Llama 3.1 405B Instruct — VRAM Requirements, Quantization Matrix & GPU Guide
Llama 3.1 405B Instruct (Meta Llama, Llama 3.1 Community) is a Dense Transformer architecture with 405B resident parameters (405B active per token), 126 layers, and Grouped-Query Attention (GQA) supporting up to 131,072 context tokens.
Interactive Llama 3.1 405B Instruct Memory & Context Simulator
Precision & Quantization VRAM Matrix (8K / 32K / 128K Context)
| Precision / Format | Effective bpw | Weights Only | Total @ 8K ctx | Total @ 32K ctx | Total @ 128K ctx | Recommended Tier (@ 32K) |
|---|---|---|---|---|---|---|
| FP16 / BF16 | 16.00 bpw | 754.37 GB | 771.17 GB | 782.99 GB | 830.24 GB | 8× NVIDIA H100 80GB |
| FP8 | 8.50 bpw | 400.76 GB | 412.26 GB | 424.07 GB | 471.32 GB | 8× NVIDIA H100 80GB |
| Q8_0 | 8.50 bpw | 400.76 GB | 412.26 GB | 424.07 GB | 471.32 GB | 8× NVIDIA H100 80GB |
| Q6_K | 6.56 bpw | 309.29 GB | 319.42 GB | 331.23 GB | 378.48 GB | Mac Studio 512GB |
| Q5_K_M | 5.69 bpw | 268.27 GB | 277.78 GB | 289.60 GB | 336.85 GB | Mac Studio 512GB |
| Q4_K_M | 4.85 bpw | 228.67 GB | 237.59 GB | 249.40 GB | 296.65 GB | Mac Studio 512GB |
| AWQ INT4 | 4.50 bpw | 212.17 GB | 220.84 GB | 232.65 GB | 279.90 GB | Mac Studio 512GB |
| IQ4_XS | 4.25 bpw | 200.38 GB | 208.87 GB | 220.69 GB | 267.94 GB | Mac Studio 512GB |
Hardware Compatibility & Decode Speed (Llama 3.1 405B Instruct @ Q4_K_M, 32K Context)
| Hardware Tier | Memory & Bandwidth | Required (32K) | Fit Verdict | Est. Decode Speed |
|---|---|---|---|---|
| RTX 3090 24GB | 24 GB · 936 GB/s | 253.17 GB (1055%) | Out of Memory (OOM) | — |
| RTX 4090 24GB | 24 GB · 1008 GB/s | 253.17 GB (1055%) | Out of Memory (OOM) | — |
| RTX 5090 32GB | 32 GB · 1792 GB/s | 253.17 GB (791%) | Out of Memory (OOM) | — |
| Apple M-Max 64GB | 64 GB · 546 GB/s | 249.40 GB (390%) | Out of Memory (OOM) | — |
| Mac Studio 128GB | 128 GB · 546 GB/s | 249.40 GB (195%) | Out of Memory (OOM) | — |
| Mac Studio 192GB | 192 GB · 800 GB/s | 249.40 GB (130%) | Out of Memory (OOM) | — |
| NVIDIA A100 80GB | 80 GB · 2039 GB/s | 253.17 GB (317%) | Out of Memory (OOM) | — |
| NVIDIA H100 80GB | 80 GB · 3350 GB/s | 253.17 GB (317%) | Out of Memory (OOM) | — |
Verified Transformer Architecture Parameters
- Checkpoint ID
- meta-llama/Llama-3.1-405B-Instruct
- Ollama Tag
- llama3.1:405b
- License / Release
- Llama 3.1 Community · 2024-Q3
- Topology & Attention
- Dense Transformer
- Attention Mechanism
- Grouped-Query Attention (GQA)
- Total Params (B)
- 405B
- Active Params / Token (B)
- 405B
- Hidden Layers / Hidden Size
- 126 / 16384
- Query Heads / KV Heads
- 128Q / 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 405B Instruct
vllm serve meta-llama/Llama-3.1-405B-Instruct \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 8 \
--gpu-memory-utilization 0.80 \
--kv-cache-dtype fp8 \
--enable-prefix-caching \
--port 8000# Optional macOS unified-memory ceiling override before launching:
# sudo sysctl iogpu.wired_limit_mb=461373
# Save as Modelfile, then run: ollama create llama-3-1-405b-custom -f Modelfile
FROM llama3.1:405b
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 serveCompare Sibling & Similar-Scale Open-Weight Models
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