HARDWARE FIT CHECK โข Meta Llama 3.3 70B Instruct + NVIDIA GeForce RTX 4090 (24GB GDDR6X)49.96 GB / 24 GB VRAM
Can I Run Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
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 4090 (24GB GDDR6X) (24 GB).
โ Out of Memory (OOM) โ Exceeds Hardware VRAM49.96 GB (208%)0 t/s
Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
49.96 GB
208% of hardware capacity (24 GB)Est. Decode Speed
0 tok/s
Dense model @ 1008 GB/sAvailable Headroom
-25.96 GB
Needs +25.96 GB more VRAMMemory Allocation & Headroom
49.96 GB allocated of 24 GB capacity
208% utilized
Weights: 42.36 GB
KV: (32K): 5.37 GB
Overhead: 2.23 GB
Deficit: -25.96 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 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 llama3.3:70bโ ๏ธ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin 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) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 141.2 GB | 5.37 GB | 152.26 GB (634%) | 0 t/s | OOM |
| Q8_08.5 bpw | 74.84 GB | 5.37 GB | 83.58 GB (348%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 57.89 GB | 5.37 GB | 66.04 GB (275%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 50.13 GB | 5.37 GB | 58 GB (242%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 42.36 GB | 5.37 GB | 49.96 GB (208%) | 0 t/s | OOM |
| FP44.25 bpw | 37.42 GB | 5.37 GB | 44.85 GB (187%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 28.95 GB | 5.37 GB | 36.08 GB (150%) | 0 t/s | OOM |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Multi-GPU Rig48 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 โ
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8Bโ14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime โ
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