Llama 3.1 8B Instruct VRAM Requirements, GGUF Sizes & GPU Gui…

Interactive Llama 3.1 8B Instruct Memory & Context Simulator

Weight Quantization (bpw)

KV Cache Precision

Target GPU / Unified Memory Tier

Devices (TP)

Context Length (tokens) 32,768 tokens

Memory Breakdown & Bandwidth Ceiling

Fits Comfortably

Model Weights: 46.2% KV: 40.8% Overhead: 13%

Total Required Memory

9.81 GB

Per-Device Allocation: 9.81 GB

Model Weights

4.53 GB

KV Cache (FP16): 4.00 GB

Est. Single-Stream Decode

24.6 tok/s

Runtime & TP Overhead: 1.28 GB

Max Context Before OOM

49,153

tokens

Architecture KV compression saves 75.0% vs naive MHA FP16 (16.00 GB).

Precision & Quantization VRAM Matrix (8K / 32K / 128K Context)

Total GPU memory required across weight precisions and context lengths (batch = 1, FP16 KV cache)
Precision / Format	Effective bpw	Weights Only	Total @ 8K ctx	Total @ 32K ctx	Total @ 128K ctx	Recommended Tier (@ 32K)
FP16 / BF16	16.00 bpw	14.96 GB	17.55 GB	20.55 GB	32.55 GB	RTX 3090 24GB
FP8	8.50 bpw	7.95 GB	10.32 GB	13.32 GB	25.32 GB	RTX 4060 Ti 16GB
Q8_0	8.50 bpw	7.95 GB	10.32 GB	13.32 GB	25.32 GB	RTX 4060 Ti 16GB
Q6_K	6.56 bpw	6.13 GB	8.46 GB	11.46 GB	23.46 GB	RTX 3060 12GB
Q5_K_M	5.69 bpw	5.32 GB	7.62 GB	10.62 GB	22.62 GB	RTX 3060 12GB
Q4_K_M	4.85 bpw	4.53 GB	6.81 GB	9.81 GB	21.81 GB	RTX 3060 12GB
AWQ INT4	4.50 bpw	4.21 GB	6.47 GB	9.47 GB	21.47 GB	RTX 3060 12GB
IQ4_XS	4.25 bpw	3.97 GB	6.23 GB	9.23 GB	21.23 GB	RTX 3060 12GB

Hardware Compatibility & Decode Speed (Llama 3.1 8B Instruct @ Q4_K_M, 32K Context)

Fit status, memory utilization, and estimated decode throughput across flagship GPUs and Apple Silicon
Hardware Tier	Memory & Bandwidth	Required (32K)	Fit Verdict	Est. Decode Speed
RTX 3090 24GB	24 GB · 936 GB/s	9.81 GB (41%)	Fits Comfortably	63.9 tok/s
RTX 4090 24GB	24 GB · 1008 GB/s	9.81 GB (41%)	Fits Comfortably	68.8 tok/s
RTX 5090 32GB	32 GB · 1792 GB/s	9.81 GB (31%)	Fits Comfortably	122.3 tok/s
Apple M-Max 64GB	64 GB · 546 GB/s	9.40 GB (15%)	Fits Comfortably	35.1 tok/s
Mac Studio 128GB	128 GB · 546 GB/s	9.40 GB (7%)	Fits Comfortably	35.1 tok/s
Mac Studio 192GB	192 GB · 800 GB/s	9.40 GB (5%)	Fits Comfortably	51.4 tok/s
NVIDIA A100 80GB	80 GB · 2039 GB/s	9.81 GB (12%)	Fits Comfortably	147.4 tok/s
NVIDIA H100 80GB	80 GB · 3350 GB/s	9.81 GB (12%)	Fits Comfortably	242.2 tok/s

Verified Transformer Architecture Parameters

Checkpoint ID: meta-llama/Llama-3.1-8B-Instruct
Ollama Tag: llama3.1:8b
License / Release: Llama 3.1 Community · 2024-Q3
Topology & Attention: Dense Transformer
Attention Mechanism: Grouped-Query Attention (GQA)
Total Params (B): 8.03B
Active Params / Token (B): 8.03B
Hidden Layers / Hidden Size: 32 / 4096
Query Heads / KV Heads: 32Q / 8KV (d=128)
Vocab / Max Position: 128,256 / 131,072

Quantized formats store per-block scale factors and zero-points alongside packed integer weights. A 4-bit GGUF Q4_K_M checkpoint averages 4.85 effective bits per parameter (not 4.00), while Q8_0 averages 8.50 bpw. For Mixture-of-Experts models, all routed and shared experts remain resident in memory to prevent token-by-token host paging.

Standard Grouped-Query Attention allocates 2 × L × H_kv × d_head × S × B × bytes_kv across layers. DeepSeek Multi-head Latent Attention (MLA) compresses keys and values into L × (d_kv_lora + d_qk_rope) × S × B × bytes_kv, slashing long-context memory by over 90%. Hybrid sliding-window models cap local-attention layers at min(S, window_size) while scaling global layers linearly with S.

Quick-Start vLLM & Ollama Commands for Llama 3.1 8B Instruct

vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --tensor-parallel-size 1 \
  --max-model-len 32768 \
  --max-num-seqs 8 \
  --gpu-memory-utilization 0.95 \
  --kv-cache-dtype fp8 \
  --enable-prefix-caching \
  --port 8000

# Save as Modelfile, then run: ollama create llama-3-1-8b-custom -f Modelfile
FROM llama3.1:8b
PARAMETER num_ctx 32768
PARAMETER num_batch 512
PARAMETER flash_attn true

# Environment variables for ollama serve:
# OLLAMA_NUM_PARALLEL=8 OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve