Deploy GPU ↗
HARDWARE FIT CHECK • Meta Llama 3.1 405B Instruct + NVIDIA GeForce RTX 5090 (32GB GDDR7)260.72 GB / 32 GB VRAM

Can I Run Meta Llama 3.1 405B Instruct on NVIDIA GeForce RTX 5090 (32GB GDDR7)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.1 405B Instruct (405B) on NVIDIA GeForce RTX 5090 (32GB GDDR7) (32 GB).

✕ OOM260.72 GB (815%)0 t/s

Model & Target Hardware

interactive
Frontier 405B Open Flagship for 8x H100 or 512GB Mac Studio Clusters
GPU Topology / Quantity:Single GPU / Unified SoC
Meta Llama 3.1 405B Instruct on NVIDIA GeForce RTX 5090 (32GB GDDR7)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
260.72 GB
815% of hardware capacity (32 GB)
Est. Decode Speed
0 tok/s
Dense model @ 1792 GB/s
Available Headroom
-228.72 GB
Needs +228.72 GB more VRAM
Memory Allocation & Headroom
260.72 GB allocated of 32 GB capacity
815% utilized
Weights: 243 GB
KV: (32K): 8.46 GB
Overhead: 9.26 GB
Deficit: -228.72 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5090 (32GB GDDR7) (1792 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama3.1:405b

Local Hardware Configurations & Pricing

⚠️ Out of VRAM: High-Capacity Hardware Upgrades
High-bandwidth VRAM specs and current market prices
Consumer GPU
32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP
$1,999Amazon Prime →
Multi-GPU Rig↑ Upgrade
48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP
$1,550Amazon Prime →
Apple Silicon↑ Upgrade
112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP
$3,499Amazon Prime →

⚠️ Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Meta Llama 3.1 405B Instruct @ 260.72 GB VRAM)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)
Launch 1-Click GPU Pod →
Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)
Rent Spot GPU →
Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)
Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw810 GB8.46 GB847.56 GB (2649%)0 t/sOOM
Q8_08.5 bpw429.3 GB8.46 GB453.54 GB (1417%)0 t/sOOM
Q6_K6.56 bpw332.1 GB8.46 GB352.93 GB (1103%)0 t/sOOM
Q5_K_M5.68 bpw287.55 GB8.46 GB306.82 GB (959%)0 t/sOOM
Q4_K_M4.8 bpw243 GB8.46 GB260.72 GB (815%)0 t/sOOM
FP44.25 bpw214.65 GB8.46 GB231.37 GB (723%)0 t/sOOM
IQ3_XS3.3 bpw166.05 GB8.46 GB181.07 GB (566%)0 t/sOOM

Export Full Architecture & Infrastructure Spec

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FAQ & Reference

Frequently Asked Questions: Meta Llama 3.1 405B Instruct on NVIDIA GeForce RTX 5090 (32GB GDDR7)

How much VRAM does Meta Llama 3.1 405B Instruct need on NVIDIA GeForce RTX 5090 (32GB GDDR7)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 3.1 405B Instruct requires 260.72 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Meta Llama 3.1 405B Instruct achieve on NVIDIA GeForce RTX 5090 (32GB GDDR7)?

Based on NVIDIA GeForce RTX 5090 (32GB GDDR7)'s 1792 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.