HARDWARE FIT CHECK • Meta Llama 3.1 8B Instruct + NVIDIA GeForce RTX 4090 (24GB GDDR6X)7.87 GB / 24 GB VRAM
Can I Run Meta Llama 3.1 8B 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 3.1 8B Instruct (8B) on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (24 GB).
✅ Runs 100% in GPU VRAM (Optimal Headroom)7.87 GB (33%)~128 t/s
Meta Llama 3.1 8B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
✓ Fits Comfortably in VRAM
✓ FITS IN VRAM
Required VRAM
7.87 GB
33% of hardware capacity (24 GB)Est. Decode Speed
~128 tok/s
Dense model @ 1008 GB/sAvailable Headroom
+16.13 GB
Safe for OS display & full contextMemory Allocation & Headroom
7.87 GB allocated of 24 GB capacity
33% utilized
Weights: 4.8 GB
KV: (32K): 2.15 GB
Overhead: 0.92 GB
Free Headroom: +16.13 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~128 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 GB/s). Prompt prefill / TTFT (~1,856 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 (81%) | ~41 t/s | FITS |
| Q8_08.5 bpw | 8.48 GB | 2.15 GB | 11.68 GB (49%) | ~76 t/s | FITS |
| Q6_K6.56 bpw | 6.56 GB | 2.15 GB | 9.69 GB (40%) | ~97 t/s | FITS |
| Q5_K_M5.68 bpw | 5.68 GB | 2.15 GB | 8.78 GB (37%) | ~110 t/s | FITS |
| Q4_K_M4.8 bpw | 4.8 GB | 2.15 GB | 7.87 GB (33%) | ~128 t/s | FITS |
| FP44.25 bpw | 4.24 GB | 2.15 GB | 7.29 GB (30%) | ~143 t/s | FITS |
| IQ3_XS3.3 bpw | 3.28 GB | 2.15 GB | 6.29 GB (26%) | ~180 t/s | FITS |
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 14B–32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon 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 →
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 →
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8B–14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime →
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