DeepSeek-V3 671B (Local MoE) vs Llama 3.3 70B Instruct
Side-by-side technical trade-offs across real GGUF quantization VRAM math (24GB target rig), context retention, commercial license terms, and brutal truth verdicts.
The flagship 671B total / 37B active Mixture-of-Experts foundation model with Multi-head Latent Attention (MLA), matching closed frontier models under MIT license.
SELF-HOST
ENTRY COSTFree weights; workstation cluster or unified RAM required
Where each engine wins: DeepSeek-V3 671B (Local MoE) vs Llama 3.3 70B Instruct
2 engines · 13 criteria
CRITERION
DeepSeek-V3 671B (Local MoE)DeepSeek
Llama 3.3 70B InstructMeta
•VERDICT
SELF-HOST
SELF-HOST
•THE BRUTAL TRUTH
The biggest open-weight breakthrough in history. If you have 64GB-128GB unified memory or a dual-GPU node, it replaces your entire cloud API bill under MIT.
70B capability at 40GB VRAM cost — only worth it if you already own two 24GB cards or a 48GB workstation. Skip on single-GPU rigs.
Matches GPT-4o and Claude 3.5 Sonnet on standard reasoning benchmarks · MIT license grants total commercial and fine-tuning freedom · 37B active parameters keep token generation snappy once loaded
Reliable instruction following and reasoning · Broad tooling support across runtimes
•KNOWN GOTCHAS
671B total weights require extreme disk storage (160GB–350GB) · Demands 48GB+ VRAM or 128GB unified memory Mac to run locally
40 GB at Q4 rules out single consumer GPUs · Throughput drops on unified-memory devices
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.
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