Llama 3.1 8B vs Llama 3.2 3B: Local VRAM, Speed & Benchmarks

Llama 3.1 8B and Llama 3.2 3B are Meta’s two most-downloaded small open-weight models, and they answer different hardware questions: the 8B is the quality ceiling that fits consumer GPUs, while the 3B is built specifically for constrained devices. Meta trained the 3B using logit distillation from the larger Llama 3.1 models, then published both models’ benchmark numbers in the same internal evaluation setup - which makes this one of the few head-to-head comparisons you can ground entirely in official data. Every score below comes from Meta’s published Llama 3.2 and Llama 3.1 model cards. No third-party leaderboard numbers or first-party runs are mixed in.

Quick Comparison Table

All spec rows and figures come from Meta’s Llama 3.1 and Llama 3.2 model cards; quantized file sizes are approximate standard GGUF figures described in the VRAM section below.

Introduction to Both Models

Llama 3.1 8B launched July 2024 as the smallest member of the Llama 3.1 collection (8B/70B/405B). Meta optimized it as the entry point of a frontier family: 128K context, tool calling, and strong multilingual support, with the largest ecosystem of fine-tunes, quantizations, and serving tools in the open-weights space by download count.

Llama 3.2 3B arrived two months later as part of a dedicated small-model collection (1B/3B) designed for edge and mobile deployment. Meta built it by distilling logits from Llama 3.1 8B and 70B during pretraining and pruning, with post-training quantization (SpinQuant and QAT+LoRA) produced specifically for ExecuTorch on ARM CPUs. On Meta’s own mobile measurements, the 3B decodes at 7.6 tokens/second in BF16 and about 19.7 tokens/second in SpinQuant form on a OnePlus 12.

Both share Grouped-Query Attention (GQA) and support the 8 officially supported languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai).

Performance Benchmarks

Meta published both models in one table using its internal evaluations library, so these figures are directly comparable (instruction-tuned bf16 versions, minus the distillation caveat below).

Three honest readings of that table:

1. The 8B leads on knowledge and long-context recall. Its MMLU, multi-needle and MGSM advantages are the largest gaps, reflecting more parameters rather than better training.
2. The 3B nearly ties on instruction following and tool calling. IFEval (77.4 vs 80.4) and BFCL v2 (67.0 vs 67.1) are effectively level - the distilled small model was clearly optimized for exactly these agentic behaviors. This is why the 3B works well for structured-output and function-calling apps.
3. One distillation caveat: because Llama 3.2 was trained with logit targets from the 3.1 family, Meta’s reported 3B numbers can read slightly optimistic relative to independently trained models of the same size. Cross-check any decision-critical benchmark against your own tasks.

Use Case Performance

Choose Llama 3.1 8B when the task is knowledge-heavy: document Q&A builds, RAG systems where the retriever is imperfect, multi-step reasoning, or any coding work beyond small scripts. The 8B’s larger gap on MMLU and MGSM shows up precisely when prompts reach outside tightly-scoped patterns. Its downside is the footprint: Q4 weights of roughly 4.9 GB mean nothing larger than an 8 GB GPU is comfortable once context grows.

Choose Llama 3.2 3B when the deployment target is small: Android phones (the ExecuTorch SpinQuant build runs at roughly a quarter of the BF16 memory), Raspberry Pi-class boards, guaranteed-fit containers on shared cloud GPUs, or high-volume servers where every gigabyte multiplies across replicas. Its IFEval/BFCL parity also makes it a legitimate choice for agent pipelines, chat UX, summarization and classification at the edge - tasks where instructions matter more than world knowledge.

On raw speed with a fixed VRAM budget, the 3B decodes notably faster per generated token because it moves roughly 2.5x fewer parameter bytes per step; expect roughly 2-2.5x higher tokens/second on the same GPU at the same quantization, plus faster cold-start. The 8B stays the pick when answer quality per prompt is the binding constraint.

Quantization and VRAM math

Local memory budgeting is a straightforward calculation: keep the weight footprint, add runtime overhead, then size the KV cache for your context length. Approximate GGUF weight sizes (llama.cpp / Ollama conventions):

On top of weights, budget for:

• KV cache, which grows with context length and depends on the number of layers and GQA head configuration. At 8K context this adds well under a gigabyte for either model; at 32K-128K context it can add multiple gigabytes on the 8B, which is exactly where 8 GB cards run out of room.
• Runtime buffers and metadata, typically a few hundred MB in llama.cpp or Ollama.

Practical fits: an 8 GB GPU runs the 8B at Q4_K_M with a modest context (8-16K), or the 3B at Q8 with huge context. A 4 GB GPU realistically runs only the 3B at Q4_K_M. A phone (8-12 GB shared RAM) runs the 3B at 4-bit (Meta’s official quantized ExecuTorch builds target an 8K context for this reason), while the 8B needs 12-16 GB.

Llama 3.1 8B vs Llama 3.2 3B – Which Meta Model Is Better

For most developers with a real GPU, Llama 3.1 8B remains the better default: it scores higher on 12 of the 14 shared benchmarks in Meta’s table, has the deeper ecosystem of fine-tunes, and its longer knowledge cutoff (April vs December 2023) matters more than you’d expect when answering questions about recent software. Pick the 3B when hardware, latency-per-token, or price-per-deployment is the constraint - it was built for exactly that case and measurably holds the line on instruction following and tool calling.

If you are comparing Meta’s small models against other providers at the same size class, see our broader head-to-head Llama 3.1 8B vs Qwen 2.5 7B benchmark comparison, and our open-source LLM guide for current alternatives that outperform both on modern leaderboards.