Llama 3.1 8b Instruct - Memory Usage More than Reported

Llama 3.1 8B Instruct requires 32 GB of memory at 128K context due to a 16 GB KV cache, not just the 15 GB of weights, contradicting earlier reports that it fits in 24 GB. Users can reduce memory by quantizing the cache, capping context, or lowering weight precision.

“Memory Usage More than Reported” Late to this thread but it keeps being the 1 surprise, so for future readers — the missing 16 GB is the KV cache , and you can compute it from config.json alone: KV bytes = 2 K+V × layers × kv heads × head dim × context × bytes/elem Llama-3.1-8B: 2 × 32 × 8 × 128 × 131072 × 2 fp16 = 16 GB 128 KB per token So at the advertised 128K context: 15 GB of weights + 16 GB of cache + scratch ≈ 32 GB — it was never going to fit in 24 GB, and it’s not the model’s “fault”: it’s the context. Three levers, cheapest first: quantize the cache q8 0 halves it , cap the context ~66K fits in 24 GB with fp16 weights , or drop weight precision. I packaged this arithmetic plus the “which side is the problem” verdict and the max-context solver into a free browser tool — geometry fetched from the model card, nothing downloaded: TAF Agent — Test ANY Transformer LLM in Your Browser https://karlesmarin.github.io/tafagent/?demo=fitcheck