MODEL ARCHITECTURE SPEC • META AI70.6B Dense
Meta Llama 3.3 70B Instruct VRAM & inference speed
70.6B Total ParamsDense Architecture80 Layers • 8 KV Heads32K Default Ctx (Max 128K)Ollama: llama3.3:70b
Accurate memory requirements, KV cache footprint, and tokens/sec generation benchmarks for running Meta Llama 3.3 70B Instruct across local GPUs, Apple Silicon Macs, and cloud instances.
⚡ Fits in VRAM (Tight Headroom — Use Q8/Q4 KV Cache)49.96 GB (93%)~4 t/s
Meta Llama 3.3 70B Instruct on Apple Mac Mini M4 Pro (64GB Unified Memory)
⚡ Tight Fit — Runs 100% on GPU (Low Headroom)
⚡ TIGHT FIT
Required VRAM
49.96 GB
93% of hardware capacity (54 GB)Est. Decode Speed
~4 tok/s
Dense model @ 273 GB/sAvailable Headroom
+4.04 GB
Safe for OS display & full contextMemory Allocation & Headroom
49.96 GB allocated of 54 GB capacity
93% utilized
Weights: 42.36 GB
KV: (32K): 5.37 GB
Overhead: 2.23 GB
Free Headroom: +4.04 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~4 tok/s) is memory-bandwidth bound at batch size = 1 on Apple Mac Mini M4 Pro (64GB Unified Memory) (273 GB/s). Prompt prefill / TTFT (~58 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 llama3.3:70bCloud Instance Options for Meta Llama 3.3 70B 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 | 141.2 GB | 5.37 GB | 152.26 GB (282%) | 0 t/s | OOM |
| Q8_08.5 bpw | 74.84 GB | 5.37 GB | 83.58 GB (155%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 57.89 GB | 5.37 GB | 66.04 GB (122%) | ~1 t/s | PARTIAL |
| Q5_K_M5.68 bpw | 50.13 GB | 5.37 GB | 58 GB (107%) | ~1 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 42.36 GB | 5.37 GB | 49.96 GB (93%) | ~4 t/s | FITS |
| FP44.25 bpw | 37.42 GB | 5.37 GB | 44.85 GB (83%) | ~4 t/s | FITS |
| IQ3_XS3.3 bpw | 28.95 GB | 5.37 GB | 36.08 GB (67%) | ~6 t/s | FITS |
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Hardware Compatibility Matrix for Meta Llama 3.3 70B 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 • 1792 GB/s | 32 GB | 49.96 GB | -17.96 GB | 0 (OOM) | ✕ OOM | Test Fit → |
| NVIDIA GeForce RTX 4090 (24GB GDDR6X) Consumer GPU • 1008 GB/s | 24 GB | 49.96 GB | -25.96 GB | 0 (OOM) | ✕ OOM | Test Fit → |
| 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) Multi-GPU Rig • 936 GB/s | 48 GB | 49.96 GB | -1.96 GB | ~3 tok/s | ⚡ PARTIAL | Test Fit → |
| NVIDIA GeForce RTX 5080 (16GB GDDR7) Consumer GPU • 960 GB/s | 16 GB | 49.96 GB | -33.96 GB | 0 (OOM) | ✕ OOM | Test Fit → |
| NVIDIA RTX 4070 Ti Super (16GB) Consumer GPU • 672 GB/s | 16 GB | 49.96 GB | -33.96 GB | 0 (OOM) | ✕ OOM | Test Fit → |
| NVIDIA GeForce RTX 3060 (12GB) Consumer GPU • 360 GB/s | 12 GB | 49.96 GB | -37.96 GB | 0 (OOM) | ✕ OOM | Test Fit → |
| Apple Mac Mini M4 Pro (64GB Unified Memory) Apple Silicon • 273 GB/s | 54 GB | 49.96 GB | +4.04 GB | ~4 tok/s | ⚡ TIGHT FIT | Test Fit → |
| Apple Mac Studio M4 Max (128GB Unified Memory) Apple Silicon • 546 GB/s | 112 GB | 49.96 GB | +62.04 GB | ~8 tok/s | ✓ FITS | Test Fit → |
| Apple Mac Studio M5 Ultra (256GB Unified Memory) Apple Silicon • 890 GB/s | 236 GB | 49.96 GB | +186.04 GB | ~13 tok/s | ✓ FITS | Test Fit → |
| AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) Unified APU / Mini-PC • 256 GB/s | 110 GB | 49.96 GB | +60.04 GB | ~3 tok/s | ✓ FITS | Test Fit → |
| NVIDIA DGX Spark (128GB Grace-Blackwell Unified) AI Appliance • 273 GB/s | 118 GB | 49.96 GB | +68.04 GB | ~4 tok/s | ✓ FITS | Test Fit → |
| NVIDIA H100 SXM (80GB HBM3 Cloud GPU) Datacenter Cloud GPU • 3350 GB/s | 80 GB | 49.96 GB | +30.04 GB | ~56 tok/s | ✓ FITS | Test Fit → |