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VRAM + cost per token
Meta · Dense · GQA

Llama 3.3 70B Instruct: VRAM by quantization and context

70.6B parameters, 80 layers, 8 KV heads. Here is what that means in gigabytes.

70.6B parameters 42.4 GB at Q4_K_M 80 layers 8 KV heads
Architecture
Parameters70.6BVendor model card
Layers80config.json
Hidden size8192config.json
Attention heads64config.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
141.2 GB2.7 GB145.4 GBNo
INT8 / Q8_0
1.06 B/param
74.8 GB2.7 GB79.0 GBMac Studio M4 Max 128GB
Q4_K_M (4-bit)
0.6 B/param
42.4 GB2.7 GB46.5 GBMac Studio M4 Max 128GB
Q5_K_M (5-bit)
0.72 B/param
50.8 GB2.7 GB55.0 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
4k1.3 GB45.2 GBMac Studio M4 Max 128GB
8k2.7 GB46.5 GBMac Studio M4 Max 128GB
16k5.4 GB49.2 GBMac Studio M4 Max 128GB
32k10.7 GB54.6 GBMac Studio M4 Max 128GB
64k21.5 GB65.3 GBMac Studio M4 Max 128GB
128k42.9 GB86.8 GBMac Studio M4 Max 128GB

Verdict

At Q4_K_M the weights alone are about 42.4 GB. With 8K of context and 1.5 GB of runtime overhead it lands near 46.5 GB. It fits: 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.