GQA)
Head dim128config.json
Max context131072 tokensconfig.json

Source: Meta Llama 3 architecture (published); repo gated

VRAM by quantization
Total includes 8K of KV cache and 1.5 GB runtime overhead.
QuantizationWeightsKV @ 8KTotal neededRuns on
FP16 / BF16
2.0 B/param
16.1 GB1.1 GB18.6 GBMac Studio M4 Max 128GB
INT8 / Q8_0
1.06 B/param
8.5 GB1.1 GB11.1 GBMac Studio M4 Max 128GB
Q4_K_M (4-bit)
0.6 B/param
4.8 GB1.1 GB7.4 GBMac Studio M4 Max 128GB
Q5_K_M (5-bit)
0.72 B/param
5.8 GB1.1 GB8.4 GBMac Studio M4 Max 128GB
KV cache by context length
Q4_K_M weights plus 1.5 GB overhead. KV cache at FP16.
ContextKV cacheTotal neededRuns on
4k0.5 GB6.9 GBMac Studio M4 Max 128GB
8k1.1 GB7.4 GBMac Studio M4 Max 128GB
16k2.1 GB8.5 GBMac Studio M4 Max 128GB
32k4.3 GB10.6 GBMac Studio M4 Max 128GB
64k8.6 GB14.9 GBMac Studio M4 Max 128GB
128k17.2 GB23.5 GBMac Studio M4 Max 128GB

Verdict

At Q4_K_M the weights alone are about 4.8 GB. With 8K of context and 1.5 GB of runtime overhead it lands near 7.4 GB. It fits: RTX 4090, RTX 3090, RTX 5090, Mac Studio M4 Max 128GB.

Back to calculator →  ·  Open the TCO calculator →  ·  Why 70B won't fit in 24 GB →

All throughput figures are estimates derived from memory bandwidth and published measurements. VRAM figures are computed from model architecture and quantization assumptions. Verify against your own runtime before buying hardware.