HARDWARE FIT CHECK • Meta Llama 3.3 70B Instruct + NVIDIA GeForce RTX 5090 (32GB GDDR7)49.96 GB / 32 GB VRAM

Can I Run Meta Llama 3.3 70B 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 3.3 70B Instruct (70.6B) on NVIDIA GeForce RTX 5090 (32GB GDDR7) (32 GB).

❌ Out of Memory (OOM) — Exceeds Hardware VRAM49.96 GB (156%)0 t/s

Model & Target Hardware

interactive

Model70.6B Dense

Standard 70B Dense Enterprise Baseline for 48GB–64GB Rigs

Target Hardware32GB • 1792 GB/s

▼ Fine-Tune Precision (Q4_K_M, 32K ctx, Q8_0 KV)quant / context

Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 5090 (32GB GDDR7)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM

Required VRAM

49.96 GB

156% of hardware capacity (32 GB)

Est. Decode Speed

0 tok/s

Dense model @ 1792 GB/s

Available Headroom

-17.96 GB

Needs +17.96 GB more VRAM

Memory Allocation & Headroom

49.96 GB allocated of 32 GB capacity

156% utilized

Weights: 42.36 GB

KV: (32K): 5.37 GB

Overhead: 2.23 GB

Deficit: -17.96 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 llama3.3:70b

⚠️ 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 3.3 70B Instruct @ 49.96 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

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	141.2 GB	5.37 GB	152.26 GB (476%)	0 t/s	OOM
Q8_08.5 bpw	74.84 GB	5.37 GB	83.58 GB (261%)	0 t/s	OOM
Q6_K6.56 bpw	57.89 GB	5.37 GB	66.04 GB (206%)	0 t/s	OOM
Q5_K_M5.68 bpw	50.13 GB	5.37 GB	58 GB (181%)	0 t/s	OOM
Q4_K_M4.8 bpw	42.36 GB	5.37 GB	49.96 GB (156%)	0 t/s	OOM
FP44.25 bpw	37.42 GB	5.37 GB	44.85 GB (140%)	0 t/s	OOM
IQ3_XS3.3 bpw	28.95 GB	5.37 GB	36.08 GB (113%)	~9 t/s	PARTIAL