HARDWARE FIT CHECK • Meta Llama 3.1 8B Instruct + NVIDIA GeForce RTX 3060 (12GB)7.87 GB / 12 GB VRAM
Can I Run Meta Llama 3.1 8B Instruct on NVIDIA GeForce RTX 3060 (12GB)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.1 8B Instruct (8B) on NVIDIA GeForce RTX 3060 (12GB) (12 GB).
✅ Runs 100% in GPU VRAM (Optimal Headroom)7.87 GB (66%)~44 t/s
Meta Llama 3.1 8B Instruct on NVIDIA GeForce RTX 3060 (12GB)
✓ Fits Comfortably in VRAM
✓ FITS IN VRAM
Required VRAM
7.87 GB
66% of hardware capacity (12 GB)Est. Decode Speed
~44 tok/s
Dense model @ 360 GB/sAvailable Headroom
+4.13 GB
Safe for OS display & full contextMemory Allocation & Headroom
7.87 GB allocated of 12 GB capacity
66% utilized
Weights: 4.8 GB
KV: (32K): 2.15 GB
Overhead: 0.92 GB
Free Headroom: +4.13 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~44 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3060 (12GB) (360 GB/s). Prompt prefill / TTFT (~638 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 llama3.1:8bCloud Instance Options for Meta Llama 3.1 8B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 16 GB | 2.15 GB | 19.46 GB (162%) | 0 t/s | OOM |
| Q8_08.5 bpw | 8.48 GB | 2.15 GB | 11.68 GB (97%) | ~26 t/s | FITS |
| Q6_K6.56 bpw | 6.56 GB | 2.15 GB | 9.69 GB (81%) | ~33 t/s | FITS |
| Q5_K_M5.68 bpw | 5.68 GB | 2.15 GB | 8.78 GB (73%) | ~38 t/s | FITS |
| Q4_K_M4.8 bpw | 4.8 GB | 2.15 GB | 7.87 GB (66%) | ~44 t/s | FITS |
| FP44.25 bpw | 4.24 GB | 2.15 GB | 7.29 GB (61%) | ~49 t/s | FITS |
| IQ3_XS3.3 bpw | 3.28 GB | 2.15 GB | 6.29 GB (52%) | ~61 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU12 GB VRAM
NVIDIA GeForce RTX 3060 (12GB)
Entry-level budget card for 7B–14B Q4 models
360 GB/s170W TDP
$289Amazon Prime →
Consumer GPU32 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 →
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime →
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 →
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