Stactora - AI Hardware Index
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HEAD-TO-HEAD SPECIFICATION & VRAM MATRIX

Llama 3.3 70B Instruct vs Mistral Small 3

Side-by-side technical trade-offs across real GGUF quantization VRAM math (24GB target rig), context retention, commercial license terms, and brutal truth verdicts.

COMPARED ENGINES2 / 3
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Llama 3.3 70B Instruct01Agents
Meta

Llama 3.3 70B Instruct

Meta's reliable 70B dense model for serious reasoning, long-form work, and agentic pipelines that need dependable instruction following.

SELF-HOST
ENTRY COSTFree weights; multi-GPU or high-memory host required
87/100INDEX SIGNAL
Engine Page →Hugging Face ↗
Mistral Small 302Agents
Mistral AI

Mistral Small 3

A 24B Apache-2.0 model tuned for speed and local agentic loops, fitting in 16 GB at 4-bit for real-time workflows.

USE IT
ENTRY COSTFree weights; compute cost applies
86/100INDEX SIGNAL
Engine Page →Hugging Face ↗
SIDE-BY-SIDE TECHNICAL MATRIX

Where each engine wins: Llama 3.3 70B Instruct vs Mistral Small 3

2 engines · 13 criteria
CRITERIONLlama 3.3 70B InstructMetaMistral Small 3Mistral AI
•VERDICTSELF-HOSTUSE IT
•THE BRUTAL TRUTH70B capability at 40GB VRAM cost — only worth it if you already own two 24GB cards or a 48GB workstation. Skip on single-GPU rigs.The only sub-16GB model on this list that runs real-time agent loops. Trade: weaker reasoning than 27B+ models.
•CATEGORYAgentsAgents
•ARCHITECTURE & PARAMSLlama 3.3 70B InstructMistral Small 3 (24B)
•QUANTIZATION & MIN VRAMQ4_K_M: 40GB · Q5_K_M: 48GB · Q8_0: 75GB · FP16: 140GBQ4_K_M: 15GB · Q5_K_M: 18GB · Q8_0: 27GB · FP16: 48GB
•RECOMMENDED VRAM50 GB (with KV Cache)19 GB (with KV Cache)
•CONTEXT WINDOW128K tokens32K tokens
•LICENSE TERMSLlama 3.3 Community LicenseApache 2.0
•PRICING & ENTRY COSTFree weights · your hardwareFree weights · your hardware
•TRUE FOUNDATION ENGINE?✓ Foundation Engine✓ Foundation Engine
•STRENGTHSReliable instruction following and reasoning · Broad tooling support across runtimesFast enough for real-time agent loops · Apache 2.0 with no additional restrictions
•KNOWN GOTCHAS40 GB at Q4 rules out single consumer GPUs · Throughput drops on unified-memory devicesLess capable than 70B-class models on hard reasoning · Smaller context than some competitors
•CLOUD GPU DEPLOYDeploy on RunPod ($0.14/hr) ↗Deploy on RunPod ($0.14/hr) ↗
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VRAM Precision Footnote: Stactora benchmarks assume typical KV cache overhead at 8K context and modern runtime engines (Ollama, vLLM, ComfyUI). Actual memory usage may vary with longer context or parallel batching.