MODEL ARCHITECTURE SPEC โข META AI8B Dense
Meta Llama 3.1 8B Instruct VRAM & inference speed
8B Total ParamsDense Architecture32 Layers โข 8 KV Heads16K Default Ctx (Max 128K)Ollama: llama3.1:8b
Accurate memory requirements, KV cache footprint, and tokens/sec generation benchmarks for running Meta Llama 3.1 8B Instruct across local GPUs, Apple Silicon Macs, and cloud instances.
โ
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)
โ Fits Comfortably in VRAM
โ 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
โก 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) 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 | 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 |
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Hardware Compatibility Matrix for Meta Llama 3.1 8B Instruct
Standard Q4_K_M precision with 32K context window and Q8_0 KV cache
| Hardware System | VRAM Envelope | Required VRAM | Headroom | Est. Decode Speed | Compatibility | Test Fit |
|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 (32GB GDDR7) Consumer GPU โข 1792 GB/s | 32 GB | 7.87 GB | +24.13 GB | ~228 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 4090 (24GB GDDR6X) Consumer GPU โข 1008 GB/s | 24 GB | 7.87 GB | +16.13 GB | ~128 tok/s | โ FITS | Test Fit โ |
| 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) Multi-GPU Rig โข 936 GB/s | 48 GB | 7.87 GB | +40.13 GB | ~109 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 5080 (16GB GDDR7) Consumer GPU โข 960 GB/s | 16 GB | 7.87 GB | +8.13 GB | ~122 tok/s | โ FITS | Test Fit โ |
| NVIDIA RTX 4070 Ti Super (16GB) Consumer GPU โข 672 GB/s | 16 GB | 7.87 GB | +8.13 GB | ~84 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 3060 (12GB) Consumer GPU โข 360 GB/s | 12 GB | 7.87 GB | +4.13 GB | ~44 tok/s | โ FITS | Test Fit โ |
| Apple Mac Mini M4 Pro (64GB Unified Memory) Apple Silicon โข 273 GB/s | 54 GB | 7.87 GB | +46.13 GB | ~32 tok/s | โ FITS | Test Fit โ |
| Apple Mac Studio M4 Max (128GB Unified Memory) Apple Silicon โข 546 GB/s | 112 GB | 7.87 GB | +104.13 GB | ~65 tok/s | โ FITS | Test Fit โ |
| Apple Mac Studio M5 Ultra (256GB Unified Memory) Apple Silicon โข 890 GB/s | 236 GB | 7.87 GB | +228.13 GB | ~107 tok/s | โ FITS | Test Fit โ |
| AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) Unified APU / Mini-PC โข 256 GB/s | 110 GB | 7.87 GB | +102.13 GB | ~27 tok/s | โ FITS | Test Fit โ |
| NVIDIA DGX Spark (128GB Grace-Blackwell Unified) AI Appliance โข 273 GB/s | 118 GB | 7.87 GB | +110.13 GB | ~34 tok/s | โ FITS | Test Fit โ |
| NVIDIA H100 SXM (80GB HBM3 Cloud GPU) Datacenter Cloud GPU โข 3350 GB/s | 80 GB | 7.87 GB | +72.13 GB | ~452 tok/s | โ FITS | Test Fit โ |